Graph embedding techniques, applications, and performance: A survey

Applied logistic regression

(1990) · Choice Reviews Online · doi:10.5860/choice.27-3338 · Cited by 35,305 · see citing works

Untitled work

(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works

Semi-supervised classification with graph convolutional networks

Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853 · see citing works

Auto-Encoding Variational Bayes

Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067 · see citing works

Normalized cuts and image segmentation

Shi, J., & Malik, J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.868688 · Cited by 15,842 · see citing works

Nonlinear Dimensionality Reduction by Locally Linear Embedding

Roweis, S. T., & Saul, L. K. (2000) · Science · doi:10.1126/science.290.5500.2323 · Cited by 14,965 · see citing works

Finding and evaluating community structure in networks

Newman, M. E. J., & Girvan, M. (2004) · Physical Review E · doi:10.1103/physreve.69.026113 · Cited by 13,931 · see citing works

Applied Logistic Regression

Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013) · Wiley Series in Probability and Statistics · doi:10.1002/9781118548387 · Cited by 13,861 · see citing works

LIII. On lines and planes of closest fit to systems of points in space

Karl Pearson, F. R. S. (1901) · The London Edinburgh and Dublin Philosophical Magazine and Journal of Science · doi:10.1080/14786440109462720 · Cited by 13,658 · see citing works

A Global Geometric Framework for Nonlinear Dimensionality Reduction

Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585 · see citing works

Representation Learning: A Review and New Perspectives

Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001 · see citing works

Proceedings of the 24th international conference on Machine learning

(2007) · doi:10.1145/1273496 · Cited by 11,869 · see citing works

Principal component analysis

Wold, S., Esbensen, K. H., & Geladi, P. L. M. (1987) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(87)80084-9 · Cited by 11,792 · see citing works

node2vec: Scalable Feature Learning for Networks

Grover, A., & Leskovec, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939754 · Cited by 10,926 · see citing works

Inductive Representation Learning on Large Graphs

Hamilton, W. L., Ying, R., & Leskovec, J. (2017) · arXiv · doi:10.48550/arxiv.1706.02216 · Cited by 10,639 · see citing works

DeepWalk: online learning of social representations

Perozzi, B., Al-Rfou, R., & Skiena, S. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623732 · Cited by 10,123 · see citing works

Advances In Deep Learning On Graphs (Gsp'18 Workshop)Preprint

Defferrard, M., Bresson, X., & Vandergheynst, P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.09375 · Cited by 6,912 · see citing works

Modeling by shortest data description

Rissanen, J. (1978) · Automatica · doi:10.1016/0005-1098(78)90005-5 · Cited by 6,011 · see citing works

LINE: Large-scale Information Network Embedding

Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., & Mei, Q. (2015) · Proceedings of the 24th International Conference on World Wide Web · doi:10.1145/2736277.2741093 · Cited by 5,416 · see citing works

Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks

Silva, G., Rodrigues, M., Teixeira, A., et al. (2024) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.sea.2024.20 · Cited by 5,266 · see citing works

Advances In Deep Learning On Graphs (Gsp'18 Workshop)

Defferrard, M. (2016) · Figshare · doi:10.5281/zenodo.1286817 · Cited by 4,977 · see citing works

An Information Flow Model for Conflict and Fission in Small Groups

Zachary, W. W. (1977) · Journal of Anthropological Research · doi:10.1086/jar.33.4.3629752 · Cited by 4,709 · see citing works

Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering

Belkin, M., & Niyogi, P. (2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.003.0080 · Cited by 4,534 · see citing works

Spectral Networks and Locally Connected Networks on Graphs

Bruna, J., Zaremba, W., Szlam, A., & LeCun, Y. (2013) · arXiv · doi:10.48550/arxiv.1312.6203 · Cited by 3,983 · see citing works

A New Status Index Derived from Sociometric Analysis

Katz, L. (1953) · Psychometrika · doi:10.1007/bf02289026 · Cited by 3,945 · see citing works

Locality Preserving Projections

He, X., & Niyogi, P. (2003) · Cited by 3,942 · see citing works

Gated Graph Sequence Neural NetworksPreprint

Li, Y., Tarlow, D., Brockschmidt, M., & Zemel, R. (2015) · International Conference on Learning Representations · doi:10.48550/arxiv.1511.05493 · Cited by 3,735 · see citing works

Variational Graph Auto-EncodersPreprint

Kipf, T., & Welling, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.07308 · Cited by 3,635 · see citing works

A comparison of event models for naive bayes text classification

McCallum, A., & Nigam, K. (1998) · Cited by 3,289 · see citing works

PCA versus LDA

Martinez, A. M., & Kak, A. C. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.908974 · Cited by 3,249 · see citing works

The link‐prediction problem for social networks

Liben‐Nowell, D., & Kleinberg, J. M. (2007) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.20591 · Cited by 3,063 · see citing works

Graph Embedding and Extensions: A General Framework for Dimensionality Reduction

Yan, S., Xu, D., Zhang, B., Zhang, H.-J., Yang, Q., & Lin, S. C.-F. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.250598 · Cited by 3,035 · see citing works

Friends and neighbors on the Web

Adamic, L. A., & Adar, E. (2003) · Social Networks · doi:10.1016/s0378-8733(03)00009-1 · Cited by 2,989 · see citing works

Stochastic blockmodels: First steps

Holland, P. W., Laskey, K. B., & Leinhardt, S. (1983) · Social Networks · doi:10.1016/0378-8733(83)90021-7 · Cited by 2,877 · see citing works

Structural Deep Network Embedding

Wang, D., Cui, P., & Zhu, W. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939753 · Cited by 2,845 · see citing works

Link prediction in complex networks: A survey

Lü, L., & Zhou, T. (2010) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2010.11.027 · Cited by 2,841 · see citing works

Convolutional Networks on Graphs for Learning Molecular Fingerprints

Duvenaud, D., Maclaurin, D., Aguilera-Iparraguirre, J., Gómez-Bombarelli, R., Hirzel, T., Aspuru-Guzik, A., & Adams, R. P. (2015) · arXiv · doi:10.48550/arxiv.1509.09292 · Cited by 2,628 · see citing works

Graph evolution

Leskovec, J., Kleinberg, J. M., & Faloutsos, C. (2007) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/1217299.1217301 · Cited by 2,494 · see citing works

A measure of betweenness centrality based on random walks

Newman, M. E. J. (2005) · Social Networks · doi:10.1016/j.socnet.2004.11.009 · Cited by 2,471 · see citing works

Étude comparative de la distribution florale dans une portion des Alpes et du Jura

Jaccard, P. (1901) · E-Periodica · doi:10.5169/seals-266450 · Cited by 2,409 · see citing works

Learning Convolutional Neural Networks for GraphsPreprint

Niepert, M., Ahmed, M., & Kutzkov, K. (2016) · International Conference on Machine Learning · doi:10.48550/arxiv.1605.05273 · Cited by 2,311 · see citing works

Hierarchical structure and the prediction of missing links in networks

Clauset, A., Moore, C., & Newman, M. E. J. (2008) · Nature · doi:10.1038/nature06830 · Cited by 2,171 · see citing works

Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks

Hornik, K., Stinchcombe, M. B., & White, H. (1990) · Neural Networks · doi:10.1016/0893-6080(90)90005-6 · Cited by 2,143 · see citing works

Social Structure from Multiple Networks. I. Blockmodels of Roles and Positions

White, H. C., Boorman, S. A., & Breiger, R. L. (1976) · American Journal of Sociology · doi:10.1086/226141 · Cited by 2,097 · see citing works

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 16

Storkey, A. (2004) · neural information processing systems · Cited by 2,022 · see citing works

Sparse Representation for Computer Vision and Pattern Recognition

Wright, J. N., Ma, Y., Mairal, J., Sapiro, G., Huang, T. S., & Yan, S. (2010) · Proceedings of the IEEE · doi:10.1109/jproc.2010.2044470 · Cited by 1,863 · see citing works

Proceedings of the 29th International Conference on Machine Learning (ICML-12)Preprint

Langford, J. C., & Pineau, J. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1207.4676 · Cited by 1,687 · see citing works

GraRep

Cao, S., Lu, W., & Xu, Q. (2015) · doi:10.1145/2806416.2806512 · Cited by 1,642 · see citing works

Identifying Resilient Communities in Road Networks: A Path-Based Embedding Approach

Wagner, C., Dodge, S., & Alizadeh, D. (2025) · Leibniz international proceedings in informatics · doi:10.4230/lipics.giscience.2025.9 · Cited by 1,599 · see citing works

Graph visualization and navigation in information visualization: A survey

Herman, I., Mélançon, G., & Marshall, M. S. (2000) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/2945.841119 · Cited by 1,559 · see citing works

Revisiting Semi-Supervised Learning with Graph EmbeddingsPreprint

Yang, Z., Cohen, W. W., & Salakhutdinov, R. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.08861 · Cited by 1,366 · see citing works

Asymmetric Transitivity Preserving Graph Embedding

Ou, M., Cui, P., Pei, J., Zhang, Z., & Zhu, W. (2016) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2939672.2939751 · Cited by 1,298 · see citing works

Random-Walk Computation of Similarities between Nodes of a Graph with Application to Collaborative Recommendation

Fouss, F., Pirotte, A., Renders, J.-M., & Saerens, M. (2007) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2007.46 · Cited by 1,265 · see citing works

Deep Convolutional Networks on Graph-Structured Data

Henaff, M., Bruna, J., & LeCun, Y. (2015) · arXiv · doi:10.48550/arxiv.1506.05163 · Cited by 1,255 · see citing works

The Maximum Clique Problem

Bomze, I. M., Budinich, M., Pardalos, P. M., & Pelillo, M. (1999) · Springer eBooks · doi:10.1007/978-1-4757-3023-4_1 · Cited by 1,168 · see citing works

Principal Component Analysis and Factor Analysis

Jolliffe, I. T. (1986) · Springer series in statistics · doi:10.1007/978-1-4757-1904-8_7 · Cited by 1,124 · see citing works

DeepWalk: Online Learning of Social RepresentationsPreprint

Perozzi, B., Al-Rfou, R., & Skiena, S. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1403.6652 · Cited by 1,109 · see citing works

Deep Neural Networks for Learning Graph Representations

Cao, S., Lu, W., & Xu, Q. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10179 · Cited by 1,070 · see citing works

Proceedings of the 24th International Conference on World Wide Web

Gangemi, A., Leonardi, S., & Panconesi, A. (2015) · doi:10.1145/2740908 · Cited by 1,054 · see citing works

The small world of human language

Ferrer‐i‐Cancho, R., & Solé, R. V. (2001) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2001.1800 · Cited by 1,050 · see citing works

Network representation learning with rich text information

Yang, C. H., Liu, Z., Zhao, D., Sun, M., & Chang, E. Y. (2015) · Cited by 977 · see citing works

Auto-Encoding Variational Bayes

Chen, Y.-K., Liu, J., Peng, L., Wu, Y., Xu, Y., & Zhang, Z. (2024) · Cambridge Explorations in Arts and Sciences · doi:10.61603/ceas.v2i1.33 · Cited by 939 · see citing works

Learning Probabilistic Relational Models

Getoor, L. C., Friedman, N., Koller, D., & Pfeffer, A. (2001) · Springer eBooks · doi:10.1007/978-3-662-04599-2_13 · Cited by 936 · see citing works

Graph clustering based on structural/attribute similarities

Zhou, Y., Cheng, H., & Yu, J. X. (2009) · Proceedings of the VLDB Endowment · doi:10.14778/1687627.1687709 · Cited by 865 · see citing works

Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

(2015) · doi:10.1145/2806416 · Cited by 860 · see citing works

Algorithms for drawing graphs: an annotated bibliography

Battista, G. D., Eades, P., Tamassia, R., & Tollis, I. G. (1994) · Computational Geometry · doi:10.1016/0925-7721(94)00014-x · Cited by 854 · see citing works

A min-max cut algorithm for graph partitioning and data clustering

Ding, C. H. Q., He, X., Hongyuan Zha, B., Gu, M., & Simon, H. D. (2002) · Proceedings · doi:10.1109/icdm.2001.989507 · Cited by 850 · see citing works

SCAN

Xu, X., Yuruk, N., Feng, Z., & Schweiger, T. A. J. (2007) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1281192.1281280 · Cited by 841 · see citing works

Relational learning via latent social dimensions

Tang, L., & Liu, H. (2009) · Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1557019.1557109 · Cited by 799 · see citing works

Social Network Data Analytics

Aggarwal, C. C. (2011) · Springer eBooks · doi:10.1007/978-1-4419-8462-3 · Cited by 785 · see citing works

Proceedings of the 22nd international conference on World Wide Web

(2013) · doi:10.1145/2488388 · Cited by 702 · see citing works

The BioGRID Interaction Database: 2008 update

Breitkreutz, B., Stark, C., Reguly, T., Boucher, L., Breitkreutz, A., Livstone, M. S., Oughtred, R., Lackner, D. H., Bähler, J., Wood, V., et al. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm1001 · Cited by 698 · see citing works

Link-based Classification

Getoor, L. C. (2006) · Springer eBooks · doi:10.1007/1-84628-284-5_7 · Cited by 688 · see citing works

Distributed large-scale natural graph factorization

Ahmed, A. A., Shervashidze, N., Narayanamurthy, S., Josifovski, V., & Smola, A. J. (2013) · International World Wide Web Conference (WWW) · doi:10.1145/2488388.2488393 · Cited by 669 · see citing works

Generalizing the Singular Value Decomposition

Loan, C. F. V. (1976) · SIAM Journal on Numerical Analysis · doi:10.1137/0713009 · Cited by 656 · see citing works

Visualizing Social Networks.

Freeman, L. C. (2000) · Journal of Social Structure · Cited by 655 · see citing works

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

(2015) · doi:10.1145/2783258 · Cited by 653 · see citing works

LINE: Large-Scale Information Network Embedding

Tang, J. (2024) · doi:10.57702/6e6l3h4g · Cited by 628 · see citing works

Heterogeneous Network Embedding via Deep Architectures

Chang, S., Han, W., Tang, J., Qi, G.-J., Aggarwal, C. C., & Huang, T. S. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2783296 · Cited by 602 · see citing works

A Survey of Link Prediction in Social Networks

Hasan, M. A., & Zaki, M. J. (2011) · Springer eBooks · doi:10.1007/978-1-4419-8462-3_9 · Cited by 571 · see citing works

NODE CLASSIFICATION IN SOCIAL NETWORKS

Bhagat, S., Cormode, G., & Muthukrishnan, S. (2011) · Cited by 500 · see citing works

Label Informed Attributed Network Embedding

Huang, X. X., Li, J., & Hu, X. (2017) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3018661.3018667 · Cited by 498 · see citing works

Stochastic blockmodels for directed graphs.

Wang, Y. J., & Wong, G. Y. (1987) · Journal of the American Statistical Association · doi:10.1080/01621459.1987.10478385 · Cited by 480 · see citing works

Network visualization and analysis of gene expression data using BioLayout Express3D

Theocharidis, A., van Dongen, S., Enright, A. J., & Freeman, T. C. (2009) · Nature Protocols · doi:10.1038/nprot.2009.177 · Cited by 453 · see citing works

Video suggestion and discovery for youtube

Baluja, S., Seth, R., Sivakumar, D., Jing, Y., Yagnik, J., Kumar, S., Ravichandran, D., & Aly, M. (2008) · International World Wide Web Conference (WWW) · doi:10.1145/1367497.1367618 · Cited by 447 · see citing works

Efficient aggregation for graph summarization

Tian, Y., Hankins, R. A., & Patel, J. M. (2008) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/1376616.1376675 · Cited by 444 · see citing works

A Spectral Clustering Approach To Finding Communities in Graphs

White, S., & Smyth, P. (2005) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972757.25 · Cited by 423 · see citing works

DynGEM: Deep Embedding Method for Dynamic GraphsPreprint

Goyal, P., Kamra, N., He, X., & Liu, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.11273 · Cited by 406 · see citing works

Proceedings of the 26th Annual International Conference on Machine Learning

(2009) · doi:10.1145/1553374 · Cited by 406 · see citing works

Proceedings of the 2019 International Conference on Management of Data

(2019) · doi:10.1145/3299869 · Cited by 396 · see citing works

The maximum clique problem

Pardalos, P. M., & Xue, J. (1994) · Journal of Global Optimization · doi:10.1007/bf01098364 · Cited by 384 · see citing works

Tri-party deep network representation

Pan, S., Wu, J. X., Xingquan Zhu, A., Zhang, C., & Wang, Y. (2016) · Griffith Research Online · Cited by 376 · see citing works

HARP: Hierarchical Representation Learning for Networks

Chen, H., Perozzi, B., Hu, Y., & Skiena, S. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11849 · Cited by 367 · see citing works

Graph summarization with bounded error

Navlakha, S., Rastogi, R. R., & Shrivastava, N. (2008) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/1376616.1376661 · Cited by 348 · see citing works

Graph Evolution: Densification and Shrinking DiametersPreprint

Leskovec, J., Kleinberg, J. M., & Faloutsos, C. (2006) · arXiv (Cornell University) · doi:10.48550/arxiv.physics/0603229 · Cited by 346 · see citing works

Iterative Classification in Relational Data

Neville, J., & Jensen, D. D. (2000) · Cited by 308 · see citing works

Graphs, Networks and Algorithms

Jungnickel, D. (2005) · Springer eBooks · doi:10.1007/b138283 · Cited by 287 · see citing works

Overview of the 2003 KDD Cup

Gehrke, J. E., Ginsparg, P., & Kleinberg, J. M. (2003) · ACM SIGKDD Explorations Newsletter · doi:10.1145/980972.980992 · Cited by 285 · see citing works

Proceedings of the Tenth ACM International Conference on Web Search and Data Mining

(2017) · doi:10.1145/3018661 · Cited by 265 · see citing works

Scalable Temporal Latent Space Inference for Link Prediction in Dynamic Social Networks

Zhu, L., Guo, D. M., Yin, J., et al. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2591009 · Cited by 259 · see citing works

Proceedings of the 18th ACM conference on Information and knowledge management

(2009) · doi:10.1145/1645953 · Cited by 254 · see citing works

Max-margin deepwalk: discriminative learning of network representation

Tu, C., Zhang, W., Liu, Z., & Sun, M. (2016) · International Joint Conference on Artificial Intelligence · Cited by 250 · see citing works

Scalable learning of collective behavior based on sparse social dimensions

Tang, L., & Liu, H. (2009) · Proceedings of the 18th ACM conference on Information and knowledge management · doi:10.1145/1645953.1646094 · Cited by 233 · see citing works

Learning probabilistic relational models

Schmidt, D. C. (2001) · Springer eBooks · Cited by 212 · see citing works

Structure preserving embedding

Shaw, B. J., & Jebara, T. (2009) · International Conference on Machine Learning · doi:10.1145/1553374.1553494 · Cited by 211 · see citing works

Stochastic Relational Models for Discriminative Link Prediction

Yu, K., Chu, W., Yu, S., et al. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7503.003.0199 · Cited by 201 · see citing works

How to draw a directed graph

Eades, P., & Xuemin, L. (2003) · IEEE Workshop on Visual Languages (VL) · doi:10.1109/wvl.1989.77035 · Cited by 161 · see citing works

Multidimensional scaling

Maital, S. (1978) · Journal of Econometrics · doi:10.1016/0304-4076(78)90088-x · Cited by 152 · see citing works

Learning Probabilistic Relational Models

Getoor, L. (2000) · Lecture notes in computer science · doi:10.1007/3-540-44914-0_25 · Cited by 152 · see citing works

Proceedings of the 2004 SIAM International Conference on Data Mining

(2004) · doi:10.1137/1.9781611972740 · Cited by 149 · see citing works

Recent advances in graph-based pattern recognition with applications in document analysis

Bunke, H., & Riesen, K. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.11.015 · Cited by 148 · see citing works

Probabilistic Entity-Relationship Models, PRMs, and Plate Models

Heckerman, D. E., Meek, C., & Koller, D. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7432.003.0009 · Cited by 142 · see citing works

Discriminative Deep Random Walk for Network Classification

Li, J., Zhu, J., & Zhang, B. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1095 · Cited by 142 · see citing works

Compression of weighted graphs

Toivonen, H. T. T., Zhou, F., Hartikainen, A., et al. (2011) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2020408.2020566 · Cited by 126 · see citing works

HARP: Hierarchical Representation Learning for NetworksPreprint

Chen, H., Perozzi, B., Hu, Y., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.07845 · Cited by 124 · see citing works

Graph Embedding in Vector Spaces by Means of Prototype Selection

Riesen, K., Neuhaus, M., & Horst Bunke, A. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-72903-7_35 · Cited by 116 · see citing works

Homophily, Structure, and Content Augmented Network Representation Learning

Zhang, D., Yin, J., Xingquan Zhu, A., & Zhang, C. (2016) · Proceedings · doi:10.1109/icdm.2016.0072 · Cited by 112 · see citing works

Semantic manifold learning for image retrieval

Lin, Y., Liu, T.-L., & Chen, H.-T. (2005) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1101149.1101193 · Cited by 106 · see citing works

Non-negative graph embedding

Yang, J., Yan, S., Fu, Y., Li, X., & Huang, T. S. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587665 · Cited by 103 · see citing works

Clique partitions, graph compression and speeding-up algorithms

Feder, T., & Motwani, R. (1991) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/103418.103424 · Cited by 101 · see citing works

The rendezvous algorithm

Azran, A. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273503 · Cited by 92 · see citing works

Proceedings of the 13th annual ACM international conference on Multimedia

(2005) · doi:10.1145/1101149 · Cited by 87 · see citing works

The ABACUS system for branch-and-cut-and-price algorithms in integer programming and combinatorial optimization

J�nger, M., & Thienel, S. (2000) · Software Practice and Experience · doi:10.1002/1097-024x(200009)30:11 · Cited by 86 · see citing works

Cauchy Graph Embedding

Luo, D., Nie, F., Huang, H., et al. (2011) · Cited by 76 · see citing works

Stochastic Blockmodels for Directed Graphs

Wang, Y. J., & Wong, G. Y. (1987) · Journal of the American Statistical Association · doi:10.2307/2289119 · Cited by 75 · see citing works

Deep Coevolutionary Network: Embedding User and Item Features for RecommendationPreprint

Dai, H., Wang, Y., Trivedi, R., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.03675 · Cited by 72 · see citing works

Link-Based Classification

Alhajj, R., & Rokne, J. G. (2014) · Springer eBooks · doi:10.1007/978-1-4614-6170-8_100636 · Cited by 69 · see citing works

Continuous nonlinear dimensionality reduction by kernel eigenmaps

Brand, M. (2003) · Cited by 60 · see citing works

Walklets: Multiscale Graph Embeddings for Interpretable Network Classification.Preprint

Perozzi, B., Kulkarni, V., & Skiena, S. (2016) · arXiv (Cornell University) · Cited by 58 · see citing works

Postpartum options: matching the method to individual needs.

Townsend, S. (1990) · PubMed · Cited by 55 · see citing works

Graphs, Networks and Algorithms.

Smith, D. K., & Jungnickel, D. (1999) · Journal of the Operational Research Society · doi:10.2307/3010642 · Cited by 49 · see citing works

Applying link-based classification to label blogs

Bhagat, S., Rozenbaum, I., & Cormode, G. (2007) · doi:10.1145/1348549.1348560 · Cited by 48 · see citing works

Don't Walk, Skip! Online Learning of Multi-scale Network EmbeddingsPreprint

Perozzi, B., Kulkarni, V., Chen, H., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.02115 · Cited by 37 · see citing works

Proceedings of the twenty-third annual ACM symposium on Theory of computing

Koutsougeras, C., & Vitter, J. S. (1991) · Symposium on the Theory of Computing · Cited by 24 · see citing works

Link Prediction in Complex Networks

Pujari, M., & Kanawati, R. (2016) · Advances in wireless technologies and telecommunication book series · doi:10.4018/978-1-4666-9964-9.ch003 · Cited by 23 · see citing works

Multi-Modal Bayesian Embeddings for Learning Social Knowledge GraphsPreprint

Yang, Z., Tang, J., & Cohen, W. W. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1508.00715 · Cited by 20 · see citing works

Graphs, networks and algorithms

Viennot, L., Habib, M., Croisy, D., et al. (2007) · Cited by 3 · see citing works

Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories

Chen, T., Wang, Z., Li, L., Li, D., Li, Z., Chang, X., Bian, P., Liang, G., Wang, Q., & Xie, T. (2025) · Schloss Dagstuhl – Leibniz-Zentrum für Informatik · doi:10.4230/lipics.ecoop.2025.6

A Comprehensive Survey on Graph Neural Networks

Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2978386 · Cited by 11,000 · see citing works

Graph neural networks: A review of methods and applications

Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020) · AI Open · doi:10.1016/j.aiopen.2021.01.001 · Cited by 6,880 · see citing works

A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications

Cai, H., Zheng, V. W., & Chang, K. C.-C. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2807452 · Cited by 2,034 · see citing works

Graph convolutional networks: a comprehensive review

Zhang, S., Tong, H., Xu, J., & Maciejewski, R. (2019) · Computational Social Networks · doi:10.1186/s40649-019-0069-y · Cited by 1,888 · see citing works

Network Representation Learning: A Survey

Zhang, D., Yin, J., Zhu, X., & Zhang, C. (2020) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2018.2850013 · Cited by 714 · see citing works

Deep learning similarities from different representations of source code

Tufano, M., Watson, C., Bavota, G., Di Penta, M., White, M., & Poshyvanyk, D. (2018) · Proceedings of the 15th International Conference on Mining Software Repositories · doi:10.1145/3196398.3196431 · Cited by 158 · see citing works

DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques

Thafar, M. A., Olayan, R. S., Ashoor, H., Albaradei, S., Bajic, V. B., Gao, X., Gojobori, T., & Essack, M. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00447-2 · Cited by 143 · see citing works

Applications of near-term photonic quantum computers: software and algorithms

Bromley, T. R., Arrazola, J. M., Jahangiri, S., Izaac, J., Quesada, N., Gran, A. D., Schuld, M., Swinarton, J., Zabaneh, Z., & Killoran, N. (2020) · Quantum Science and Technology · doi:10.1088/2058-9565/ab8504 · Cited by 128 · see citing works

Hyperbolic Entailment Cones for Learning Hierarchical Embeddings

Ganea, O.-E., Bécigneul, G., & Hofmann, T. (2018) · arXiv · doi:10.48550/arxiv.1804.01882 · Cited by 115 · see citing works

Network analysis reveals rare disease signatures across multiple levels of biological organization

Buphamalai, P., Kokotovic, T., Nagy, V., & Menche, J. (2021) · Nature Communications · doi:10.1038/s41467-021-26674-1 · Cited by 95 · see citing works

Formatting biological big data for modern machine learning in drug discovery

Duran‐Frigola, M., Fernández‐Torras, A., Bertoni, M., & Aloy, P. (2019) · WIREs Computational Molecular Science · doi:10.1002/wcms.1408 · Cited by 25 · see citing works

Dual embedding with input embedding and output embedding for better word representation

Ahn, Y., Rhee, E., & Lee, J. (2022) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v27.i2.pp1091-1099

Embedding complete multi-partite graphs into Cartesian product of paths and cycles

Rajan, R. S., Shantrinal, A. A., Rajalaxmi, T. M., et al. (2021) · Electronic Journal of Graph Theory and Applications · doi:10.5614/ejgta.2021.9.2.21 · Cited by 4 · see citing works

Embedding of Complete graphs and Cycles into Grids with holes

Mary, R. S., & Rajasingh, I. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.05.017 · Cited by 1 · see citing works

Embedding complete multi-partite graphs into Cartesian product of paths and cyclesPreprint

Rajan, R. S., Shantrinal, A. A., Kumar, K., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.07717 · Cited by 2 · see citing works

Planarity Testing and Optimal Edge Insertion with Embedding Constraints

Gutwenger, C., Klein, K., & Mutzel, P. (2007) · Springer eBooks · doi:10.1007/978-3-540-70904-6_14 · Cited by 3 · see citing works