Fetching the paper…
Reading the bibliography…
Structural roles define sets of structurally similar nodes that are more similar to nodes inside the set than outside, whereas communities define sets of nodes with more connections inside the set than outside.
Homophily-heterophily: Relational concepts for communication research
Everett M Rogers and Dilip K Bhowmik. 1970 · 1970
Earlier work this paper cites.
Structural equivalence of individuals in social networks†
F. Lorrain and H.C. White. 1971 · 1971
Earlier work this paper cites.
Local structure in social networks
Paul W Holland and Samuel Leinhardt. 1976 · 1976
Earlier work this paper cites.
Structural equivalence: Meaning and definition, computation and application
Lee Douglas Sailer. 1978 · 1978
Earlier work this paper cites.
An exponential family of probability distributions for directed graphs
P.W. Holland and S. Leinhardt. 1981 · 1981
Earlier work this paper cites.
Graph and semigroup homomorphisms on networks of relations
D.R. White and K.P. Reitz. 1983 · 1983
Earlier work this paper cites.
Role colouring a graph
M.G. Everett and S. Borgatti. 1991 · 1991
Earlier work this paper cites.
Notions of position in social network analysis
S.P. Borgatti and M.G. Everett. 1992 · 1992
Earlier work this paper cites.
Regular equivalence: General theory
M.G. Everett and S.P. Borgatti. 1994 · 1994
Earlier work this paper cites.
Spectral graph theory
Fan RK Chung. 1997 · 1997
Earlier work this paper cites.
Inferring web communities from link topology. In HyperText
David Gibson, Jon Kleinberg, and Prabhakar Raghavan. 1998 · 1998
Earlier work this paper cites.
Relations, residuals, regular interiors, and relative regular equivalence
J.P. Boyd and M.G. Everett. 1999 · 1999
Earlier work this paper cites.
Graph-theoretic techniques for macromolecular docking
Eleanor J. Gardiner, Peter Willett, and Peter J. Artymiuk. 2000 · 2000
Earlier work this paper cites.
Iterative classification in relational data. In AAAI SRL Workshop
Jennifer Neville and David Jensen. 2000 · 2000
Earlier work this paper cites.
Graph clustering by flow simulation
Stijn Marinus Van Dongen. 2000 · 2000
Earlier work this paper cites.
A random walks view of spectral segmentation
Marina Meila and Jianbo Shi. 2001b · 2001
Earlier work this paper cites.
Diffusion kernels on graphs and other discrete input spaces. In Machine Learning
R.I. Kondor and J. Lafferty. 2002 · 2002
Earlier work this paper cites.
Network motifs: simple building blocks of complex networks
Ron Milo, Shai Shen-Orr, Shalev Itzkovitz, Nadav Kashtan, Dmitri Chklovskii, and Uri Alon. 2002 · 2002
Earlier work this paper cites.
On spectral clustering: Analysis and an algorithm. In NIPS
Andrew Y Ng, Michael I Jordan, and Yair Weiss. 2002 · 2002
Earlier work this paper cites.
Recommender Systems for Large-Scale E-Commerce: Scalable Neighborhood Formation Using Clustering. In Proceedings of the 5th International Conference on Computer and Information Technology (ICCIT)
Badrul M. Sarwar, George Karypis, Joseph Konstan, and John Reidl. 2002 · 2002
Earlier work this paper cites.
Clustering approach for hybrid recommender system. In Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003)
Qing Li and Byeong Man Kim. 2003 · 2003
Earlier work this paper cites.
Defining and measuring trophic role similarity in food webs using regular equivalence
J.J. Luczkovich, S.P. Borgatti, J.C. Johnson, and M.G. Everett. 2003 · 2003
Earlier work this paper cites.
Eigenspace-based anomaly detection in computer systems. In KDD
Tsuyoshi Idé and Hisashi Kashima. 2004 · 2004
Earlier work this paper cites.
On clusterings: Good, bad and spectral
Ravi Kannan, Santosh Vempala, and Adrian Vetta. 2004 · 2004
Earlier work this paper cites.
Autocorrelation and Relational Learning: Challenges and Opportunities. In Proceedings of the Workshop on Statistical Relational Learning
J. Neville, O. Şimşek, and D. Jensen. 2004 · 2004
Earlier work this paper cites.
Fast algorithm for detecting community structure in networks
M.E.J. Newman. 2004 · 2004
Earlier work this paper cites.
Finding and evaluating community structure in networks
M.E.J. Newman and M. Girvan. 2004 · 2004
Earlier work this paper cites.
Models and methods in social network analysis
Peter J Carrington, John Scott, and Stanley Wasserman. 2005 · 2005
Earlier work this paper cites.
Consistent Network Alignment via Proximity-Preserving Node Embedding. In arXiv:2005.04725
Xiyuan Chen, Mark Heimann, Fatemeh Vahedian, and Danai Koutra. 2020b · 2005
Earlier work this paper cites.
Leveraging relational autocorrelation with latent group models. In ICDM
Jennifer Neville and David Jensen. 2005 · 2005
Earlier work this paper cites.
Local clustering of large graphs by approximate Fiedler vectors. In International Workshop on Experimental and Efficient Algorithms
Pekka Orponen and Satu Elisa Schaeffer. 2005 · 2005
Earlier work this paper cites.
Ask-GraphView: A large scale graph visualization system
James Abello, Frank Van Ham, and Neeraj Krishnan. 2006 · 2006
Earlier work this paper cites.
Local graph partitioning using pagerank vectors. In FOCS
Reid Andersen, Fan Chung, and Kevin Lang. 2006 · 2006
Earlier work this paper cites.
Group formation in large social networks: Membership, growth, and evolution. In Proceeding of the 12th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining
L. Backstrom, D. Huttenlocher, J. Kleinberg, and X. Lan. 2006 · 2006
Earlier work this paper cites.
Evolutionary clustering. In Proceeding of the 12th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining
D. Chakrabarti, R. Kumar, and A. Tomkins. 2006 · 2006
Earlier work this paper cites.
Pairwise alignment of protein interaction networks
Mehmet Koyutürk, Yohan Kim, Umut Topkara, Shankar Subramaniam, Wojciech Szpankowski, and Ananth Grama. 2006 · 2006
Earlier work this paper cites.
Combining Collective Classification and Link Prediction. In ICDM Workshops
Mustafa Bilgic, Galileo Mark Namata, and Lise Getoor. 2007 · 2007
Earlier work this paper cites.
Biological network comparison using graphlet degree distribution
Nataša Pržulj. 2007 · 2007
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.
Graph clustering
Satu Elisa Schaeffer. 2007 · 2007
Earlier work this paper cites.
Graphscope: Parameter-free mining of large time-evolving graphs. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
J. Sun, C. Faloutsos, S. Papadimitriou, and P.S. Yu. 2007 · 2007
Earlier work this paper cites.
Using Ghost Edges for Classification in Sparsely Labeled Networks. In KDD
B. Gallagher, H. Tong, T. Eliassi-Rad, and C. Faloutsos. 2008 · 2008
Earlier work this paper cites.
Visual analytics: Definition, process, and challenges
Daniel Keim, Gennady Andrienko, Jean-Daniel Fekete, Carsten Görg, Jörn Kohlhammer, and Guy Melançon. 2008 · 2008
Earlier work this paper cites.
Ranking Links on the Web: Search and Surf Engines
Jean-Louis Lassez, Ryan A. Rossi, and Kumar Jeev. 2008 · 2008
Earlier work this paper cites.
Locally computable approximations for spectral clustering and absorption times of random walks
Pekka Orponen, Satu Elisa Schaeffer, and Vanesa Ávalos Gaytán. 2008 · 2008
Earlier work this paper cites.
Collective classification in network data
P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, and T. Eliassi-Rad. 2008 · 2008
Earlier work this paper cites.
Anomaly detection: A survey
Varun Chandola, Arindam Banerjee, and Vipin Kumar. 2009 · 2009
Earlier work this paper cites.
Dynamic mixed membership blockmodel for evolving networks. In Proceedings of the 26th Annual International Conference on Machine Learning
Wenjie Fu, Le Song, and Eric P Xing. 2009 · 2009
Earlier work this paper cites.
Statistical analysis of network data with R
Eric D Kolaczyk and Gábor Csárdi. 2009 · 2009
Earlier work this paper cites.
Cautious collective classification
Luke K McDowell, Kalyan Moy Gupta, and David W Aha. 2009 · 2009
Earlier work this paper cites.
Detecting novel discrepancies in communication networks. In ICDM
James Abello, Tina Eliassi-Rad, and Nishchal Devanur. 2010 · 2010
Earlier work this paper cites.
Active learning for networked data
M. Bilgic, L. Mihalkova, and L. Getoor. 2010 · 2010
Earlier work this paper cites.
Dense subgraph extraction with application to community detection
Jie Chen and Yousef Saad. 2010 · 2010
Earlier work this paper cites.
A puzzle concerning triads in social networks: Graph constraints and the triad census
Katherine Faust. 2010 · 2010
Earlier work this paper cites.
Community detection in graphs
S. Fortunato. 2010a · 2010
Earlier work this paper cites.
Community detection in graphs
Santo Fortunato. 2010b · 2010
Earlier work this paper cites.
HCDF: A hybrid community discovery framework. In SDM
Keith Henderson, Tina Eliassi-Rad, Spiros Papadimitriou, and Christos Faloutsos. 2010 · 2010
Cited alongside, same era.
Randomization Tests for Distinguishing Social Influence and Homophily Effects. In WWW
Timothy La Fond and Jennifer Neville. 2010 · 2010
Cited alongside, same era.
Graph kernels
S Vichy N Vishwanathan, Nicol N Schraudolph, Risi Kondor, and Karsten M Borgwardt. 2010 · 2010
Cited alongside, same era.
A survey of link prediction in social networks
Mohammad Al Hasan and Mohammed J Zaki. 2011 · 2011
Cited alongside, same era.
Is there a best quality metric for graph clusters?. In ECML/PKDD
Hélio Almeida, Dorgival Guedes, Wagner Meira, and Mohammed J Zaki. 2011 · 2011
Cited alongside, same era.
Graph algorithms in the language of linear algebra
Jeremy Kepner and John Gilbert. 2011 · 2011
Cited alongside, same era.
Carina: Interactive million-node graph visualization using web browser technologies. In WWW
Dezhi Fang, Matthew Keezer, Jacob Williams, Kshitij Kulkarni, Robert Pienta, and Duen Horng Chau. 2017 · 2017
Later among the works it cites.
Inductive Representation Learning on Large Graphs
William Hamilton, Rex Ying, and Jure Leskovec. 2017 · 2017
Later among the works it cites.
On Generalizing Neural Node Embedding Methods to Multi-Network Problems. In ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, MLG Workshop
Mark Heimann and Danai Koutra. 2017 · 2017
Later among the works it cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2017 · 2017
Later among the works it cites.
Individual and Collective Graph Mining: Principles, Algorithms, and Applications
Danai Koutra and Christos Faloutsos. 2017 · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Temporal motifs in time-dependent networks
Lauri Kovanen, Márton Karsai, Kimmo Kaski, János Kertész, and Jari Saramäki. 2011 · 2011
Cited alongside, same era.
Leveraging social media networks for classification
Lei Tang and Huan Liu. 2011 · 2011
Cited alongside, same era.
Visual analysis of large graphs: state-of-the-art and future research challenges. In Computer graphics forum
Tatiana Von Landesberger, Arjan Kuijper, Tobias Schreck, Jörn Kohlhammer, Jarke J van Wijk, J-D Fekete, and Dieter W Fellner. 2011 · 2011
Cited alongside, same era.
Community-based anomaly detection in evolutionary networks
Zhengzhang Chen, William Hendrix, and Nagiza F Samatova. 2012 · 2012
Cited alongside, same era.
Does a daily deal promotion signal a distressed business? an empirical investigation of small business survival
Ayman Farahat, Nesreen Ahmed, and Uptal Dholakia. 2012 · 2012
Cited alongside, same era.
Graph embedding for pattern analysis
Yun Fu and Yunqian Ma. 2012 · 2012
Cited alongside, same era.
Prune: Preserving proximity and global ranking for network embedding. In Advances in neural information processing systems
Yi-An Lai, Chin-Chi Hsu, Wen Hao Chen, Mi-Yen Yeh, and Shou-De Lin. 2017 · 2017
Later among the works it cites.
graph2vec: Learning distributed representations of graphs
Annamalai Narayanan, Mahinthan Chandramohan, Rajasekar Venkatesan, Lihui Chen, Yang Liu, and Shantanu Jaiswal. 2017 · 2017
Later among the works it cites.
Matching Node Embeddings for Graph Similarity. In Proceedings of the 31st AAAI Conference on Artificial Intelligence
Giannis Nikolentzos, Polykarpos Meladianos, and Michalis Vazirgiannis. 2017 · 2017
Later among the works it cites.
Graph-based semi-supervised learning for relational networks. In SDM
Leto Peel. 2017 · 2017
Later among the works it cites.
Struc2Vec: Learning Node Representations from Structural Identity. In KDD
Leonardo F.R. Ribeiro, Pedro H.P. Saverese, and Daniel R. Figueiredo. 2017 · 2017
Later among the works it cites.
Deep Feature Learning for Graphs. In arXiv:1704.08829
Ryan A. Rossi, Rong Zhou, and Nesreen K. Ahmed. 2017 · 2017
Later among the works it cites.
On Summarizing Large-Scale Dynamic Graphs
Neil Shah, Danai Koutra, Lisa Jin, Tianmin Zou, Brian Gallagher, and Christos Faloutsos. 2017 · 2017
Later among the works it cites.
Community Preserving Network Embedding. In AAAI
Xiao Wang, Peng Cui, Jing Wang, Jian Pei, Wenwu Zhu, and Shiqiang Yang. 2017 · 2017
Later among the works it cites.
Sub2vec: Feature learning for subgraphs. In PAKDD
Bijaya Adhikari, Yao Zhang, Naren Ramakrishnan, and B Aditya Prakash. 2018 · 2018
Later among the works it cites.
Learning Role-based Graph Embeddings. In IJCAI
Nesreen K. Ahmed, Ryan A. Rossi, Rong Zhou, John Boaz Lee, Xiangnan Kong, Theodore L. Willke, and Hoda Eldardiry. 2018 · 2018
Later among the works it cites.
Learning Graph Representation: A Comparative Study. In International Arab Conference on Information Technology (ACIT)
Wael Al Etaiwi and Arafat Awajan. 2018 · 2018
Later among the works it cites.
Analyzing social networks
Stephen P Borgatti, Martin G Everett, and Jeffrey C Johnson. 2018 · 2018
Later among the works it cites.
A comprehensive survey of graph embedding: Problems, techniques, and applications
Hongyun Cai, Vincent W Zheng, and Kevin Chen-Chuan Chang. 2018 · 2018
Later among the works it cites.
Supervised Community Detection with Line Graph Neural Networks
Zhengdao Chen, Lisha Li, and Joan Bruna. 2018 · 2018
Later among the works it cites.
Designing Size Consistent Statistics for Accurate Anomaly Detection in Dynamic Networks
Timothy La Fond, Jennifer Neville, and Brian Gallagher. 2018 · 2018
Later among the works it cites.
Graph embedding techniques, applications, and performance: A survey
Palash Goyal and Emilio Ferrara. 2018 · 2018
Later among the works it cites.
Regal: Representation learning-based graph alignment. In CIKM
Mark Heimann, Haoming Shen, Tara Safavi, and Danai Koutra. 2018 · 2018
Later among the works it cites.
Graph summarization methods and applications: A survey
Yike Liu, Tara Safavi, Abhilash Dighe, and Danai Koutra. 2018a · 2018
Later among the works it cites.
Continuous-time dynamic network embeddings. In WWW
Giang Hoang Nguyen, John Boaz Lee, Ryan A Rossi, Nesreen K Ahmed, Eunyee Koh, and Sungchul Kim. 2018 · 2018
Later among the works it cites.
Network embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec. In WSDM
Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, and Jie Tang. 2018 · 2018
Later among the works it cites.
Interactive Visual Graph Mining and Learning
Ryan A. Rossi, Nesreen K. Ahmed, Hoda Eldardiry, and Rong Zhou. 2018a · 2018
Later among the works it cites.
HONE: Higher-Order Network Embeddings
Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh, Sungchul Kim, Anup Rao, and Yasin Abbasi-Yadkori. 2018b · 2018
Later among the works it cites.
Relational Similarity Machines (RSM): A Similarity-based Learning Framework for Graphs. In IEEE BigData
Ryan A. Rossi, Rong Zhou, Nesreen K. Ahmed, and Hoda Eldardiry. 2018 · 2018
Later among the works it cites.
Few-Shot Learning with Graph Neural Networks. In ICLR
Victor Garcia Satorras and Joan Bruna Estrach. 2018 · 2018
Later among the works it cites.
Overlapping Community Detection with Graph Neural Networks
Oleksandr Shchur and Stephan Günnemann. 2018 · 2018
Later among the works it cites.
Learning graph representations with recurrent neural network autoencoders. In Proc. KDD Deep Learn. Day
Aynaz Taheri, Kevin Gimpel, and Tanya Berger-Wolf. 2018 · 2018
Later among the works it cites.
Graph Attention Networks. In ICLR
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
Later among the works it cites.
Feature Propagation on Graph: A New Perspective to Graph Representation Learning
Biao Xiang, Ziqi Liu, Jun Zhou, and Xiaolong Li. 2018 · 2018
Later among the works it cites.
Hierarchical graph representation learning with differentiable pooling. In Proceedings of the 31st Annual Conference on Neural Information Processing Systems
Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec. 2018 · 2018
Later among the works it cites.
Network representation learning: A survey
Daokun Zhang, Jie Yin, Xingquan Zhu, and Chengqi Zhang. 2018 · 2018
Later among the works it cites.
Embedding Both Finite and Infinite Communities on Graphs
Sandro Cavallari, Erik Cambria, Hongyun Cai, Kevin Chen-Chuan Chang, and Vincent W Zheng. 2019 · 2019
Closest in time.
Hyperbolic graph convolutional neural networks. In Advances in Neural Information Processing Systems
Ines Chami, Zhitao Ying, Christopher Ré, and Jure Leskovec. 2019 · 2019
Closest in time.
Distribution of Node Embeddings as Multiresolution Features for Graphs. In IEEE International Conference on Data Mining
Mark Heimann, Tara Safavi, and Danai Koutra. 2019 · 2019
Closest in time.
Graph recurrent networks with attributed random walks. In KDD
Xiao Huang, Qingquan Song, Yuening Li, and Xia Hu. 2019 · 2019
Closest in time.
Micro-and macro-level churn analysis of large-scale mobile games
Xi Liu, Muhe Xie, Xidao Wen, Rui Chen, Yong Ge, Nick Duffield, and Na Wang. 2019 · 2019
Closest in time.
Deep graph similarity learning for brain data analysis
Guixiang Ma, Nesreen K Ahmed, Theodore L Willke, Dipanjan Sengupta, Michael W Cole, Nicholas B Turk-Browne, and Philip S Yu. 2019b · 2019
Closest in time.
Deep Graph Similarity Learning: A Survey
Guixiang Ma, Nesreen K Ahmed, Theodore L Willke, and Philip S Yu. 2019a · 2019
Closest in time.
Learning Structural Node Representations using Graph Kernels
Giannis Nikolentzos and Michalis Vazirgiannis. 2019 · 2019
Closest in time.
Multi-scale Attributed Node Embedding
Benedek Rozemberczki, Carl Allen, and Rik Sarkar. 2019 · 2019
Closest in time.
On the Equivalence between Node Embeddings and Structural Graph Representations
Balasubramaniam Srinivasan and Bruno Ribeiro. 2019 · 2019
Closest in time.
A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S Yu. 2019a · 2019
Closest in time.
Graph wavenet for deep spatial-temporal graph modeling
Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019b · 2019
Closest in time.
RiWalk: Fast Structural Node Embedding via Role Identification. In IEEE 19th International Conference on Data Mining
Ma Xuewei, Geng Qin, Zhiyang Qiu, Mingxin Zheng, and Zhe Wang. 2019 · 2019
Closest in time.
GroupINN: Grouping-based Interpretable Neural Network for Classification of Limited, Noisy Brain Data. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Yujun Yan, Jiong Zhu, Marlena Duda, Eric Solarz, Chandra Sripada, and Danai Koutra. 2019 · 2019
Closest in time.
GNNexplainer: Generating explanations for graph neural networks. In Advances in Neural Information Processing Systems
Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. 2019 · 2019
Closest in time.
G2SAT: Learning to Generate SAT Formulas. In Advances in Neural Information Processing Systems
Jiaxuan You, Haoze Wu, Clark Barrett, Raghuram Ramanujan, and Jure Leskovec. 2019 · 2019
Closest in time.
Attribute-Aware Graph Recurrent Networks for Scholarly Friend Recommendation Based on Internet of Scholars in Scholarly Big Data
Chunyou Zhang, Xiaoqiang Wu, Wei Yan, Lukun Wang, and Lei Zhang. 2019 · 2019
Closest in time.
Deep Parametric Model for Discovering Group-cohesive Functional Brain Regions. In SIAM International Conference on Data Mining (SDM)
John Boaz Lee, Xiangnan Kong, Constance M Moore, and Nesreen K Ahmed. 2020 · 2020
Closest in time.
Toward Activity Discovery in the Personal Web. In The Thirteenth ACM International Conference on Web Search and Data Mining
Tara Safavi, Adam Fourney, Robert Sim, Marcin Juraszek, Shane Williams, Ned Friend, Danai Koutra, and Paul N. Bennett. 2020 · 2020
Closest in time.
Learning convolutional neural networks for graphs. In ICML
Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov. 2016 · 2023
Closest in time.