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Proximity preserving and structural role-based node embeddings have become a prime workhorse of applied graph mining.
It’s Who You Know: Graph Mining Using Recursive Structural Features. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data mining . ACM, 663–671
Keith Henderson, Brian Gallagher, Lei Li, Leman Akoglu, Tina Eliassi-Rad, Hanghang Tong, and Christos Faloutsos. 2011 · 2011
Earlier work this paper cites.
Scikit-Learn: Machine Learning in Python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
Earlier work this paper cites.
RolX: Structural Role Extraction and Mining in Large Graphs. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 1231–1239
Keith Henderson, Brian Gallagher, Tina Eliassi-Rad, Hanghang Tong, Sugato Basu, Leman Akoglu, Danai Koutra, Christos Faloutsos, and Lei Li. 2012 · 2012
Earlier work this paper cites.
API Design for Machine Learning Software: Experiences from the Scikit-Learn Project. In ECML PKDD Workshop: Languages for Data Mining and Machine Learning . 108–122
Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gramfort, Jaques Grobler, Robert Layton, Jake VanderPlas, Arnaud Joly, Brian Holt, and Gaël Varoquaux. 2013 · 2013
Earlier work this paper cites.
DeepWalk: Online Learning of Social Representations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014 · 2014
Earlier work this paper cites.
GraRep: Learning Graph Representations with Global Structural Information. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management . ACM, 891–900
Shaosheng Cao, Wei Lu, and Qiongkai Xu. 2015 · 2015
Earlier work this paper cites.
LINE: Large-Scale Information Network Embedding. In Proceedings of the 24th International Conference on World Wide Web . 1067–1077
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015 · 2015
Earlier work this paper cites.
Node2Vec: Scalable Feature Learning for Networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . 855–864
Aditya Grover and Jure Leskovec. 2016 · 2016
Cited alongside, same era.
Asymmetric Transitivity Preserving Graph Embedding. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 1105–1114
Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, and Wenwu Zhu. 2016 · 2016
Cited alongside, same era.
Don’t Walk, Skip!: Online Learning of Multi-scale Network Embeddings. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 . ACM, 258–265
Bryan Perozzi, Vivek Kulkarni, Haochen Chen, and Steven Skiena. 2017 · 2017
Cited alongside, same era.
Learning Role-based Graph Embeddings
Nesreen K Ahmed, Ryan Rossi, John Boaz Lee, Xiangnan Kong, Theodore L Willke, Rong Zhou, and Hoda Eldardiry. 2018 · 2018
Cited alongside, same era.
Fast Sequence-Based Embedding with Diffusion Graphs. In International Workshop on Complex Networks . Springer, 99–107
Benedek Rozemberczki and Rik Sarkar. 2018 · 2018
Later among the works it cites.
Billion-scale network embedding with iterative random projection. In 2018 IEEE International Conference on Data Mining (ICDM) . IEEE, 787–796
Ziwei Zhang, Peng Cui, Haoyang Li, Xiao Wang, and Wenwu Zhu. 2018 · 2018
Later among the works it cites.
Multi-Scale Attributed Node Embedding
Benedek Rozemberczki, Carl Allen, and Rik Sarkar. 2019a · 2019
Later among the works it cites.
GEMSEC: Graph Embedding with Self Clustering. In Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2019 . ACM, 65–72
Benedek Rozemberczki, Ryan Davies, Rik Sarkar, and Charles Sutton. 2019b · 2019
Later among the works it cites.
Role-based Graph Embeddings
N. Ahmed, R. A. Rossi, J. Lee, T. Willke, R. Zhou, X. Kong, and H. Eldardiry. 2020 · 2020
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Learning Structural Node Embeddings via Diffusion Wavelets. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 1320–1329
Claire Donnat, Marinka Zitnik, David Hallac, and Jure Leskovec. 2018 · 2018
Cited alongside, same era.
Attributed Social Network Embedding
Lizi Liao, Xiangnan He, Hanwang Zhang, and Tat-Seng Chua. 2018 · 2018
Cited alongside, same era.
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and Node2Vec. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining . ACM, 459–467
Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, and Jie Tang. 2018 · 2018
Cited alongside, same era.
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM ’20) . ACM
Benedek Rozemberczki, Oliver Kiss, and Rik Sarkar. 2020a
Cited in the paper.
Little Ball of Fur: A Python Library for Graph Sampling. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM ’20) . ACM, 3133–3140
Benedek Rozemberczki, Oliver Kiss, and Rik Sarkar. 2020b
Cited in the paper.
Later among the works it cites.
Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM ’20) . ACM
Benedek Rozemberczki and Rik Sarkar. 2020 · 2020
Later among the works it cites.