External resources
==================

Overlapping Community Detection
--------------------

* Alessandro Epasto, Silvio Lattanzi, Renato Paes Leme: **Ego-splitting Framework: from Non-Overlapping to Overlapping Clusters** `Paper <https://www.eecs.yorku.ca/course_archive/2017-18/F/6412/reading/kdd17p145.pdf>`_, `Slides <https://epasto.org/papers/kdd2017-Slides.pdf>`_, `Video <https://www.youtube.com/watch?v=xMGZo-F_jss>`_, `Code <https://github.com/benedekrozemberczki/EgoSplitting>`_

* Fanghua Ye, Chuan Chen, Zibin Zheng: **Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection** `Paper <https://smartyfh.com/Documents/18DANMF.pdf>`_, `Code <https://github.com/benedekrozemberczki/DANMF>`_

* Xiao Wang, Peng Cui, Jing Wang, Jian Pei, Wenwu Zhu, Shiqiang Yang: **Community Preserving Network Embedding** `Paper <https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14589>`_, `Code <https://github.com/benedekrozemberczki/M-NMF>`_

* Bing-Jie Sun, Huawei Shen, Jinhua Gao, Wentao Ouyang, Xueqi Cheng: **A Non-negative Symmetric Encoder-Decoder Approach for Community Detection** `Paper <http://www.bigdatalab.ac.cn/~shenhuawei/publications/2017/cikm-sun.pdf>`_

* Jaewon Yang and Jure Leskovec: **Overlapping Community Detection at Scale:  A Nonnegative Matrix Factorization Approach** `Paper <http://infolab.stanford.edu/~crucis/pubs/paper-nmfagm.pdf>`_, `Slides <http://snap.stanford.edu/class/cs224w-2017/slides/18-overlapping.pdf>`_, `Video <https://www.youtube.com/watch?v=Y78Kugdq24I>`_, `Video <https://www.youtube.com/watch?v=cuDDBfvK71g>`_

* Da Kuang, Chris Ding, Haesun Park: **Symmetric Nonnegative Matrix Factorization for Graph Clustering** `Paper <https://www.cc.gatech.edu/~hpark/papers/DaDingParkSDM12.pdf>`_

Non-Overlapping Community Detection
--------------------

*  Benedek Rozemberczki, Ryan Davies, Rik Sarkar, and Charles Sutton: **GEMSEC: Graph Embedding with Self Clustering** `Paper <https://arxiv.org/abs/1802.03997>`_, `Code <https://github.com/benedekrozemberczki/GEMSEC>`_

* Pei-Zhen Li, Ling Huang, Chang-Dong Wang, and Jian-Huang Lai: **EdMot: An Edge Enhancement Approach for Motif-aware Community Detection** `Paper <https://arxiv.org/abs/1906.04560>`_, `Video <https://www.youtube.com/watch?v=6fEGMHJSsq0>`_, `Code <https://github.com/benedekrozemberczki/EdMot>`_

* Arnau Prat-Perez, David Dominguez-Sal, Joseph-Luis Larriba-Pey: **High Quality, Scalable and Parallel Community Detectionfor Large Real Graphs** `Paper <http://wwwconference.org/proceedings/www2014/proceedings/p225.pdf>`_

* Usha Nandini Raghavan, Reka Albert, Soundar Kumara: **Near Linear Time Algorithm to Detect Community Structures in Large-Scale Networks** `Paper <https://arxiv.org/abs/0709.2938>`_, `Code <https://github.com/benedekrozemberczki/LabelPropagation>`_

Neighbourhood-Based Node Embedding
--------------------

* Lei Tang, Huan Liu: **Relational Learning via Latent Social Dimensions** `Paper <http://www.public.asu.edu/~huanliu/papers/kdd09.pdf>`_

* Leo Torres, Kevin S Chan, Tina Eliassi-Rad: **GLEE: Geometric Laplacian Eigenmap Embedding** `Paper <https://arxiv.org/abs/1905.09763>`_, `Code <https://github.com/leotrs/glee>`_

* Jundong Li, Liang Wu, and Huan Liu: **Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation** `Paper <https://arxiv.org/abs/1808.08627>`_

* Dingqi Yang, Paolo Rosso, Bin Li, and Philippe Cudre-Mauroux: **NodeSketch: Highly-Efficient Graph Embeddingsvia Recursive Sketching** `Paper <https://exascale.info/assets/pdf/yang2019nodesketch.pdf>`_

* Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, and Jie Tang: **Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and Node2Vec** `Paper <https://keg.cs.tsinghua.edu.cn/jietang/publications/WSDM18-Qiu-et-al-NetMF-network-embedding.pdf>`_

* Ziwei Zhang, Peng Cui, Haoyang Li, Xiao Wang, Wenwu Zhu: **Billion-scale Network Embedding with Iterative Random Projection** `Paper <https://arxiv.org/abs/1805.02396>`_, `Code <https://github.com/ZW-ZHANG/RandNE>`_

* Benedek Rozemberczki and Rik Sarkar: **Fast Sequence Based Embedding with Diffusion Graphs** `Paper <https://arxiv.org/abs/2001.07463>`_, `Code <https://github.com/benedekrozemberczki/diff2vec>`_

* Bryan Perozzi, Vivek Kulkarni, Haochen Chen, Steven Skiena: **Don't Walk, Skip! Online Learning of Multi-scale Network Embeddings** `Paper <https://arxiv.org/abs/1605.02115>`_, `Code <https://github.com/benedekrozemberczki/walklets>`_

* Mingdong Ou, Peng  Cui, Jian  Pei, Ziwei  Zhang, Wenwu Zhu: **Asymmetric Transitivity Preserving Graph Embedding** `Paper <https://dl.acm.org/doi/abs/10.1145/2939672.2939751>`_

* Shaosheng Cao, Wei Lu, Qiongkai Xu: **GraRep: Learning Graph Representations with Global Structural Information** `Paper <https://dl.acm.org/citation.cfm?id=2806512>`_, `Code <https://github.com/benedekrozemberczki/GraRep>`_

* Aditya Grover and Jure Leskovec: **Node2Vec: Scalable Feature Learning for Networks** `Paper <https://cs.stanford.edu/~jure/pubs/node2vec-kdd16.pdf>`_, `Video <https://www.youtube.com/watch?v=1_QH5BEP5BM>`_

* Bryan Perozzi, Rami Al-Rfou, Steven Skiena: **DeepWalk: Online Learning of Social Representations** `Paper <https://arxiv.org/abs/1403.6652>`_, `Slides <https://www.slideshare.net/bperz/14-kdddeep-walk-2>`_, `Video <https://www.youtube.com/watch?v=n12HS-24CtA>`_, `Video <https://www.youtube.com/watch?v=aZNtHJwfIVg>`_

* Dennis L. Sun and Cédric Févotte: **Alternating Direction Method of Multipliers for Non-Negative Matrix Factorization with the Beta-Divergence** `Paper <http://statweb.stanford.edu/~dlsun/papers/nmf_admm.pdf>`_, `Code <https://github.com/benedekrozemberczki/NMFADMM>`_

* Mikhail Belkin and Partha Niyogi: **Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering** `Paper <http://papers.nips.cc/paper/1961-laplacian-eigenmaps-and-spectral-techniques-for-embedding-and-clustering.pdf>`_, `Video <https://www.youtube.com/watch?v=U31TIICsHiA>`_

Structural Node Embedding
--------------------

* Claire Donnat, Marinka Zitnik, David Hallac, Jure Leskovec: **Learning Structural Node Embeddings Via Diffusion Wavelets** `Paper <https://arxiv.org/abs/1710.10321>`_, `Video <https://www.youtube.com/watch?v=S4QZiUPJkRI>`_, `Code <https://github.com/benedekrozemberczki/GraphWaveMachine>`_

* Nesreen K. Ahmed, Ryan Rossi, John Boaz Lee, Theodore L. Willke, Rong Zhou, Xiangnan Kong, Hoda Eldardiry: **Learning Role-based Graph Embeddings** `Paper <https://arxiv.org/abs/1802.02896>`_, `Code <https://github.com/benedekrozemberczki/Role2Vec>`_

Attributed Node Embedding
--------------------

* Benedek Rozemberczki, Rik Sarkar: **Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models** `Paper <https://arxiv.org/abs/2005.07959>`_, `Code <https://github.com/benedekrozemberczki/FEATHER>`_

* Benedek Rozemberczki, Carl Allen, Rik Sarkar: **Multi-Scale Attributed Node Embedding** `Paper <https://arxiv.org/abs/1909.13021>`_, `Code <https://github.com/benedekrozemberczki/MUSAE>`_

* Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang: **SINE: Scalable Incomplete Network Embedding** `Paper <https://arxiv.org/pdf/1810.06768.pdf>`_, `Code <https://github.com/benedekrozemberczki/SINE>`_

* Hong Yang, Shirui Pan, Peng Zhang, Ling Chen, Defu Lian, Chengqi Zhang: **Binarized Attributed Network Embedding** `Paper <https://ieeexplore.ieee.org/document/8626170>`_, `Code <https://github.com/benedekrozemberczki/BANE>`_

* Shuang Yang, Bo Yang: **Enhanced Network Embedding with Text Information** `Paper <https://ieeexplore.ieee.org/document/8545577>`_, `Code <https://github.com/benedekrozemberczki/TENE>`_

* Lizi Liao, Xiangnan He, Hanwang Zhang, Tat-Seng Chua: **Attributed Social Network Embedding** `Paper <https://arxiv.org/abs/1705.04969>`_, `Code <https://github.com/benedekrozemberczki/ASNE>`_

* Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, Edward Y. Chang: **Network Representation Learning with Rich Text Information** `Paper <https://www.ijcai.org/Proceedings/15/Papers/299.pdf>`_, `Code <https://github.com/benedekrozemberczki/TADW>`_

* Sambaran Bandyopadhyay, Harsh Kara, Aswin Kannan, M N Murty: **Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information Networks** `Paper <https://arxiv.org/pdf/1804.05313.pdf>`_, `Code <https://github.com/benedekrozemberczki/FSCNMF>`_

Meta Node Embedding
--------------------

* Cheng Yang, Maosong Sun, Zhiyuan Liu, Cunchao Tu: **Fast Network Embedding Enhancement via High Order Proximity Approximation** `Paper <https://www.ijcai.org/Proceedings/2017/0544.pdf>`_

Whole Graph Embedding
--------------------

* Lili Wang, Chenghan Huang, Weicheng Ma, Xinyuan Cao, Soroush Vosoughi: **Graph Embedding via Diffusion-Wavelets-Based Node Feature Distribution Characterization** `Paper <https://arxiv.org/abs/2109.07016>`_

* Benedek Rozemberczki, Rik Sarkar: **Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models** `Paper <https://arxiv.org/abs/2005.07959>`_, `Code <https://github.com/benedekrozemberczki/FEATHER>`_

* Chen Cai, Yusu Wang: **A Simple Yet Effective Baseline for Non-Attributed Graph Classification** `Paper <https://arxiv.org/abs/1811.03508>`_, `Code <https://github.com/Chen-Cai-OSU/LDP/>`_

* Alexis Galland, Marc Lelarge: **Invariant Embedding for Graph Classification** `Paper <https://graphreason.github.io/papers/16.pdf>`_, `Code <https://github.com/agalland/invariant_embedding>`_

* Feng Gao, Guy Wolf, Matthew Hirn: **Geometric Scattering for Graph Data Analysis** `Paper <http://proceedings.mlr.press/v97/gao19e.html>`_

* Hong Chen, Hisashi Koga: **GL2Vec: Graph Embedding Enriched by Line Graphs with Edge Features** `Paper <https://link.springer.com/chapter/10.1007/978-3-030-36718-3_1>`_

* Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alex Bronstein, Emmanuel Müller: **NetLSD: Hearing the Shape of a Graph** `Paper <https://arxiv.org/abs/1805.10712>`_, `Video <https://www.youtube.com/watch?v=aiPOa1NTgvM>`_

* Nathan de Lara, Edouard Pineau: **A Simple Baseline Algorithm for Graph Classification** `Paper <https://arxiv.org/abs/1810.09155>`_

* Saurabh Verma, Zhi-Li Zhang: **Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs** `Paper <https://papers.nips.cc/paper/6614-hunt-for-the-unique-stable-sparse-and-fast-feature-learning-on-graphs>`_

* Annamalai Narayanan, Mahinthan Chandramohan, Rajasekar Venkatesan, Lihui Chen, Yang Liu, Shantanu Jaiswal: **graph2vec: Learning Distributed Representations of Graphs** `Paper <https://arxiv.org/abs/1707.05005>`_, `Code <https://github.com/benedekrozemberczki/graph2vec>`_
