Topic · Graph Theory and Algorithms
Most cited papers in Graph Theory and Algorithms
What are the most cited papers in Graph Theory and Algorithms?
The most cited paper in Graph Theory and Algorithms is "A Comprehensive Survey on Graph Neural Networks" (Zonghan Wu, Shirui Pan, Fengwen Chen et al., 2020), with 10,010 citations. Next come "The Graph Neural Network Model" (9,764) and "Graph neural networks: A review of methods and applications" (5,883).
Showing 50 of 100 papers. Every paper with more than 518 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | 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 |
| 2 | The Graph Neural Network Model Franco Scarselli, M. Gori, Ah Chung Tsoi et al. · IEEE Transactions on Neural Networks | 10 Dec 2008 | 9,764 |
| 3 | Graph neural networks: A review of methods and applications Jie Zhou, Ganqu Cui, Shengding Hu et al. · AI Open | 2020 | 5,883 |
| 4 | Introduction to Graph Theory J. Fields | 2001 | 4,070 |
| 5 | Geometric Deep Learning: Going beyond Euclidean data Michael M. Bronstein, Joan Bruna, Yann LeCun et al. · IEEE Signal Processing Magazine | Jul 2017 | 3,748 |
| 6 | Pregel Grzegorz Greg Malewicz, Matthew H. Austern, Aart J. C. Bik et al. | 6 Jun 2010 | 3,571 |
| 7 | 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 |
| 8 | 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 |
| 9 | Apache Spark Matei Zaharia, Reynold Xin, Patrick Wendell et al. · Communications of the ACM | 28 Oct 2016 | 2,450 |
| 10 | Paths, Trees, and Flowers Jack Edmonds · Canadian Journal of Mathematics | 1965 | 2,399 |
| 11 | Spectral Hashing Yair Weiss, Antonio B. Torralba, Rob Fergus · neural information processing systems | 8 Dec 2008 | 2,081 |
| 12 | A Survey of General‐Purpose Computation on Graphics Hardware John D. Owens, David P. Luebke, Naga K. Govindaraju et al. · Computer Graphics Forum | Mar 2007 | 2,034 |
| 13 | Reduced surface: An efficient way to compute molecular surfaces Michel F. Sanner, Arthur J. Olson, Jean-Claude Spehner · Biopolymers | Mar 1996 | 1,996 |
| 14 | Graph convolutional networks: a comprehensive review Si Zhang, Hanghang Tong, Jiejun Xu et al. · Computational Social Networks | 10 Nov 2019 | 1,906 |
| 15 | Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs Federico Monti, Davide Boscaini, Jonathan Masci et al. | Jul 2017 | 1,877 |
| 16 | Apache Hadoop YARN Vinod Kumar Vavilapalli, Arun C. Murthy, Chris Douglas et al. | Oct 2013 | 1,865 |
| 17 | Survey of graph database models Renzo Angles, Claudio Gutiérrez · ACM Computing Surveys | Feb 2008 | 1,714 |
| 18 | A Review of Relational Machine Learning for Knowledge Graphs Maximilian Nickel, Kevin P. Murphy, Volker Tresp et al. · Proceedings of the IEEE | 17 Dec 2015 | 1,693 |
| 19 | Distributed GraphLab Yucheng Low, Danny Bickson, Joseph E. Gonzalez et al. · Proceedings of the VLDB Endowment | Apr 2012 | 1,675 |
| 20 | Practical graph isomorphism, II Brendan D. McKay, Adolfo Piperno · Journal of Symbolic Computation | 27 Sep 2013 | 1,646 |
| 21 | Weisfeiler-Lehman Graph Kernels Nino Shervashidze, Pascal Schweitzer, Erik Jan Van Leeuwen et al. · Max Planck Digital Library | 2010 | 1,594 |
| 22 | Discrete Signal Processing on Graphs Aliaksei Sandryhaila, José M. F. Moura · IEEE Transactions on Signal Processing | 8 Mar 2013 | 1,566 |
| 23 | Deep Learning on Graphs: A Survey Ziwei Zhang, Peng Cui, Wenwu Zhu · IEEE Transactions on Knowledge and Data Engineering | 17 Mar 2020 | 1,565 |
| 24 | An End-to-End Deep Learning Architecture for Graph Classification Muhan Zhang, Zhicheng Cui, Marion Neumann et al. · Proceedings of the AAAI Conference on Artificial Intelligence | 29 Apr 2018 | 1,524 |
| 25 | A (sub)graph isomorphism algorithm for matching large graphs Luigi Pietro Cordella, Pasquale Foggia, Carlo Sansone et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence | 16 Aug 2004 | 1,507 |
| 26 | PowerGraph: distributed graph-parallel computation on natural graphs Joseph E. Gonzalez, Yucheng Low, Haijie Gu et al. | 8 Oct 2012 | 1,503 |
| 27 | Heterogeneous Graph Transformer Ziniu Hu, Yuxiao Dong, Kuansan Wang et al. | 20 Apr 2020 | 1,463 |
| 28 | DeepGCNs: Can GCNs Go As Deep As CNNs? Guohao Li, Matthias Müller, Ali Thabet et al. | Oct 2019 | 1,387 |
| 29 | THIRTY YEARS OF GRAPH MATCHING IN PATTERN RECOGNITION Donatello Conte, Pasquale Foggia, Carlo Sansone et al. · International Journal of Pattern Recognition and Artificial Intelligence | May 2004 | 1,382 |
| 30 | Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science Ankit Kumar Agrawal, Alok Choudhary · APL Materials | 15 Apr 2016 | 1,370 |
| 31 | Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs Martin Simonovsky, Nikos Komodakis | Jul 2017 | 1,342 |
| 32 | On-line fingerprint verification Anil Kumar Jain, Hong Lin, Ruud M. Bolle · IEEE Transactions on Pattern Analysis and Machine Intelligence | Apr 1997 | 1,317 |
| 33 | Random-Walk Computation of Similarities between Nodes of a Graph with Application to Collaborative Recommendation François Fouss, Alain Pirotte, Jean-Michel Renders et al. · IEEE Transactions on Knowledge and Data Engineering | 9 Feb 2007 | 1,290 |
| 34 | R-MAT: A Recursive Model for Graph Mining Deepayan Chakrabarti, Yiping Zhan, Christos Faloutsos | 22 Apr 2004 | 1,276 |
| 35 | Networks of constraints: Fundamental properties and applications to picture processing Ugo Montanari · Information Sciences | 1974 | 1,271 |
| 36 | Query evaluation techniques for large databases Goetz Graefe · ACM Computing Surveys | Jun 1993 | 1,269 |
| 37 | A spectral technique for correspondence problems using pairwise constraints Marius Leordeanu, Martial Hebert | 2005 | 1,260 |
| 38 | Sampling from large graphs Jure Leskovec, Christos Faloutsos | 20 Aug 2006 | 1,204 |
| 39 | Vital nodes identification in complex networks Linyuan Lü, Duanbing Chen, Xiao-Long Ren et al. · Physics Reports | 3 Jul 2016 | 1,191 |
| 40 | Analysis of Recursive Game Graphs Using Data Flow Equations Kousha Etessami · Lecture notes in computer science | 2004 | 1,136 |
| 41 | A graduated assignment algorithm for graph matching Steven Gold, Anand Rangarajan · IEEE Transactions on Pattern Analysis and Machine Intelligence | Apr 1996 | 1,103 |
| 42 | Efficient Planarity Testing John E. Hopcroft, Robert Endre Tarjan · Journal of the ACM | Oct 1974 | 1,083 |
| 43 | Deep Neural Networks for Learning Graph Representations Shaosheng Cao, Wei Lu, Qiongkai Xu · Proceedings of the AAAI Conference on Artificial Intelligence | 21 Feb 2016 | 1,075 |
| 44 | Simple Linear-Time Algorithms to Test Chordality of Graphs, Test Acyclicity of Hypergraphs, and Selectively Reduce Acyclic Hypergraphs Robert Endre Tarjan, Mihalis Yannakakis · SIAM Journal on Computing | Aug 1984 | 1,068 |
| 45 | Graph theory with applications to engineering and computer science N.R. Malik · Proceedings of the IEEE | Oct 1975 | 1,048 |
| 46 | A distance measure between attributed relational graphs for pattern recognition Alberto Sanfeliu, King‐Sun Fu · IEEE Transactions on Systems Man and Cybernetics | May 1983 | 1,014 |
| 47 | Algorithm 447: efficient algorithms for graph manipulation John E. Hopcroft, Robert Endre Tarjan · Communications of the ACM | Jun 1973 | 996 |
| 48 | GraphChi: large-scale graph computation on just a PC Aapo Kyrola, Guy E. Blelloch, Carlos Guestrin | 8 Oct 2012 | 893 |
| 49 | The K-D-B-tree John T. Robinson | 1981 | 889 |
| 50 | On Graph Kernels: Hardness Results and Efficient Alternatives Thomas Gärtner, Peter A. Flach, Stefan Wrobel · Lecture notes in computer science | 2003 | 889 |
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.