A Graph-Based Article Recommendation System with Multilayer Citation

Abstract

We present a citation-centered article recommendation system that ranks works in a heterogeneous two-hop OpenAlex graph by combining structural connectivity with concept overlap and venue signals. Direct references form a primary, local-reading tier, whereas references reached through them form a secondary discovery tier. Ten target articles were evaluated from a data snapshot collected on March 8, 2026. Because concept-set Jaccard is also a scoring input, we supplemented it with an independent abstract-level TF–IDF cosine analysis and a leave-one-out hidden-reference experiment. For the full model, mean abstract cosine was 0.045 for primary and 0.029 for secondary recommendations among available abstract pairs. Across 358 held-out direct citations, 45.5% re-entered the two-hop candidate pool; Recall@10 was 0.112 overall and 0.245 when conditioned on candidate coverage. The networks were sparse and weakly connected when citation direction was ignored, although these properties are partly induced by ego-network construction. The findings show that a lightweight and interpretable hybrid can recover relevant citation-neighborhood works without model training, while also exposing candidate-generation and ground-truth limitations that require larger labelled or user-based evaluation.

Keywords

• citation networks
• complex networks
• recommendation systems
• paper recommendation
• OpenAlex

Project Number

395

Çok Katmanlı Atıf Yapısına Sahip Graf Tabanlı Bir Makale Öneri Sistemi

Abstract

Bu çalışmada, kullanıcı tarafından belirtilen bir hedef makale için ilgili çalışmaları belirlemek amacıyla akademik atıf ağlarının yapısından yararlanan atıf merkezli bir makale öneri sistemi sunuyoruz. OpenAlex kullanılarak, hedef makaleyi, birincil ve ikincil atıflarını, konu kavramlarını ve yayın mecralarını içeren heterojen çok katmanlı bir çizge oluşturuyoruz. Aday makaleler, yerel alaka için birincil öneriler ile daha geniş keşif için ikincil öneriler arasında bilinçli bir ayrımı korurken, yapısal bağlantılılığı anlamsal benzerlik ve yayın mecrası tabanlı tamamlayıcı sinyallerle bütünleştiren verimli bir hibrit puanlama modeli ile sıralanmaktadır. Sistem, kesin doğruluk niteliğinde alaka etiketleri mevcut olmadığı için vekil metrikler (Jaccard benzerliği ve ablasyon kararlılığı) kullanılarak atıf öneri literatüründen seçilen on hedef makale üzerinde değerlendirilmiştir. Oluşturulan ağlar büyük, seyrek ve iyi bağlantılıdır ve öneriler, birincil öneriler için 0.210 ve ikincil öneriler için 0.187 ortalama Jaccard benzerliği skorlarıyla tutarlı konu uyumu göstermiştir. Bu bulgular, basit ve yorumlanebilir bir hibrit tasarımın, pahalı gömme modelleri veya kullanıcı etkileşim verileri gerektirmeden hem yapısal olarak temellendirilmiş hem de anlamsal olarak anlamlı öneriler üretebileceğini göstermektedir.

Keywords

• atıf ağları
• karmaşık ağlar
• öneri sistemleri
• makale önerisi
• OpenAlex

Project Number

395

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