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We present Karate Club a Python framework combining more than 30 state-of-the-art graph mining algorithms which can solve unsupervised machine learning tasks.
An information flow model for conflict and fission in small groups
Wayne W Zachary. 1977 · 1977
Earlier work this paper cites.
Laplacian eigenmaps and spectral techniques for embedding and clustering. In Advances in neural information processing systems . 585–591
Mikhail Belkin and Partha Niyogi. 2002 · 2002
Earlier work this paper cites.
Near Linear Time Algorithm to Detect Community Structures in Large-scale Networks
Usha Nandini Raghavan, Réka Albert, and Soundar Kumara. 2007 · 2007
Earlier work this paper cites.
Exploring network structure, dynamics, and function using NetworkX
Aric Hagberg, Pieter Swart, and Daniel S Chult. 2008 · 2008
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.
Gensim—statistical semantics in python
Radim Rehurek and Petr Sojka. 2011 · 2011
Earlier work this paper cites.
Low distortion delaunay embedding of trees in hyperbolic plane. In International Symposium on Graph Drawing . Springer, 355–366
Rik Sarkar. 2011 · 2011
Earlier work this paper cites.
The NumPy array: a structure for efficient numerical computation
Stéfan van der Walt, S Chris Colbert, and Gael Varoquaux. 2011 · 2011
Earlier work this paper cites.
Rolx: structural role extraction & mining in large graphs. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining . 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.
Symmetric nonnegative matrix factorization for graph clustering. In Proceedings of the 2012 SIAM international conference on data mining . SIAM, 106–117
Da Kuang, Chris Ding, and Haesun Park. 2012 · 2012
Earlier work this paper cites.
API design for machine learning software: experiences from the scikit-learn project
Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gramfort, Jaques Grobler, Robert Layton, Jacob VanderPlas, Arnaud Joly, Brian Holt, and Gaël Varoquaux. 2013 · 2013
Earlier work this paper cites.
Overlapping community detection at scale: a nonnegative matrix factorization approach. In Proceedings of the sixth ACM international conference on Web search and data mining . ACM, 587–596
Jaewon Yang and Jure Leskovec. 2013 · 2013
Earlier work this paper cites.
SNAP Datasets: Stanford Large Network Dataset Collection
Jure Leskovec and Andrej Krevl. 2014 · 2014
Earlier work this paper cites.
The graph-tool python library
Tiago P Peixoto. 2014 · 2014
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, 701–710
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014 · 2014
Earlier work this paper cites.
High quality, scalable and parallel community detection for large real graphs. In Proceedings of the 23rd international conference on World wide web . 225–236
Arnau Prat-Pérez, David Dominguez-Sal, and Josep-Lluis Larriba-Pey. 2014 · 2014
Earlier work this paper cites.
Alternating direction method of multipliers for non-negative matrix factorization with the beta-divergence. In 2014 IEEE international conference on acoustics, speech and signal processing (ICASSP) . IEEE, 6201–6205
Dennis L Sun and Cedric Fevotte. 2014 · 2014
Earlier work this paper cites.
Metric embedding, hyperbolic space, and social networks. In Proceedings of the thirtieth annual symposium on Computational geometry . 501–510
Kevin Verbeek and Subhash Suri. 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
Cited alongside, same era.
Deep Graph Kernels. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . 1365–1374
Pinar Yanardag and S.V.N. Vishwanathan. 2015 · 2015
Cited alongside, same era.
Network representation learning with rich text information. In Twenty-Fourth International Joint Conference on Artificial Intelligence
Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, and Edward Chang. 2015 · 2015
Cited alongside, same era.
Tensorflow: A system for large-scale machine learning. In 12th { \{ USENIX } \} Symposium on Operating Systems Design and Implementation ( { \{ OSDI } \} 16) . 265–283
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
Cited alongside, same era.
Netlsd: hearing the shape of a graph. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 2347–2356
Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alexander Bronstein, and Emmanuel Müller. 2018 · 2018
Later among the works it cites.
Binarized attributed network embedding. In 2018 IEEE International Conference on Data Mining (ICDM) . IEEE, 1476–1481
Hong Yang, Shirui Pan, Peng Zhang, Ling Chen, Defu Lian, and Chengqi Zhang. 2018 · 2018
Later among the works it cites.
Enhanced Network Embedding with Text Information. In 2018 24th International Conference on Pattern Recognition (ICPR) . IEEE, 326–331
Shuang Yang and Bo Yang. 2018 · 2018
Later among the works it cites.
Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM ’18) . 1393–1402
Fanghua Ye, Chuan Chen, and Zibin Zheng. 2018 · 2018
Later among the works it cites.
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Asymmetric transitivity preserving graph embedding. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining . 1105–1114
Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, and Wenwu Zhu. 2016 · 2016
Cited alongside, same era.
Ego-Splitting Framework: From Non-Overlapping to Overlapping Clusters. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’17) . 145–154
Alessandro Epasto, Silvio Lattanzi, and Renato Paes Leme. 2017 · 2017
Cited alongside, same era.
graph2vec: Learning distributed representations of graphs
Annamalai Narayanan, Mahinthan Chandramohan, Rajasekar Venkatesan, Lihui Chen, and Yang Liu. 2017 · 2017
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.
A non-negative symmetric encoder-decoder approach for community detection. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management . ACM, 597–606
Bing-Jie Sun, Huawei Shen, Jinhua Gao, Wentao Ouyang, and Xueqi Cheng. 2017 · 2017
Cited alongside, same era.
Hunt for the unique, stable, sparse and fast feature learning on graphs. In Advances in Neural Information Processing Systems . 88–98
Saurabh Verma and Zhi-Li Zhang. 2017 · 2017
Cited alongside, same era.
Community Preserving Network Embedding. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI’17) . 203–209
Xiao Wang, Peng Cui, Jing Wang, Jian Pei, Wenwu Zhu, and Shiqiang Yang. 2017 · 2017
Cited alongside, same era.
Fast network embedding enhancement via high order proximity approximation.. In IJCAI . 3894–3900
Cheng Yang, Maosong Sun, Zhiyuan Liu, and Cunchao Tu. 2017 · 2017
Cited alongside, same era.
SINE: Scalable Incomplete Network Embedding. In 2018 IEEE International Conference on Data Mining (ICDM) . IEEE, 737–746
Daokun Zhang, Jie Yin, Xingquan Zhu, and Chengqi Zhang. 2018 · 2018
Later among the works it cites.
role2vec: Role-based network embeddings. In Proc. DLG KDD
Nesreen K Ahmed, Ryan A Rossi, John Boaz Lee, Theodore L Willke, Rong Zhou, Xiangnan Kong, and Hoda Eldardiry. 2019 · 2019
Later among the works it cites.
GL2vec: Graph Embedding Enriched by Line Graphs with Edge Features. In International Conference on Neural Information Processing . Springer, 3–14
Hong Chen and Hisashi Koga. 2019 · 2019
Later among the works it cites.
Geometric Scattering for Graph Data Analysis. In Proceedings of the 36th International Conference on Machine Learning , Vol. 97. 2122–2131
Feng Gao, Guy Wolf, and Matthew Hirn. 2019 · 2019
Later among the works it cites.
Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation. In Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2019 . ACM, 50–56
Huan Liu Jundong Li, Liang Wu. 2019 · 2019
Later among the works it cites.
EdMot: An Edge Enhancement Approach for Motif-aware Community Detection. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’19) . 479–487
Pei-Zhen Li, Ling Huang, Chang-Dong Wang, and Jian-Huang Lai. 2019 · 2019
Later among the works it cites.
PyTorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems . 8024–8035
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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.
SciPy 1.0–fundamental algorithms for scientific computing in Python
Pauli Virtanen, Ralf Gommers, Travis E Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, et al · 2019
Later among the works it cites.
NodeSketch: Highly-Efficient Graph Embeddings via Recursive Sketching. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 1162–1172
Dingqi Yang, Paolo Rosso, Bin Li, and Philippe Cudre-Mauroux. 2019 · 2019
Later among the works it cites.
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
Benedek Rozemberczki, Oliver Kiss, and Rik Sarkar. 2020 · 2020
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Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models. In Proceedings of the 29th ACM International on Conference on Information and Knowledge Management (CIKM ’20) . ACM
Benedek Rozemberczki and Rik Sarkar. 2020 · 2020
Closest in time.