GRAPH NEURAL NETWORKS IN GRAPH LEARNING

Year 2025, Volume: 7 Issue: 2, 17 - 56, 28.02.2025

Hamza Talha Gümüş , Can Eyüpoğlu

https://doi.org/10.56809/icujtas.1442504

Abstract

Neural networks have a large family of sub-fields. The constraints and limitations that neural networks face within themselves have positively affected their development and led to the creation of new neural network models. The biggest example of this is the development of graph neural network (GNN) models in addition to convolutional neural network (CNN) models, which do not perform well in some three-dimensional data. GNN, a deep learning model, basically uses graph learning. GNNs are a kind of graph deep learning. However, it should be noted that GNNs are a member of the neural network family and a sub-model of graph learning. In this study, the basic concepts, common features, differences, advantages, disadvantages and application areas of graph learning and GNNs are discussed.

Keywords

Graph Neural Networks, Graph Learning, Artificial Neural Networks, Deep Learning

ÇİZGE ÖĞRENMEDE ÇİZGE SİNİR AĞLARI

Abstract

Sinir ağları, alt alanlarında geniş bir aileyi sahiptir. Sinir ağlarının kendi içerisinde karşılaştığı kısıtlar ve limitler gelişimini olumlu yönde etkilemiş ve yeni sinir ağı modellerinin oluşmasını sağlamıştır. Bunun en büyük örneği bazı üç boyutlu verilerde yüksek başarı sergilemeyen evrişimli sinir ağı (Convolutional Neural Network-CNN) modellerine ek olarak çizge sinir ağı (Graph Neural Network-GNN) modellerinin geliştirilmesi olmuştur. Bir derin öğrenme modeli olan GNN, temelde çizge öğrenmeyi kullanmaktadır. GNN’ler bir nevi çizge derin öğrenmedir. Ancak bilinmelidir ki GNN’ler sinir ağları ailesinin bir üyesi olduğu gibi çizge öğrenmenin de alt modellerinden birisidir. Bu çalışmada çizge öğrenme ve GNN’ler ile ilgili temel kavramlar, ortak özellikler, farklılıklar, avantajlar, dezavantajlar ve uygulama alanlarından bahsedilmektedir.

Keywords

Çizge Sinir Ağları, Çizge Öğrenme, Yapay Sinir Ağları, Derin Öğrenme

References

• Abu-El-Haija, S., Perozzi, B., Al-Rfou, R., Alemi, A. (2018). Watch your step: learning graph embeddings through attention. In Proceedings of Adv. Neural Inf. Process. Syst., 2018, pp. 9180–9190.
• Ahmed, A., Shervashidze, N., Narayanamurthy, S., Josifovski, V., and Smola, A.J. (2013). Distributed large-scale natural graph factorization. In Proceedings of 22nd Int. Conf. World Wide Web, May 2013, pp. 37–48.
• Allamanis, M., Brockschmidt, M., Khademi, M. (2017). Learning to represent programs with graphs. In Proceedings of the 7th International Conference on Learning Representations 2017, arXiv:1711.00740. Al-Rfou, R., Perozzi, B., Zelle, D. (2019). DDGK: Learning graph representations for deep divergence graph kernels. In Proceedings of World Wide Web Conf., 2019, pp. 37–48.
• Army. (2022). Ordu araştırması, | son teknoloji siber güvenlik yöntemlerini geliştiriyor Madde | Amerika Birleşik Devletleri Ordusu. (army.mil).
• Asif, N.A., Sarker, Y., Chakrabbortty, R.K., Ryan, M.J., Ahamed, H., Saha, D.K., Badal, F.R., Das, S.K., Ali, F., Moyeen, S.I., Islam, R., Tasneemi Z. (2021). Graph Neural Network: A Comprehensive Review on Non-Euclidean Space. IEEE Access, vol. 9, pp. 60588–60606.
• Atwood, J., Towsley, D. (2016). Diffusion-Convolutional Neural Networks. In Proceedings of the 29th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain, pp. 1993–2001.
• Baek, J., Lee, D.B., Hwang, S.J. (2020). Learning to extrapolate knowledge: Transductive few-shot out-of-graph link prediction. 2020, arXiv:2006.06648.
• Bai, L., Yao, L., Kanhere, S.S., Wang, X., Sheng, Q.Z. (2019). STG2Seq: Spatial-temporal graph to sequence model for multi-step passenger demand forecasting. 2019, arXiv:1905.10069.
• Bai, Y., Ding, H., Qiao, Y., Marinovic, A., Gu, K., Chen, T., Sun, Y., Wang, W. (2019). Unsupervised inductive graph-level representation learning via graph-graph proximity. 2019, arXiv:1904.01098.
• Baingana, B., Giannakis, G.B. (2016). Tracking switched dynamic network topologies from information cascades. IEEE Trans. Signal. Process., vol. 65, no. 4, 2016, pp. 985–997.
• Bastings, J., Titov, I., Aziz, W., Marcheggiani, D., Simaan, K. (2017). Graph convolutional encoders for syntax-aware neural machine translation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017, arXiv:1704.04675.
• Battaglia, P.W., Pascanu, R., Lai, M., Rezende, D., Kavukcuoglu, K. (2016). Interaction networks for learning about objects, relations and physics. In Proceedings of the 30th Conference on Neural Information Processing Systems, 2016.
• Beck, D., Haffari, G., Cohn, T. (2018). Graph-to-sequence learning using gated graph neural network. In Proceedings of ACL 2018, pp. 273–283. arXiv:1806.09835.
• Belkin, M., Niyogi, P. (2003). Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput., vol. 15, no. 6, 2003, pp. 1373–1396.
• Bello, I., Pham, H., Le, Q.V., Norouzi, M., Bengio, S., (2017). Neural Combinatorial Optimization with Reinforcement Learning. arXiv preprint, 2017, arXiv:1611.09940.
• Berg, R.V.D., Kipf, T.N., Welling, M. (2017). Graph convolutional matrix completion. 2017, arXiv:1706.02263.
• Bian, T., Xiao, X., Xu, T., Zhao, P., Huang, W., Rong, Y., Huang, A. (2017). Rumor detection on social media with bi-directional graph convolutional networks. In Proceedings of AAAI Conf. Artif. Intell., vol. 34, 2020, pp. 549–556.
• Bojchevski, A., Günnemann, S. (2019a). Adversarial attacks on node embeddings via graph poisoning. In Proceedings of Int. Conf. Mach. Learn., 2019, pp. 695–704.
• Bojchevski, A., Günnemann, S. (2019b). Certifiable robustness to graph perturbations. In Proceedings of Adv. Neural Inf. Process. Syst., 2019, pp. 8319–8330.
• Brockschmidt, M., Allamanis, M., Gaunt, A.L., Polozov, O. (2018). Generative code modeling with graphs. 2018, arXiv:1805.08490.
• Bruna, J., Zaremba, W., Szlam, A., LeCun, Y. (2014). Spectral networks and locally connected networks on graphs. In Proceedings of the 2nd Int. Conf. Learn. Repres., Banff, AB, Canada 2014.
• Cao, S., Lu, W., Xu, Q. (2015). Grarep: Learning graph representations with global structural information. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, 2015, ACM, pp. 891–900.
• Cao, N.D., Kipf, T. (2018). MolGAN: An implicit generative model for small molecular graphs. In Proceedings of ICML 2018 workshop on Theoretical Foundations and Applications of Deep Generative Models, 2018.
• Cao, S., Lu, W., Xu, Q. (2016). Deep neural networks for learning graph representations. In Proceedings of the 30th AAAI Conference on Artificial Intelligence, 2016, pp. 1145–1152.
• Chauhan, J., Nathani, D., Kaul, M. (2020). Few-shot learning on graphs via super-classes based on graph spectral measures. 2020, arXiv:2002.12815.
• Chen, D. (2009). A novel clustering algorithm for graphs. In Proceedings of 2009 International Conference on Artificial Intelligence and Computational Intelligence, vol. 4, pp.279–283.
• Chen, H., Perozzi, B., Hu, Y., Skiena, S. (2018a). HARP: Hierarchical representation learning for networks. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence, 2018, pp. 2127–2134.
• Chen, J., Zhang, Q., Huang, X. (2016). Incorporate group information to enhance network embedding. In Proceedings of the 25th ACM International Conference on Information and Knowledge Management, 2016, pp. 1901–1904.
• Chen, J., Zhu, J., Song, L., (2018b). Stochastic Training of Graph Convolutional Networks with Variance Reduction. In Proceedings of the 35 th International Conference on Machine Learning, Stockholm, Sweden, PMLR 80.
• Chen, L., Wu, L., Hong, R., Zhang, K., Wang, M., (2020a). Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach. In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), 2020, pp. 27–35.
• Chen, X., Li, L.-J., Fei-Fei, L., Gupta, A. (2018c). Iterative visual reasoning beyond convolutions. In Proceedings of CVPR, pp. 7239–7248.
• Chen, Z., Kolhe, G., Rafatirad, S., Lu, C.T., Manoj, S.P.D., Homayoun, H., Zhao, L. (2020b). Estimating the circuit de-obfuscation runtime based on graph deep learning. In Proceedings of Design, Automat. Test Eur. Conf. Exhib. (DATE), Mar. 2020, pp. 358–363.
• Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J. (2017). Gram: graph-based attention model for healthcare representation learning. In Proceedings of KDD. ACM, 2017, pp. 787–795.
• Choi, E., Xiao, C., Stewart, W., Sun, J. (2018). Mime: Multilevel medical embedding of electronic health records for predictive healthcare. In Proceedings of NeurIPS, 2018, pp. 4548–4558.
• Coley, C.W., Barzilay, R., Green, W.H., Jaakkola, T.S., Jensen, K.F. (2017). Convolutional embedding of attributed molecular graphs for physical property prediction. Journal of Chemical Information and Modeling, vol. 57, no. 8, pp. 1757–1772.
• Cvitkovic, M., Singh, B., Anandkumar, A. (2019). Open vocabulary learning on source code with a graph-structured cache. In Proceedings of Int. Conf. Mach. Learn., 2019, pp. 1475–1485.
• Dai, H., Dai, B., Song, L. (2016). Discriminative embeddings of latent variable models for structured data. In Proceedings of ICML, 2016, pp. 2702–2711.
• Dai, H., Kozareva, Z., Dai, B., Smola, A., Song, L. (2018a). Learning Steady-States of Iterative Algorithms Over Graphs. In the 35th International Conference on Machine Learning, PMLR 80, pp. 1106–1114.
• Dai, H., Li, C., Coley, C., Dai, B., Song, L. (2019a). Retrosynthesis prediction with conditional graph logic network. In Proceedings of Adv. Neural Inf. Process. Syst., 2019, pp. 8872–8882.
• Dai, H., Li, H., Tian, T., Huang, X., Wang, L., Zhu, J., Song, L. (2018b). Adversarial attack on graph structured data. In Proceedings of the 35th International Conference on Machine Learning, 2018, arXiv:1806.02371.
• Dai, Q., Li, Q., Tang, J., Wang, D. (2019b). Adversarial network embedding, In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence.
• Das, R., Dhuliawala, S., Zaheer, M., Vilnis, L., Durugkar, I., Krishnamurthy, A., Smola, A., McCallum, A. (2018). Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning. In Proceedings of the 7th International Conference on Learning Representations.
• De Bacco, C., Power, E.A., Larremore, D.B., Moore, C. (2018). Community detection, link prediction, and layer interdependence in multilayer networks. Phys. Rev. E., vol. 95, no. 4, 2017, 042317.
• Defferrard, M., Bresson, X., Vandergheynst, P. (2016). Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. In Proceedings of NeurIPS 2016, pp. 3844–3852.
• Deng, Z., Dong, Y., Zhu, J. (2019). Batch Virtual Adversarial Training for Graph Convolutional Networks. ArXiv abs/1902.09192 (2019).
• Derr, T., Ma, Y., Tang, J. (2018). Signed graph convolutional networks. In Proceedings of 2018 IEEE International Conference on Data Mining (ICDM), IEEE, 2018, pp. 929–934.
• Ding, C.H., He X., Zha H., Gu M., Simon H.D. (2001). A min-max cut algorithm for graph partitioning and data clustering. In Proceedings of the 2001 IEEE International Conference on Data Mining, IEEE, 2001, pp. 107–114.
• Ding, M., Tang, J., Zhang, J. (2018). Semi-supervised learning on graphs with generative adversarial nets, In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018.
• Ding, M., Zhou, C., Chen, Q., Yang, H., Tang, J. (2019). Cognitive graph for multi-hop reading comprehension at scale. In Proceedings of ACL, 2019, pp. 2694–2703.
• Do, K., Tran, T., Venkatesh, S. (2019). Graph transformation policy network for chemical reaction prediction. Proceedings of SIGKDD, pp. 750–760. J. Zhou et al. AI Open 1, pp. 57–81 77, In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019.
• Dong, X., Thanou, D., Frossard, P., Vandergheynst, P. (2016). Learning Laplacian matrix in smooth graph signal representations. IEEE Trans. Signal. Process., vol. 64, no. 23, 2016, pp. 6160–6173.
• Donnat, C., Zitnik, M., Hallac, D., Leskovec, J. (2017). Spectral graph wavelets for structural role similarity in networks. arXiv preprint arXiv:1710.10321, 2017.
• Dourisboure, Y., Geraci, F., Pellegrini, M. (2007). Extraction and classification of dense communities in the web. In Proceedings of the 16th International Conference on World Wide Web, ACM, pp. 461–470.
• Du, M., Li, F., Zheng, G., Srikumar, V. (2017). Deeplog: anomaly detection and diagnosis from system logs through deep learning. In Proceedings of Acm Sigsac Conference on Computer & Communications Security 2017.
• Dutil, F., Cohen, J.P., Weiss, M., Derevyanko, G., Bengio, Y. (2018). Towards gene expression convolutions using gene interaction graphs. In Proceedings of International Conference on Machine Learning Workshop on Computational Biology, 2018.
• Duvenaud, D.K., Maclaurin, D., Aguileraiparraguirre, J., Gomezbombarelli, R., Hirzel, T.D., Aspuruguzik, A., Adams, R.P. (2015). Convolutional networks on graphs for learning molecular fingerprints. In Proceedings of NIPS, Neural Inf. Process. Syst., 2015, pp. 2224–2232.
• Entezari, N., Al-Sayouri, A.S., Darvishzadeh, A., Papalexakis, E.E. (2020). All you need is low (rank) - defending against adversarial attacks on graphs. In WSDM ’20: the Thirteenth ACM International Conference on Web Search and Data Mining, Houston TX USA February, 2020, pp. 169–177.
• Fan, W., Ma, Y., Li, Q., He, Y., Zhao, E., Tang, J., Yin, D. (2019). Graph neural networks for social recommendation. In Proceedings of WWW, 2019, pp. 417–426.
• Feder, T., Motwani, R. (1995). Clique partitions, graph compression and speeding-up algorithms. J. Comput. Syst. Sci., vol. 51, no. 2, 1995, pp. 261–272.
• Feng, R., Yang, Y., Hu, W., Wu, F., Zhuang, Y. (2018a). Representation learning for scale-free networks. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence, 2018, pp. 282–289.
• Feng, Y., You, H., Zhang, Z., Ji, R., Gao, Y., (2018b). Hypergraph neural networks. In Proceedings of AAAI, vol. 33, 2018, pp. 3558–3565.
• Fey, M., Lenssen, J.E.., Morris, C., Masci, J., Kriege, N.M. (2020). Deep graph matching consensus. Arxiv 2020, arXiv:2001.09621.
• Fortunato, S. (2010). Community detection in graphs. Phys. Rep., vol. 486, no. 3-5, 2010, pp. 75–174.
• Fout, A., Byrd, J., Shariat, B., Ben-Hur, A. (2010). Protein interface prediction using graph convolutional networks. In Proceedings of NIPS, 2017, pp. 6533–6542.
• Gao, H., Wang Z., Ji S. (2018). Large-scale learnable graph convolutional networks. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, ACM, 2018, pp. 1416–1424.
• Garcia, V., Bruna, J. (2018). Few-shot learning with graph neural networks. In Proceedings of the 7th International Conference on Learning Representations, 2018.
• Gasse, M., Chetelat, D., Ferroni, N., Charlin, L., Lodi, A. (2019). Exact combinatorial optimization with graph convolutional neural networks. In Proceedings of NeurIPS 2019, pp. 15580–15592.
• Gidaris, S., Komodakis, N. (2019). Generating classification weights with GNN denoising autoencoders for few-shot learning. In Proceedings of IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2019, pp. 21–30.
• Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E. (2017). Neural Message Passing for Quantum Chemistry. In Proceedings of International Conference on Machine Learning, 2017. ArXiv, abs/1704.01212.
• Girvan, M., Newman, M.E. (2002). Community structure in social and biological networks. In Proceedings of Natl. Acad. Sci., vol. 99, no. 12, pp. 7821–7826.
• Gong, Y., Zhu, Y., Duan, L., Liu, Q., Guan, Z., Sun, F., Ou, W., Zhu, K.Q. (2019). Exact-K recommendation via maximal clique optimization. In Proceedings of the 25th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, Jul. 2019, pp. 617–626.
• Green, S.B., Yang Y. (2009). Reliability of summed item scores using structural equation modeling: an alternative to coefficient alpha. Psychometrika, vol. 74, no. 1, 2009, pp. 155–167.
• Grover, A., Leskovec, J. (2016). node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 855–864.
• Grover, A., Zweig, A., Ermon, S. (2019). Graphite: iterative generative modeling of graphs. In Proceedings of ICML, 2019, pp. 2434–2444.
• Gu, J., Hu, H., Wang, L., Wei, Y., Dai, J. (2018). Learning region features for object detection. In Proceedings of ECCV, 2018, pp. 381–395.
• Guo, M., Chou, E., Huang, D.A., Song, S., Yeung, S., Fei-Fei, A. (2018). Neural graph matching networks for fewshot 3D action recognition. In Proceedings of Eur. Conf. Comput. Vis. (ECCV), 2018, pp. 653–669.
• Guo, S., Lin, Y., Feng, N., Song, C., Wan, H. (2019). Attention based spatial temporal graph convolutional networks for traffic flow forecasting. In Proceedings of AAAI Conf. Artif. Intell., vol. 33, 2019, pp. 922–929.
• Gümüş, H.T., Eyüpoğlu, C. (2023). Grafik Sinir Ağlarına Genel Bir Bakış. EMO Bilimsel Dergi, vol. 13, no. 2, pp. 39–56.
• Gümüş, H.T., Eyüpoğlu, C. (2024). Grafik Sinir Ağları Üzerine Bir İnceleme. Savunma Bilimleri Dergisi.
• Hamaguchi, T., Oiwa, H., Shimbo, M., Matsumoto, Y. (2017). Knowledge transfer for out-of-knowledge-base entities: a graph neural network approach. In Proceedings of IJCAI, 2017, pp. 1802–1808.
• Hamilton, W.L., Ying, R., Leskovec, J. (2017a). Representation learning on graphs: methods and applications. 2017, arXiv preprint arXiv:1709.05584, 2017b.
• Hamilton, W.L., Ying, R., Leskovec, L. (2017b). Inductive representation learning on large graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS'17), Red Hook, NY, USA, pp. 1025–1035.
• Harandi, M.T., Sanderson, C., Shirazi, S., Lovell, B.C. (2011). Graph embedding discriminant analysis on Grassmannian manifolds for improved image set matching. In Proceedings of CVPR 2011, IEEE, 2011, pp. 2705–2712.
• Henaff, M., Bruna, J., Lecun, Y. (2015). Deep Convolutional Networks on Graph-Structured Data. 2015, arXiv preprint arXiv:1506.05163.
• Hoshen, Y. (2017). Vain: attentional multi-agent predictive modeling. In Proceedings of NIPS, pp. 2698–2708, Advances in Neural Information Processing Systems, 2017.
• Hu, C., Cheng, L., Sepulcre, J., Johnson, K.A., Fakhri, G.E., Lu, Y.M., Li, Q. (2015). A spectral graph regression model for learning brain connectivity of Alzheimer’s disease. PLoS ONE, vol. 10, no. 5, 2015, e0128136.
• Hu, J., Guo, C., Yang, B., Jensen, C.S. (2019). Stochastic weight completion for road networks using graph convolutional networks. In Proceedings of IEEE 35th Int. Conf. Data Eng. (ICDE), Apr. 2019, pp. 1274–1285.
• Hu, R., Long, G., Jiang, J., Yao, L., Zhang, C. (2018). Adversarially regularized graph autoencoder for graph embedding. In Proceedings of IJCAI, 2018, pp. 2609–2615.
• Huang, X., Li, J., Hu, X. (2017). Label informed attributed network embedding. In Proceedings of the 10th ACM International Conference on Web Search and Data Mining, 2017, pp. 731–739.
• Ioannidis, V.N., Berberidis, D., Giannakis, G.B. (2019). Graphsac: Detecting Anomalies in Large-Scale Graphs. arXiv preprint arXiv:1910.09589.
• Jain, A., Zamir, A.R., Savarese, S., Saxena, A. (2016). Structural-rnn: deep learning on spatio-temporal graphs. In Proceedings of CVPR, 2016, pp. 5308–5317.
• Jiang, J., Chen, J., Gu, T., Choo, K.K.R., Liu, C., Yu, M., Huang, W., Mohapatra, P. (2019). Anomaly detection with graph convolutional networks for insider threat and fraud detection. In Proceedings of MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM).
• Jin, W., Li, Y., Xu, H., Wang, Y., Tang, J. (2020). Adversarial Attacks and Defenses on Graphs: A Review and Empirical Study. ArXiv, abs/2003.00653.
• Jin, W., Yang, K., Barzilay, R., Jaakkola, T. (2018). Learning multimodal graph-to-graph translation for molecular optimization. 2018, arXiv:1812.01070.
• Johnson, D.D. (2016). Learning graphical state transitions. In Proceedings of Int. Conf. Learn. Represent. (ICLR), 2016.
• Jolliffe, I.T., Cadima, J. (2016). Principal component analysis: a review and recent developments. Philos. Trans. A Math. Phys. Eng Sci., vol. 374, no. 2065, 2016, 20150202.
• Jørgensen, P.B., Jacobsen, K.W., Schmidt, M.N. (2018). Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials. ArXiv, abs/1806.03146.
• Kampffmeyer, M., Chen, Y., Liang, X., Wang, H., Zhang, Y., Xing, E.P. (2019). Rethinking Knowledge Graph Propagation for Zero-Shot Learning. In Proceedings of CVPR, 2019, pp. 11487–11496.
• Karypis, G., Kumar, V. (1998). Multilevel k-way partitioning scheme for irregular graphs. J. Parallel. Distrib. Comput., vol. 48, no. 1, 1998, pp. 96–129.
• Kawahara, J., Brown, C.J., Miller, S.P., Booth, B.G., Chau, V., Grunau, R.E., Zwicker, J.G., Hamarneh, G. (2017). Brainnetcnn: convolutional neural networks for brain networks; towards predicting neurodevelopment. NeuroImage, vol. 146, pp. 1038–1049.
• Kearnes, S., McCloskey, K., Berndl, M., Pande, V., Riley, P. (2016). Molecular graph convolutions: moving beyond fingerprints. J. Comput. Aided Mol. Des., vol. 30, pp. 595–608.
• Kermarrec, A.M., Leroy V., Trédan G. (2011). Distributed social graph embedding. In Proceedings of the 20th ACM international conference on Information and Knowledge Management, ACM, 2011, pp. 1209–1214.
• Khalil, E., Dai, H., Zhang, Y., Dilkina, B., Song, L. (2017). Learning combinatorial optimization algorithms over graphs. In Proceedings of NIPS, 2017, pp. 6348–6358.
• Kim, D., Yoo, Y., Kim, J., Lee, S., Kwak, N. (2018). Dynamic graph generation network: Generating relational knowledge from diagrams. In Proceedings of IEEE/CVF Conf. Comput. Vis. Pattern Recognit., Jun. 2018, pp. 4167–4175.
• Kingma, D.P., Welling, M. (2013). Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114, 2013.
• Kipf, T.N., Fetaya, E., Wang, K., Welling, M., Zemel, R.S. (2013). Neural relational inference for interacting systems. In Proceedings of ICML. PMLR, 2018, pp. 2688–2697.
• Kipf, T.N., Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In Proceedings of 5th International Conference on Learning Representations, Toulon, France, April 24-26.
• Knyazev, B., Lin, X., Amer, M.R., Taylor, G.W. (2018). Spectral multigraph networks for discovering and fusing relationships in molecules. ArXiv abs/1811.09595.
• Kool, W., Hoof, H.V., Welling, M. (2018). Attention, learn to solve routing problems!. Arxiv, 2018, arXiv:1803.08475.
• Ktena, S.I., Parisot, S., Ferrante, E., Rajchl, M., Lee, M., Glocker, B., Rueckert, D. (2017). Distance metric learning using graph convolutional networks: Application to functional brain networks. In Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, 2017.
• Lake, B., Tenenbaum, J. (2010). Discovering structure by learning sparse graphs. In Proceedings of the 32nd Annual Meeting of the Cognitive Science Society CogSci 2010, Portland, Oregon, United States, 11-14 August, 2010, Cognitive Science Society, Inc., 2010. pp. 778–784.
• Lee, C., Fang, W., Yeh, C., Wang, Y.F. (2018a). Multi-label zero-shot learning with structured knowledge graphs. In Proceedings of CVPR, Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., Jun. 2018, pp. 1576–1585.
• Lee, J.B., Rossi, R., Kong, X. (2018b). Graph classification using structural attention. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1666–1674.
• Li, C., Goldwasser, D. (2019a). Encoding social information with graph convolutional networks for political perspective detection in news media. In Proceedings of 57th Annu. Meeting Assoc. Comput. Linguistics, 2019, pp. 2594–2604.
• Li, R., Wang, S., Zhu, F., Huang, J. (2018a). Adaptive graph convolutional neural networks. In Proceedings of Thirty-Second AAAI Conference on Artificial Intelligence, 2018. Li, C., Cao, Y., Hou, L., Shi, J., Li, J., Chua, T.S. (2019b). Semi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph Model. In Proceedings of IJCNLP, 2019.
• Li, C., Li, Z., Wang, S., Yang, Y., Zhang, X., Zhou, J. (2017a). Semisupervised network embedding. In Proceedings of International Conference on Database Systems for Advanced Applications, 2017, pp. 131–147.
• Li, C., Wang, S., Yang, D., Li, Z., Yang, Y., Zhang, X., Zhou, J. (2017b). PPNE: Property preserving network embedding. In Proceedings of International Conference on Database Systems for Advanced Applications, 2017, pp. 163–179.
• Li, R., Tapaswi, M., Liao, R., Jia, J., Urtasun, R., Fidler, S. (2017c). Situation recognition with graph neural networks. In Proceedings of IEEE Int. Conf. Comput. Vis. (ICCV), Oct. 2017, pp. 4173–4182.
• Li, W., Bao, R., Harimoto, K., Chen, D., Xu, J., Su, Q. (2020a). Modeling the stock relation with graph network for overnight stock movement prediction. In Proceedings of IJCAI, 2020, pp. 4541–4547.
• Li, Y., Jin, W., Xu, H., Tang, J. (2020b). Deeprobust: A Pytorch Library for Adversarial Attacks and Defenses. arXiv preprint arXiv:2005.06149.
• Li, Y., Tam, D.S., Xie, S., Liu, X., Ying, Q., Lau, W., Chiu, D., Chen, S.Z. (2020c). GTEA: Representation Learning for Temporal Interaction Graphs via Edge Aggregation. 2020, ArXiv, abs/2009.05266.
• Li, Y., Tarlow, D., Brockschmidt, M., Zemel, R. (2016). Gated graph sequence neural networks. In Proceedings of the 5th International Conference on Learning Representations, 2016.
• Li, Y., Vinyals, O., Dyer, C., Pascanu, R., Battaglia, P. (2018b). Learning Deep Generative Models of Graphs. arXiv preprint, 2018, arXiv:1803.03324.
• Li, Y., Yu, R., Shahabi, C., Liu, Y. (2018c). Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In Proceedings of the 7th International Conference on Learning Representations, 2018.
• Li, Z., Chen, Q., Koltun, V. (2018d). Combinatorial optimization with graph convolutional networks and guided tree search. In Proceedings of Adv. Neural Inf. Process. Syst., 2018, pp. 539–548.
• Liang, J., Gurukar, S., Parthasarathy, S. (2018). Mile: a multi-level framework for scalable graph embedding. 2018, arXiv preprint arXiv:1802.09612, 2018.
• Liang, X., Lin, L., Shen, X., Feng, J., Yan, S., Xing, E.P. (2017). Interpretable structureevolving lstm. In Proceedings of CVPR, 2017, pp. 2175– 2184.
• Liang, X., Shen, X., Feng, J., Lin, L., Yan, S. (2016). Semantic object parsing with graph lstm. In Proceedings of ECCV, 2016, pp. 125–143.
• Liao, R., Li, Y., Song, Y., Wang, S., Hamilton, W., Kduvenaud, D., Urtasun, R., Zemel, R. (2019). Effcient graph generation with graph recurrent attention networks, In Proceedings of Adv. Neural Inf. Process. Syst., 2019, pp. 4255–4265.
• Liu, J., Kumar, A., Ba, J., Kiros, J., Swersky, K. (2019a). Graph normalizing flows. In Proceedings of NeurIPS, 2019, pp. 13578–13588.
• Liu, R., Nejati H., Cheung N.M. (2018a). Joint estimation of low-rank components and connectivity graph in high-dimensional graph signals: application to brain imaging. arXiv preprint arXiv:1801.02303.
• Liu, X., Luo, Z., Huang, H. (2018b). Jointly multiple events extraction via attention-based graph information aggregation. In Proceedings of EMNLP, 2018, pp. 1247–1256.
• Liu, Z., Xiong, C., Sun, M., Liu, Z. (2019b). Fine-grained fact verification with kernel graph attention network. In Proceedings of ACL, 2019, pp. 7342–7351.
• Luo, D., Cheng, W., Xu, D., Yu, W., Zong, B., Chen, H., Zhang, X. (2020a). Parameterized explainer for graph neural network. Arxiv, 2020, arXiv:2011.04573.
• Luo, D., Cheng, W., Yu, W., Zong, B., Ni, J., Chen, H., Zhang, X. (2020b). Learning to drop: Robust graph neural network via topological denois ing. Arxiv, 2020, arXiv:2011.07057.
• Lyu, T., Zhang, Y., Zhang, Y. (2017). Enhancing the network embedding quality with structural similarity. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017, pp. 147–156.
• Ma, T., Chen, J., Xiao, C. (2018b). Constrained generation of semantically valid graphs via regularizing variational autoencoders. In Proceedings of NeurIPS, 2018, pp. 7113–7124.
• Ma, Y., Guo, Z., Ren, Z., Zhao, E., Tang, J., Yin, D. (2018a). Streaming Graph Neural Networks. Arxiv, arXiv:1810.10627, October 2018.
• Manessi, F., Rozza, A., Manzo, M. (2020). Dynamic graph convolutional networks. Pattern Recogn. Pattern Recognition, vol. 97, 107000.
• Marcheggiani, D., Bastings, J., Titov, I. (2018). Exploiting semantics in neural machine translation with graph convolutional networks. In Proceedings of NAACL, 2018, pp. 486–492.
• Marcheggiani, D., I. Titov, I. (2017). Encoding sentences with graph convolutional networks for semantic role labeling. In Proceedings of EMNLP, 2017, pp. 1506–1515.
• Marino, K., Salakhutdinov, R., Gupta, A. (2017). The more you know: Using knowledge graphs for image classification. In Proceedings of CVPR, pp. 20–28.
• Matsunaga, D., Suzumura, T., Takahashi, T. (2019). Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis. arXiv preprint, 2019, arXiv: 1909.10660.
• Miwa, M., Bansal, M., (2016). End-to-end relation extraction using lstms on sequences and tree structures. In Proceedings of ACL, 2016, pp. 1105–1116.
• Monti, F., Bronstein, B., Bresson, X. (2017). Geometric matrix completion with recurrent multi-graph neural networks, In Proceedings of Advances in Neural Information Processing Systems, 2017.
• Motsinger, A.A., Lee S.L., Mellick G., Ritchie M.D. (2006). GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease. BMC Bioinf., vol. 7, no. 1, 2006, pp. 39–11.
• Narasimhan, M., Lazebnik, S., Schwing, A. (2018). Out of the box: Reasoning with graph convolution nets for factual visual question answering. In Proceedings of Adv. Neural Inf. Process. Syst., pp. 2654–2665.
• Nathani, D., Chauhan, J., Sharma, C., Kaul, M. (2019). Learning attentionbased embeddings for relation prediction in knowledge graphs, arXiv:1906.01195, 2019.
• Newman, M.E. (2004). Detecting community structure in networks. Eur. Phys. J. B, vol. 38, no. 2, 2004, pp. 321–330.
• Nguyen, T.H., Grishman, R. (2018). Graph convolutional networks with argument-aware pooling for event detection. In Proceedings of 32nd AAAI Conf. Artif. Intell., pp. 5900–5907.
• Niepert, M., Ahmed, M., Kutzkov, K. (2016). Learning convolutional neural networks for graphs. In Proceedings of Int. Conf. Mach. Learn., pp. 2014–2023.
• Norcliffe-Brown, W., Vafeias, S., Parisot, S. (2018). Learning conditioned graph structures for interpretable visual question answering. In Proceedings of Adv. Neural Inf. Process. Syst., pp. 8334–8343.
• Nowak, A., Villar, S., Bandeira, A.S., Bruna, J. (2017). A note on learning algorithms for quadratic assignment with graph neural networks. In Proceedings of 34th Int. Conf. Mach. Learn. (ICML), vol. 1050, 2017, p. 22. Nowak, A., Villar, S., Bandeira, A.S., Bruna, J. (2018). Revised note on learning quadratic assignment with graph neural networks. In Proceedings of IEEE DSW, pp. 1–5.
• Nt, H., Jin, C.J., Murata, T. (2019). Learning Graph Neural Networks with Noisy Labels. In Proceedings of ICLR Workshop LLD.
• Nt, H., Maehara, T. (2019). Revisiting Graph Neural Networks: All We Have Is Low-Pass Filters. arXiv preprint, 2019, arXiv:1905.09550.
• Ou, M., Cui, P., Pei, J., Zhang, Z., Zhu, W. (2016). Asymmetric transitivity preserving graph embedding. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1105–1114.
• Palm, R., Paquet, U., Winther, O. (2018). Recurrent relational networks. In Proceedings of Adv. Neural Inf. Process. Syst., pp. 3368–3378.
• Pan, P., Wu, J., Zhu, X., Zhang, C., Wang, Y. (2016). Tri-party deep network representation. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI'16). AAAI Press, pp. 1895–1901.
• Pan, S., Hu, R., Long, G., Jiang, J., Yao, L., Zhang, C. (2018). Adversarially regularized graph autoencoder for graph embedding. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, pp. 2609–2615.
• Parisot, S., Ktena, S.I., Ferrante, E., Lee, M., Moreno, R.G., Glocker, B., Rueckert, D. (2017). Spectral Graph Convolutions for Population-based Disease Prediction. In Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention.
• Park, N., Kan, A., Dong, X.L., Zhao, T., Faloutsos, C. (2019). Estimating node importance in knowledge graphs using graph neural networks. In Proceedings of the 25th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2019, pp. 596–606.
• Pei, X., Yu, L., Tian, S. (2020). AMalNet: A deep learning framework based on graph convolutional networks for malware detection. Comput. Secur., vol. 93, Jun. 2020, p. 101792.
• Peng, H., Li, J., Gong, Q., Ning, Y., Wang, S., He, L. (2020). Motif-matching based subgraph-level attentional convolutional network for graph classification. In Proceedings of AAAI, 2020, pp. 5387–5394.
• Peng, H., Li, J., Gong, Q., Song, Y., Ning, Y., Lai, K., Yu, P.S. (2019). Fine-grained event categorization with heterogeneous graph convolutional networks. Arxiv, 2019, arXiv:1906.04580.
• Peng, H., Li, J., He, Y., Liu, Y., Bao, M., Wang, L., Song, Y., Yang, Q., (2018). Large-scale hierarchical text classification with recursively regularized deep graph-cnn. In Proceedings of WWW, 2018, pp. 1063–1072.
• Peng, N., Poon, H., Quirk, C., Toutanova, K., Yih, W.T. (2017). Crosssentence N-ary relation extraction with graph LSTMs. Trans. Assoc. Comput. Linguistics, vol. 5, pp. 101–115.
• Perozzi, B., Al-Rfou, R., Skiena, S. (2014). DeepWalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710.
• Prates, M., Avelar, P.H., Lemos, H., Lamb, L.C., Vardi, M.Y. (2019). Learning to solve np-complete problems: A graph neural network for decision TSP. In Proceedings of AAAI Conf. Artif. Intell., vol. 33, 2019, pp. 4731–4738.
• Qi, S., Wang, W., Jia, B., Shen, J., Zhu, S.C. (2018). Learning humanobject interactions by graph parsing neural networks. In Proceedings of the European Conference on Computer Vision (ECCV), pp. 401–417.
• Qi, X., Liao, R., Jia, J., Fidler, S., Urtasun, R. (2017). 3d graph neural networks for rgbd semantic segmentation. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 5199–5208.
• Qiu, J., Tang, J., Ma, H., Dong, Y., Wang, K., Tang, J. (2018). Deepinf: Modeling influence locality in large social networks. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp. 2110–2119.
• Qiu, L., Xiao, Y., Qu, Y., Zhou, H., Li, L., Zhang, W., Yu, Y. (2019). Dynamically fused graph network for multi-hop reasoning. In Proceedings of ACL, pp. 6140–6150.
• Rahimi, A., Cohn, T., Baldwin, T. (2018). Semi-supervised user geolocation via graph convolutional networks, vol. 1, pp. 2009–2019, arXiv:1804.08049.
• Raposo, D., Santoro, A., Barrett, D., Pascanu, R., Lillicrap, T., Battaglia, P. (2017). Discovering objects and their relations from entangled scene representations. Arxiv, 2017, arXiv:1702.05068.
• Rhee, S., Seo, S., Kim, S. (2018). Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification. In Proceedings of IJCAI, 2018, pp. 3527–3534.
• Riba, P., Fischer, A., Llados, J., Fornes, A. (2018). Learning Graph Distances with Message Passing Neural Networks. In Proceedings of the 24th International Conference on Pattern Recognition (ICPR), pp. 2239–2244.
• Ribeiro, L.F., Saverese, P.H., Figueiredo, D.R. (2017). struc2vec: Learning node representations from structural identity. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 385–394.
• Robinson, S.L., Bennett, R.J. (1995). A typology of deviant workplace behaviors: A multidimensional scaling study. Acad. Manage. J., vol. 38, no. 2, 1995, pp. 555–572.
• Roweis, S.T., Saul, L.K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, vol. 290, no. 5500, pp. 2323–2326.
• Rusek, K., Suárez-Varela, J., Mestres, A., Barlet-Ros, P., Cabellos-Aparicio, A. (2019). Unveiling the potential of graph neural networks for network modeling and optimization in SDN. In Proceedings of ACM Symp. SDN Res., pp. 140–151.
• Said, A., De Luca, E.W., Albayrak, S. (2010). How social relationships affect user similarities. In Proceedings of the 2010 Workshop on Social Recommender Systems, pp. 1–4.
• SAM. (2022). https://sam.gov/opp/178e0311b4d04df2bf25025d5c99473d/view.
• Samko, O., Marshall, A.D., Rosin, P.L. (2006). Selection of the optimal parameter value for the isomap algorithm. Pattern Recognit. Lett., vol. 27, no. 9, pp. 968–979.
• Sanchez, A., Heess, N., Springenberg, J.T., Merel, J., Hadsell, R., Riedmiller, M.A., Battaglia, P. (2018). Graph networks as learnable physics engines for inference and control. In Proceedings of ICML, pp. 4470–4479.
• Santoro, A., Raposo, D., Barrett, D.G., Malinowski, M., Pascanu, R., Battaglia, P., Lillicrap, T. (2017). A simple neural network module for relational reasoning. In Proceedings of Adv. Neural Inf. Process. Syst., pp. 4967–4976.
• Sato, R., Yamada, M., Kashima, H. (2019). Approximation Ratios of Graph Neural Networks for Combinatorial Problems. In Proceedings of NeurIPS, pp. 4081–4090.
• Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G. (2009). The Graph Neural Network Model. IEEE Transactions on Neural Networks, vol. 20, no. 1, pp. 61–80.
• Schlichtkrull, M., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., Welling, M. (2018). Modeling relational data with graph convolutional networks. In Proceedings of ESWC. Springer, pp. 593–607.
• Selsam, D., Lamm, M., Bünz, B., Liang, P., de Moura, L., Dill, D.L. (2019). Learning a SAT solver from single-bit supervision. In Proceedings of ICLR 2019.
• Shang, C., Tang, Y., Huang, J., Bi, J., He, X., Zhou, B. (2019). End-to-end structure-aware convolutional networks for knowledge base completion. In Proceedings of AAAI 33, pp. 3060–3067.
• Shchur, O., Zugner, D., Bojchevski, A., Gunnemann, S. (2018). Netgan: generating graphs via random walks. In Proceedings of ICML, pp. 609–618.
• Shi, C., Xu, M., Zhu, Z., Zhang, W., Zhang, M., Tang, J. (2020). Graphaf: a Flow Based Autoregressive Model for Molecular Graph Generation. In Proceedings of ICLR 2020.
• Singh, A.P., Gordon, G.J. (2008). Relational learning via collective matrix factorization. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp. 650–658.
• Song, L., Wang, Z., Yu, M., Zhang, Y., Florian, R., Gildea, D. (2018a). Exploring GraphStructured Passage Representation for Multi-Hop Reading Comprehension with Graph Neural Networks. ArXiv, 2018, arXiv preprint arXiv:1809.02040.
• Song, L., Zhang, Y., Wang, Z., Gildea, D. (2018b). A graph-to-sequence model for AMR-to-text generation. ArXiv. 2018, arXiv:1805.02473.
• Song, L., Zhang, Y., Wang, Z., Gildea, D. (2018c). A graph-to-sequence model for amr-totext generation. In Proceedings of ACL 2018, pp. 1616–1626.
• Song, L., Zhang, Y., Wang, Z., Gildea, D. (2018d). N-ary relation extraction using graph state lstm. In: Proceedings of EMNLP, 2018, pp. 2226–2235. arXiv:1808.09101.
• Sorokin, D., Gurevych, I. (2018). Modeling semantics with gated graph neural networks for knowledge base question answering. ArXiv. 2018, arXiv:1808.04126.
• Sukhbaatar, S., Fergus, R. et al. (2016). Learning multiagent communication with backpropagation. In Proceedings of Advances in Neural Information Processing Systems, 2016, pp. 2244–2252.
• Sun, F.Y., Hoffmann, J., Verma, V., Tang, J. (2019). InfoGraph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. ArXiv. 2019, arXiv:1908.01000.
• Sutton, R.S., Barto, A.G. (2018). Reinforcement Learning: an Introduction. MIT press, 2018.
• Tai, K.S., Socher, R., Manning, C.D. (2015). Improved semantic representations from treestructured long short-term memory networks. In Proceedings of IJCNLP, 2015, pp. 1556–1566.
• Tang, J., Qu M., Wang M., Zhang M., Yan J., Mei Q. (2015). LINE: Large-scale information network embedding. In Proceedings of the 24th International Conference on World Wide Web, pp. 1067–1077.
• Tang, L., Liu, H. (2009a). Relational learning via latent social dimensions. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009, pp. 817–826.
• Tang, L., Liu, H. (2009b). Scalable learning of collective behavior based on sparse social dimensions. In Proceedings of the 18th ACM International Conference on Information and Knowledge Management, 2009, pp. 1107–1116.
• Tang, L., Liu, H. (2011). Leveraging social media networks for classification. Data Mining and Knowledge Discovery, vol. 23, no. 3, pp. 447–478.
• Tang, X., Li, Y., Sun, Y., Yao, H., Mitra, P., Wang, S. (2020). Transferring robustness for graph neural network against poisoning attacks. In Proceedings of the 13th International Conference on Web Search and Data Mining. pp. 600–608.
• Tehranipoor, F., Karimian, N., Kermani, M.M., Mahmoodi, H. (2019). Deep RNN-oriented paradigm shift through bocanet: Broken obfuscated circuit attack. In Proceedings of Great Lakes Symp. VLSI, 2019, pp. 335–338.
• Teney, D., Liu, L., Den Hengel, A.V. (2017). Graph-structured representations for visual question answering. In Proceedings of CVPR, pp. 3233–3241, Jul. 2017, pp. 1–9.
• Thanou, D., Dong, X., Kressner, D., Frossard, P. (2017). Learning heat diffusion graphs. IEEE Trans. Signal Inform. Proc. Over Netw., vol. 3, no. 3, 2017, pp. 484–499.
• Tsitsulin, A., Palowitch, J., Perozzi, B., Müller, E. (2020). Graph Clustering with Graph Neural Networks. ArXiv. arXiv preprint, 2020, arXiv:2006.16904.
• Tu, C., Zhang, W., Liu, Z., Sun, M. (2016). Max-Margin DeepWalk: discriminative learning of network representation. In Proceedings of the 25th International Joint Conference on Artificial Intelligence, pp. 3889–3895.
• Tu, K., Cui P., Wang X., Wang F., Zhu W. (2017). Structural deep embedding for hyper-networks. 2017, CoRR. abs/1711.10146, 2017.
• Tu, K., Cui, P., Wang, X., Yu, P.S., Zhu, W. (2018). Deep recursive network embedding with regular equivalence. In Proceedings of 24th ACM Int. Conf. Knowl. Discov. Data Mining, pp. 2357–2366.
• Tu, M., Wang, G., Huang, J., Tang, Y., He, X., Zhou, B. (2019). Multi-hop reading comprehension across multiple documents by reasoning over heterogeneous graphs. In Proceedings of ACL, 2019, pp. 2704–2713.
• Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y. (2018). Graph attention networks. In Proceedings of Int. Conf. Learn. Representations. 2018.
• Vinyals, O., Fortunato, M., Jaitly, N. (2015). Pointer networks. In Proceedings of NIPS, 2015, pp. 2692–2700.
• Wang D., Cui P., Zhu W. (2016). Structural deep network embedding. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016, 1225–1234.
• Wang, H., Wang, J., Wang, J., Zhao, M., Zhang, W., Zhang, F., Xie, X., Guo, M. (2018a). Graphgan: graph representation learning with generative adversarial nets. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence.
• Wang, H., Lian, D., Ge, Y. (2019a). Binarized collaborative filtering with distilling graph convolutional networks. ArXiv. 2019, arXiv:1906.01829.
• Wang, H., Zhang, F., Zhang, M., Leskovec, J., Zhao, M., Li, W., Wang, Z. (2019b). Knowledge-aware graph neural networks with label smoothness regularization for recommender systems. In Proceedings of the 25th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining. Jul. 2019, pp. 968–977.
• Wang, L., Zong, B., Ma, Q., Cheng, W., Ni, J., Yu, W., Liu, Y., Song, D., Chen, H., Fu, Y. (2020a). Inductive and unsupervised representation learning on graph structured objects. In Proceedings of ICLR 2020, pp. 1–20.
• Wang, P., Han, J., Li, C., Pan, R. (2018b). Logic attention based neighborhood aggregation for inductive knowledge graph embedding. In Proceedings of AAAI Conf. Artif. Intell., vol. 33, pp. 7152–7159.
• Wang, S., Li, Y., Zhang, J., Meng, Q., Meng, L., Gao, F. (2020b). PM2.5- GNN: A domain knowledge enhanced graph neural network for PM2.5 forecasting. ArXiv. 2020, arXiv:2002.12898.
• Wang, S., Tang, J. Morstatter, F., Liu, H. (2016). Paired restricted Boltzmann machine for linked data. In Proceedings of the 25th ACM International Conference on Information and Knowledge Management, pp. 1753–1762.
• Wang, X., Cui, P., Wang, J., Pei, J., Zhu, W., Yang, S. (2017). Community preserving network embedding. In Proceedings of the 31st AAAI Conference on Artificial Intelligence, pp. 203–209.
• Wang, X., He, X., Cao, Y., Liu, M., Chua, T.S. (2019cv). KGAT: Knowledge graph attention network for recommendation. In Proceedings of the 25th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, Jul. 2019, pp. 950–958.
• Wang, X., Jin, B., Du, Y., Cui, P., Yang, Y. (2020c). One-class Graph Neural Networks for Anomaly Detection in Attributed Networks. Fast Is Better than Free: Revisiting Adversarial Training. arXiv preprint arXiv:2001.03994.
• Wang, X., Ye, Y., Gupta, A. (2018c). Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs. In Proceedings of CVPR 2018.
• Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M. (2018d). Dynamic Graph Cnn for Learning on Point Clouds, ACM Transactions on Graphics, vol. 38.
• Wang, Y., Yin, H., Chen, H., Wo, T., Xu, J., Zheng, K. (2019d). Origin destination matrix prediction via graph convolution: A new perspective of passenger demand modeling. In Proceedings of the 25th ACM SIGKDD Int. Conf.Knowl. Discovery Data Mining, Jul. 2019, pp. 1227–1235.
• Wang, Y.C., Wang, B., Kuo, J. (2019e). Graph representation learning: a survey, SIP, vol. 9, e15.
• Wang, Z., Chen, T., Ren, J., Yu, W., Cheng, H., Lin, L. (2018e). Deep reasoning with knowledge graph for social relationship understanding. Proceedings of IJCAI, 2018, pp. 1021–1028. arXiv:1807.00504. Available: http://arxiv.org/abs/1807.00504.
• Wang, Z., Luo, N., Zhou, P. (2020d). GuardHealth: Blockchain empowered secure data management and Graph Convolutional Network enabled anomaly detection in smart healthcare. Journal of Parallel and Distributed Computing, vol. 142, August 2020, pp. 1–12.
• Wang, Z., Lv, Q., Lan, X., Zhang, Y. (2018f). Cross-lingual knowledge graph alignment via graph convolutional networks. In Proceedings of EMNL, pp. 349–357.
• Watters, N., Zoran, D., Weber, T., Battaglia, P., Pascanu, R., Tacchetti, A. (2017). Visual interaction networks: learning a physics simulator from video. In Proceedings of NIPS, 2017, pp. 4539–4547.
• Wei, S., Chen, X., Li, Q., Zhuang, F., Liu, J., Kou, G. (2021). Graph Learning and Its Applications: A Holistic Survey, ArXiv.
• Wu, H., Wang, C., Tyshetskiy, Y., Docherty, A., Lu, K., Zhu, L. (2019a). Adversarial Examples on Graph Data: Deep Insights into Attack and Defense. ArXiv, arXiv:1903.01610.
• Wu, Q., Zhang, H., Gao, X., He, P., Weng, P., Gao, H., Chen, G. (2019b). Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems. In Proceedings of WWW, 2019, pp. 2091–2102.
• Wu, Y., Lian, D., Xu, Y., Wu, L., Chen, E. (2020). Graph convolutional networks with Markov random field reasoning for social spammer detection. In Proceedings of AAAI Conf. Artif. Intell., vol. 34, 2020, pp. 1054–1061.
• Wu, Z., S. Pan, S., Chen, F., Long, G,. Zhang, C., Yu, P.S. (2021). A Comprehensive Survey on Graph Neural Networks. IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 1, pp. 4–24.
• Xie, T., Grossman, J.C. (2018). Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Physical Review Letters, 2018.
• Xiong, W., Hoang, T., Wang, W.Y. (2017). Deeppath: A reinforcement learning method for knowledge graph reasoning. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing.
• Xu, Q., Wang, Q., Xu, C., Qu, L. (2017). Attentive graph-based recursive neural network for collective vertex classification. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 2017, pp. 2403–2406.
• Xu, C., Zhao, P., Liu, Y., Sheng, V.S., Xu, J., Zhuang, F., Fang, J., Zhou, X. (2019a). Graph contextualized self-attention network for session-based recommendation. In Proceedings of IJCAI, Aug. 2019, pp. 3940–3946.
• Xu, D., Cheng, E., Luo, D., Liu, X., Zhang, X. (2019b). Spatio-temporal attentive RNN for node classification in temporal attributed graphs. In Proceedings of IJCAI. 2019, pp. 3947–3953.
• Xu, D., Cheng, W., Luo, D., Gu, Y., Liu, X., Ni, J., Zong, B., Chen, H., Zhang, X. (2019c). Adaptive neural network for node classification in dynamic networks. In Proceedings of IEEE Int. Conf. Data Mining (ICDM), Nov. 2019, pp. 1402–1407.
• Xu, J., Chen, J., You, S., Xiao, Z., Yang, Y., Lu, J. (2021). Robustness of deep learning models on graphs: A survey. Al Open 2, pp. 69-78.
• Xu, K., Chen, H., Liu, S., Chen, P.Y., Weng, T.W., Hong, M., Lin, X. (2019d). Topology attack and defense for graph neural networks: An optimization perspective. ArXiv, 2019, arXiv:1906.04214.
• Xu, K., Wang, L., Yu, M., Feng, Y., Song, Y., Wang, Z., Yu, D. (2019e). Cross-lingual knowledge graph alignment via graph matching neural network. In Proceedings of ACL (Association for Computational Linguistics), pp. 3156–3161.
• Xu, N., Wang, P., Chen, L., Tao, J., Zhao, J. (2019f). MR-GNN: Multiresolution and dual graph neural network for predicting structured entity interactions. ArXiv. arXiv:1905.09558.
• Xu, X., Feng, W., Jiang, Y., Xie, X., Sun, Z., Deng, Z.H. (2019g). Dynamically pruned message passing networks for large-scale knowledge graph reasoning. ArXiv. 2019, arXiv:1909.11334.
• Xu, X., Yu, Y., Li, B., Song, L., Liu, C., Gunter, C. (2018). Characterizing Malicious Edges Targeting on Graph Neural Networks.
• Xu, X., Yu, Y., Song, L., Liu, C., Kailkhura, B., Gunter, C., Li, B. (2020). Edog: Adversarial Edge Detection for Graph Neural Networks. Tech. Rep. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States).
• Yan, S., Xiong, Y., and Lin, D. (2018). Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the 32nd AAAI Conf. Artif. Intell., pp. 3634–3640.
• Yang, C., Liu, Z., Zhao, D., Sun, M., Chang, E. (2015a). Network representation learning with rich text information. In Proceedings of Twenty-Fourth International Joint Conference on Artificial Intelligence 2015.
• Yang, T., Jin, R., Chi, Y., Zhu, S. (2009). Combining link and content for community detection: a discriminative approach. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2009, pp. 927–936.
• Yang, Z., Tang, J., Cohen, W. (2015b). Multi-modal Bayesian embeddings for learning social knowledge graphs. ArXiv. arXiv preprint arXiv:1508.00715, 2015b.
• Yang, Y., Wei, Z., Chen, Q., Wu, L. (2019). Using external knowledge for financial event prediction based on graph neural networks. In Proceedings of CIKM, 2019, pp. 2161–2164.
• Yang, Z., Cohen, W.W., Salakhutdinov, R. (2016). Revisiting semisupervised learning with graph embeddings. In Proceedings of the 33rd International Conference on International Conference on Machine Learning (ICML), pp. 40–48.
• Yao, H., Wu, F., Ke, J., Tang, X., Jia, Y., Lu, S., Gong, P., Ye, J., Li, Z. (2018). Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of AAAI, 2018. pp. 2588–2595.
• Yao, H., Zhang, C., Wei, Y., Jiang, M., Wang, S., Huang, J., Chawla, N., Li, Z. (2020). Graph few-shot learning via knowledge transfer. In Proceedings of AAAI Conf. Artif. Intell., vol. 34, 2020, pp. 6656–6663.
• Ye, F., Zhao, J., Sarkar, V. (2020). Advanced Graph-Based Deep Learning for Probabilistic Type Inference. ArXiv, 2020, abs/2009.05949.
• Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W.L., Leskovec, J. (2018a). Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, Jul. 2018, pp. 974–983.
• Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W., Leskovec, J. (2018b). Hierarchical graph representation learning with differentiable pooling. In Proceedings of the 32nd NeurIPS 2018.
• You, J., Liu, B., Ying, Z., Pande, V., Leskovec, J. (2018b). Graph convolutional policy network for goal-directed molecular graph generation. In Proceedings of NeurIPS 2018, pp. 6410–6421.
• You, J., Ying, R., Ren, X., Hamilton, W., Leskovec, J. (2018a). Graphrnn: Generating realistic graphs with deep auto-regressive models. In Proceedings of ICML, pp. 5694–5703.
• Yu, B., Yin, H., Zhu, Z. (2018a). Spatiotemporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. In Proceedings of IJCAI, pp. 3634–3640.
• Yu, W., Cheng, W., Aggarwal, C., Zong, B., Chen, H., Wang, W. (2019). Selfattentive attributed network embedding through adversarial learning. In Proceedings of IEEE Int. Conf. Data Mining (ICDM), Nov. 2019, pp. 758–767.
• Yu, W., Zheng, C., Cheng, W., Aggarwal, C.C., Song, D., Zong, B., Chen, H., Wang, W. (2018b). Learning deep network representations with adversarially regularized autoencoders. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018.
• Yuruk, N., Mete, M., Xu, X., Schweiger, T.A. (2009). Ahscan: Agglomerative hierarchical structural clustering algorithm for networks. In Proceedings of 2009 International Conference on Advances in Social Network Analysis and Mining, IEEE, pp. 72–77.
• Zayats, V., Ostendorf, M. (2018). Conversation modeling on reddit using a graph-structured LSTM. Trans. Assoc. Comput. Linguistics, TACL. vol. 6, pp. 121–132.
• Zhang, C., Zhang, K., Yuan, Q., Peng, H., Zheng, Y., Hanratty, T., Wang, S., Han, J. (2017a). Regions, periods, activities: Uncovering urban dynamics via cross-modal representation learning. In Proceedings of the 26th International Conference on World Wide Web. 2017, pp. 361–370.
• Zhang, D., Yin, J., Zhu, X., Zhang, C. (2016a). Homophily, structure, and content augmented network representation learning. In Proceedings of IEEE 16th International Conference on Data Mining (ICDM), pp. 609–618.
• Zhang, D., Yin, J., Zhu, X., Zhang, C. (2018a) Network representation learning: a survey. IEEE Transactions on Big Data, 2018.
• Zhang, F., Yuan, N.J., Lian, D., Xie, X., Ma, W.Y. (2016b). Collaborative knowledge base embedding for recommender systems. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, pp. 353–362.
• Zhang M., Chen, Y. (2019a). Inductive matrix completion based on graph neural networks. ArXiv. 2019, arXiv:1904.12058.
• Zhang Y., Wu B. (2015b). Finding community structure via rough k-means in social network. In Proceedings of 2015 IEEE International Conference on Big Data (Big Data), IEEE, pp. 2356–2361.
• Zhang, D., Yin, J., Zhu, X., Zhang, C. (2016c). Collective classification via discriminative matrix factorization on sparsely labeled networks. In Proceedings of the 25th ACM International Conference on Information and Knowledge Management, 2016. pp. 1563–1572.
• Zhang, D., Yin, J., Zhu, X., Zhang, C. (2017b). User profile preserving social network embedding. In Proceedings of the 26th International Joint Conference on Artificial Intelligence, 2017. pp. 3378–3384.
• Zhang, F., Liu, X., Tang, J., Dong, Y., Yao, P., Zhang, J., Gu, X., Wang, Y., Shao, B., Li, R., et al. (2019b). Oag: toward linking large-scale heterogeneous entity graphs. In Proceedings of KDD 2019, pp. 2585–2595.
• Zhang, J., Shi, X., Xie, J., Ma, H., King, I., Yeung, D. (2018b). GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs. In Proceedings of 34th Conference on Uncertainty in Artificial Intelligence 2018, pp. 339–349.
• Zhang, J., Shi, X., Zhao, S., King, I. (2019c). STAR-GCN: Stacked and reconstructed graph convolutional networks for recommender systems. 2019, ArXiv. arXiv:1905.13129.
• Zhang, W., Liu, H., Liu, Y., Zhou, J., Xiong, H. (2020a). Semi-supervised hierarchical recurrent graph neural network for city-wide parking avail ability prediction. In Proceedings of AAAI Conf. Artif. Intell., vol. 34, 2020, pp. 1186–1193.
• Zhang, X. Chen, W., Yan, H. (2016d). TLINE: scalable transductive network embedding. In Proceedings of the 12th Asia Information Retrieval Societies Conference, pp. 98–110.
• Zhang, X., Liu, H., Li, Q., Wu, X. (2019d). Attributed graph clustering via adaptive graph convolution. In Proceedings of IJCAI, pp. 4327–4333.
• Zhang, Y., Chen, X., Yang, Y., Ramamurthy, A., Li, B., Qi, Y., Song, L. (2020b). Efficient probabilistic logic reasoning with graph neural networks. ArXiv. 2020, arXiv:2001.11850.
• Zhang, Y., Khan, S., Coates, M. (2019e). Comparing and detecting adversarial attacks for graph deep learning. In Proceedings of Int. Conf. Representation Learning on Graphs and Manifolds Workshop, New Orleans, LA, USA.
• Zhang, Y., Liu, Q., Song, L. (2018c). Sentence-state LSTM for text representation. In Proceedings of ACL. 1, pp. 317–327 arXiv:1805.02474.
• Zhang, Y., Qi, P., Manning, C.D. (2018d). Graph convolution over pruned dependency trees improves relation extraction. In Proceedings of EMNLP, pp. 2205–2215. arXiv:1809.10185.
• Zhang, Z., Cui, P., Zhu, W. (2015a). Deep Learning on Graphs: A Survey. Journal of Latex Class Files, vol. 14, no. 8.
• Zheleva, E., Getoor, L., Golbeck, J., Kuter, U. (2008). Using friendship ties and family circles for link prediction. In International Workshop on Social Network Mining and Analysis. Springer, 2008, pp. 97–113.
• Zheng, C., Fan, X., Wang, C., Qi, J. (2020a). A graph multi-attention network for traffic prediction. In Proceedings of AAAI 34, 2020, pp. 1234–1241.
• Zheng, C., Zong, B., Cheng, W., Song, D., Ni, J., Yu, W., Chen, H., Wang, W. (2020b). Robust graph representation learning via neural sparsification. In Proceedings of Int. Conf. Mach. Learn., pp. 11458–11468.
• Zheng, X., Dan, C., Aragam, B., Ravikumar, P., Xing, E. (2020c). Learning sparse nonparametric dags. In Proceedings of AISTATS. PMLR, 2020, pp. 3414–3425.
• Zhong, W., Xu, J., Tang, D., Xu, Z., Duan, N., Zhou, M., Wang, J., Yin, J. (2020). Reasoning over semantic-level graph for fact checking. In Proceedings of ACL, 2020, pp. 6170–6180.
• Zhou, D., Huang, J., Schölkopf, B. (2007). Learning with hypergraphs: Clustering, classification, and embedding. In Proceedings of Advances in Neural Information Processing Systems, pp. 1601–1608.
• Zhou, Z., Li, X. (2017b). Graph convolution: A high-order and adaptive approach. ArXiv. Jun. 2017, arXiv:1706.09916
• Zhou, C., Liu, Y., Liu, X., Liu, Z., Gao, J. (2017a). Scalable graph embedding for asymmetric proximity. In Proceedings of the 31st AAAI Conference on Artificial Intelligence, pp. 2942–2948.
• Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M. (2020). Graph neural networks: A review of methods and applications. Al Open, vol. 1, 2020, pp. 57–81.
• Zhou, Y., Liu, S., Siow, J., Du, X., Liu, Y. (2019). Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks. In Proceedings of Adv. Neural Inf. Process. Syst., 2019, pp. 10197–10207.
• Zhu, D., Zhang, Z., Cui, P., Zhu, W. (2019). Robust graph convolutional networks against adversarial attacks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1399–1407.
• Zhu, J., Ahmed, A., Xing, E.P. (2012). MedLDA: Maximum margin supervised topic models. Journal of Machine Learning Research. vol. 13, no. pp. 2237–2278.
• Zhuang, C., Ma, Q. (2018). Dual graph convolutional networks for graphbased semi-supervised classification. in Proc. Web Conf., 2018, pp. 499–508.
• Zia, F., Sun, K., Yu, S., Aziz, A., Wan, L., Pan, S., Liu, H. (2021). Graph Learning: A Survey. IEEE Transactıons On Artificial Intelligence, vol. 2, no. 2, pp. 109–127.
• Zitnik, M., Agrawal, M., Leskovec, J. (2018). Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics, vol. 34, no. 13, i457–i466.
• Zügner, D., Gunnemann, S. (2019a). Adversarial attacks on graph neural networks via meta learning. In Proceedings of the 8th International Conference on Learning Representations, 2019.
• Zügner, D., Akbarnejad, A., Gunnemann, S. (2018). Adversarial attacks on neural networks for graph data. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2847–2856.
• Zügner, D., Günnemann, S. (2019b). Certifiable robustness and robust training for graph convolutional networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 246–256.
• Zügner, D., Günnemann, S. (2020). Certifiable robustness of graph convolutional networks under structure perturbations. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1656–1665.