Topic · Advanced Graph Neural Networks
Most cited papers in Advanced Graph Neural Networks
What are the most cited papers in Advanced Graph Neural Networks?
The most cited paper in Advanced Graph Neural Networks is "Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories" (Chen, Tianyu, Wang, Zeyu, Li, Lin et al., 2025), with 16,321 citations. Next come "A tutorial on spectral clustering" (10,505) and "A Comprehensive Survey on Graph Neural Networks" (10,010).
Showing 50 of 98 papers. Every paper with more than 1,135 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories Chen, Tianyu, Wang, Zeyu, Li, Lin et al. · Leibniz international proceedings in informatics | 2025 | 16,321 |
| 2 | A tutorial on spectral clustering Ulrike von Luxburg · Statistics and Computing | 21 Aug 2007 | 10,505 |
| 3 | A Comprehensive Survey on Graph Neural Networks Zonghan Wu, Shirui Pan, Fengwen Chen et al. · IEEE Transactions on Neural Networks and Learning Systems | 24 Mar 2020 | 10,010 |
| 4 | The Graph Neural Network Model Franco Scarselli, M. Gori, Ah Chung Tsoi et al. · IEEE Transactions on Neural Networks | 10 Dec 2008 | 9,764 |
| 5 | Authoritative sources in a hyperlinked environment Jon Kleinberg · Journal of the ACM | Sep 1999 | 9,110 |
| 6 | DeepWalk Bryan Perozzi, Rami Al-Rfou, Steven Skiena | 22 Aug 2014 | 8,705 |
| 7 | Semi-Supervised Classification with Graph Convolutional Networks Thomas Kipf, Max Welling · arXiv (Cornell University) | 9 Sep 2016 | 8,050 |
| 8 | Graph neural networks: A review of methods and applications Jie Zhou, Ganqu Cui, Shengding Hu et al. · AI Open | 2020 | 5,883 |
| 9 | From Louvain to Leiden: guaranteeing well-connected communities V. A. Traag, L. Waltman, Nees Jan van Eck · Scientific Reports | 26 Mar 2019 | 5,618 |
| 10 | Modeling Relational Data with Graph Convolutional Networks Michael Schlichtkrull, Thomas Kipf, Peter Bloem et al. · Lecture notes in computer science | 2018 | 5,280 |
| 11 | Translating Embeddings for Modeling Multi-relational Data Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran et al. | 5 Dec 2013 | 5,185 |
| 12 | A faster algorithm for betweenness centrality* Ulrik Brandes · Journal of Mathematical Sociology | Jun 2001 | 5,024 |
| 13 | LINE Jian Tang, Meng Qu, Mingzhe Wang et al. | 18 May 2015 | 4,765 |
| 14 | The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains David I Shuman, Sunil K. Narang, Pascal Frossard et al. · IEEE Signal Processing Magazine | 5 Apr 2013 | 4,577 |
| 15 | LightGCN Xiangnan He, Kuan Deng, Xiang Wang et al. | 25 Jul 2020 | 4,418 |
| 16 | The MovieLens Datasets F. Maxwell Harper, Joseph A. Konstan · ACM Transactions on Interactive Intelligent Systems | 22 Dec 2015 | 3,966 |
| 17 | Knowledge Graph Embedding by Translating on Hyperplanes Zhen Wang, Jianwen Zhang, Jianlin Feng et al. · Proceedings of the AAAI Conference on Artificial Intelligence | 21 Jun 2014 | 3,856 |
| 18 | Learning Entity and Relation Embeddings for Knowledge Graph Completion Yankai Lin, Zhiyuan Liu, Maosong Sun et al. · Proceedings of the AAAI Conference on Artificial Intelligence | 19 Feb 2015 | 3,697 |
| 19 | Collective Classification in Network Data Prithviraj Sen, Galileo Mark S. Namata, Mustafa Bilgic et al. · AI Magazine | Sep 2008 | 3,368 |
| 20 | A Comprehensive Survey on Graph Neural Networks Wu, Z, Pan, S, Chen, F et al. · UTS ePRESS (University of Technology Sydney) | 24 Mar 2020 | 3,301 |
| 21 | Neural Graph Collaborative Filtering Xiang Wang, Xiangnan He, Meng Wang et al. | 18 Jul 2019 | 3,200 |
| 22 | Factorization Machines Steffen Rendle | Dec 2010 | 3,131 |
| 23 | Benchmark graphs for testing community detection algorithms Andrea Lancichinetti, Santo Fortunato, Filippo Radicchi · Physical Review E | 24 Oct 2008 | 3,117 |
| 24 | The structure and dynamics of multilayer networks S. Boccaletti, G. Bianconi, R. Criado et al. · Physics Reports | 10 Jul 2014 | 3,109 |
| 25 | The link‐prediction problem for social networks David Liben‐Nowell, Jon Kleinberg · Journal of the American Society for Information Science and Technology | 26 Mar 2007 | 3,055 |
| 26 | Heterogeneous Graph Attention Network Xiao Wang, Houye Ji, Chuan Shi et al. | 13 May 2019 | 3,027 |
| 27 | Graph WaveNet for Deep Spatial-Temporal Graph Modeling Zonghan Wu, Shirui Pan, Guodong Long et al. | 28 Jul 2019 | 2,951 |
| 28 | A Survey on Knowledge Graphs: Representation, Acquisition, and Applications Shaoxiong Ji, Shirui Pan, Erik Cambria et al. · IEEE Transactions on Neural Networks and Learning Systems | 26 Apr 2021 | 2,918 |
| 29 | Link prediction in complex networks: A survey Linyuan Lü, Tao Zhou · Physica A Statistical Mechanics and its Applications | 3 Dec 2010 | 2,866 |
| 30 | Graph Convolutional Neural Networks for Web-Scale Recommender Systems Rex Ying, Ruining He, Kaifeng Chen et al. | 19 Jul 2018 | 2,858 |
| 31 | Structural Deep Network Embedding Daixin Wang, Peng Cui, Wenwu Zhu | 8 Aug 2016 | 2,850 |
| 32 | Spectral Networks and Locally Connected Networks on Graphs Joan Bruna, Wojciech Zaremba, Arthur D. Szlam et al. · arXiv (Cornell University) | 21 Dec 2013 | 2,724 |
| 33 | Knowledge Graph Embedding: A Survey of Approaches and Applications Quan Wang, Zhendong Mao, Bin Wang et al. · IEEE Transactions on Knowledge and Data Engineering | 20 Sep 2017 | 2,717 |
| 34 | Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning Qimai Li, Zhichao Han, Xiao-Ming Wu · Proceedings of the AAAI Conference on Artificial Intelligence | 29 Apr 2018 | 2,669 |
| 35 | Axiomatic Attribution for Deep Networks Mukund Sundararajan, Ankur Taly, Qiqi Yan · arXiv (Cornell University) | 4 Mar 2017 | 2,648 |
| 36 | Analysis of weighted networks Mark E. J. Newman · Physical Review E | 24 Nov 2004 | 2,536 |
| 37 | Convolutional 2D Knowledge Graph Embeddings Tim Dettmers, Pasquale Minervini, Pontus Stenetorp et al. · Proceedings of the AAAI Conference on Artificial Intelligence | 25 Apr 2018 | 2,479 |
| 38 | metapath2vec Yuxiao Dong, Nitesh V. Chawla, Ananthram Swami | 4 Aug 2017 | 2,303 |
| 39 | KGAT Xiang Wang, Xiangnan He, Yixin Cao et al. | 25 Jul 2019 | 2,179 |
| 40 | A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications Hongyun Cai, Vincent Wenchen Zheng, Kevin Chen–Chuan Chang · IEEE Transactions on Knowledge and Data Engineering | 19 Feb 2018 | 2,085 |
| 41 | Embedding Entities and Relations for Learning and Inference in Knowledge Bases Wu, Hong, Wang, Zhe, Wang, Kewen et al. · arXiv (Cornell University) | 20 Dec 2014 | 2,049 |
| 42 | Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding Jiaxi Tang, Ke Wang | 2 Feb 2018 | 2,015 |
| 43 | Graph Neural Networks for Social Recommendation Wenqi Fan, Yao Ma, Qing Li et al. | 13 May 2019 | 1,993 |
| 44 | SimRank Glen Jeh, Jennifer Widom | 23 Jul 2002 | 1,959 |
| 45 | PathSim Yizhou Sun, Jiawei Han, Xifeng Yan et al. · Proceedings of the VLDB Endowment | Aug 2011 | 1,941 |
| 46 | Graph convolutional networks: a comprehensive review Si Zhang, Hanghang Tong, Jiejun Xu et al. · Computational Social Networks | 10 Nov 2019 | 1,906 |
| 47 | Content-based Recommender Systems: State of the Art and Trends Pasquale Lops, Marco de Gemmis, Giovanni Maria Semeraro | 5 Oct 2010 | 1,905 |
| 48 | Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs Federico Monti, Davide Boscaini, Jonathan Masci et al. | Jul 2017 | 1,877 |
| 49 | Knowledge Graph Embedding via Dynamic Mapping Matrix Guoliang Ji, Shizhu He, Liheng Xu et al. | 2015 | 1,872 |
| 50 | Graph embedding techniques, applications, and performance: A survey Palash Goyal, Emilio Ferrara · Knowledge-Based Systems | 16 Mar 2018 | 1,854 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.