Multiscale Evolutionary Perturbation Attack on Community Detection

Community detection, aiming to group nodes based on their connections, plays an important role in network analysis, since communities, treated as meta-nodes, allow us to create a large-scale map of a network to simplify its analysis. However, for privacy reasons, we may want to prevent communities from being discovered in certain cases, leading to the topics on community deception. In this paper, we formalize this community detection attack problem in three scales, including global attack (macroscale), target community attack (mesoscale) and target node attack (microscale). We treat this as an optimization problem and further propose a novel Evolutionary Perturbation Attack (EPA) method, where we generate adversarial networks to realize the community detection attack. Numerical experiments validate that our EPA can successfully attack network community algorithms in all three scales, i.e., hide target nodes or communities and further disturb the community structure of the whole network by only changing a small fraction of links. By comparison, our EPA behaves better than a number of baseline attack methods on six synthetic networks and three real-world networks. More interestingly, although our EPA is based on the louvain algorithm, it is also effective on attacking other community detection algorithms, validating its good transferability.

[1]  Martin Rosvall,et al.  Maps of random walks on complex networks reveal community structure , 2007, Proceedings of the National Academy of Sciences.

[2]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  A. Clauset Finding local community structure in networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  Qi Xuan,et al.  Social Synchrony on Complex Networks , 2018, IEEE Transactions on Cybernetics.

[5]  Mark A. Musen,et al.  PhLeGrA: Graph Analytics in Pharmacology over the Web of Life Sciences Linked Open Data , 2017, WWW.

[6]  Mark E. J. Newman,et al.  Community detection in networks: Modularity optimization and maximum likelihood are equivalent , 2016, ArXiv.

[7]  T. Murata,et al.  Advanced modularity-specialized label propagation algorithm for detecting communities in networks , 2009, 0910.1154.

[8]  Qi Xuan,et al.  Fast Gradient Attack on Network Embedding , 2018, ArXiv.

[9]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  Valeria Fionda,et al.  Community Deception or: How to Stop Fearing Community Detection Algorithms , 2018, IEEE Transactions on Knowledge and Data Engineering.

[11]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Honglei Zhang,et al.  Adversarial Attack on Community Detection by Hiding Individuals , 2020, WWW.

[13]  Xiaojiao Chen,et al.  Time Synchronization Network for EAST Poloidal Field Power Supply Control System Based on IEEE 1588 , 2018, IEEE Transactions on Plasma Science.

[14]  Wei Chen,et al.  Interplay between Social Influence and Network Centrality: A Comparative Study on Shapley Centrality and Single-Node-Influence Centrality , 2016, WWW.

[15]  Xin Liu,et al.  Evaluation of Community Detection Methods , 2018, IEEE Transactions on Knowledge and Data Engineering.

[16]  Stephan Günnemann,et al.  Adversarial Attacks on Node Embeddings via Graph Poisoning , 2018, ICML.

[17]  Matthieu Latapy,et al.  Computing Communities in Large Networks Using Random Walks , 2004, J. Graph Algorithms Appl..

[18]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[19]  Amedeo Caflisch,et al.  Multistep greedy algorithm identifies community structure in real-world and computer-generated networks , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  M. Barber,et al.  Detecting network communities by propagating labels under constraints. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  R. Guimerà,et al.  Functional cartography of complex metabolic networks , 2005, Nature.

[22]  Shishir Nagaraja,et al.  The Impact of Unlinkability on Adversarial Community Detection: Effects and Countermeasures , 2010, Privacy Enhancing Technologies.

[23]  J. Reichardt,et al.  Statistical mechanics of community detection. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[24]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[25]  M. Newman,et al.  Finding community structure in networks using the eigenvectors of matrices. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[26]  Yi-Chang Chiu,et al.  A Network Partitioning Algorithmic Approach for Macroscopic Fundamental Diagram-Based Hierarchical Traffic Network Management , 2018, IEEE Transactions on Intelligent Transportation Systems.

[27]  Yanjun Li,et al.  A Framework to Model the Topological Structure of Supply Networks , 2011, IEEE Transactions on Automation Science and Engineering.

[28]  Francesco Folino,et al.  An Evolutionary Multiobjective Approach for Community Discovery in Dynamic Networks , 2014, IEEE Transactions on Knowledge and Data Engineering.

[29]  Lada A. Adamic,et al.  The political blogosphere and the 2004 U.S. election: divided they blog , 2005, LinkKDD '05.

[30]  Talal Rahwan,et al.  Hiding individuals and communities in a social network , 2016, Nature Human Behaviour.

[31]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[32]  Christos Faloutsos,et al.  Graph evolution: Densification and shrinking diameters , 2006, TKDD.

[33]  Réka Albert,et al.  Near linear time algorithm to detect community structures in large-scale networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[34]  Qi Xuan,et al.  GA-Based Q-Attack on Community Detection , 2018, IEEE Transactions on Computational Social Systems.

[35]  Chao Wang,et al.  Threshold Random Walkers for Community Structure Detection in Complex Networks , 2013, J. Softw..

[36]  Ulrike von Luxburg,et al.  Multi-agent Random Walks for Local Clustering on Graphs , 2010, 2010 IEEE International Conference on Data Mining.

[37]  Qi Xuan,et al.  Target Defense Against Link-Prediction-Based Attacks via Evolutionary Perturbations , 2018, IEEE Transactions on Knowledge and Data Engineering.

[38]  Stephan Günnemann,et al.  Adversarial Attacks on Neural Networks for Graph Data , 2018, KDD.

[39]  Michalis Vazirgiannis,et al.  Clustering and Community Detection in Directed Networks: A Survey , 2013, ArXiv.

[40]  F. Radicchi,et al.  Benchmark graphs for testing community detection algorithms. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[41]  Qi Xuan,et al.  Link Weight Prediction Using Supervised Learning Methods and Its Application to Yelp Layered Network , 2018, IEEE Transactions on Knowledge and Data Engineering.

[42]  Cheng Ding,et al.  TeamGen: An Interactive Team Formation System Based on Professional Social Network , 2017, WWW.

[43]  M. Newman Community detection in networks: Modularity optimization and maximum likelihood are equivalent , 2016, Physical review. E.

[44]  Ali Ghorbanian,et al.  A Genetic Algorithm for Modularity Density Optimization in Community Detection - TI Journals , 2015 .

[45]  Yanhua Li,et al.  Planning Bike Lanes based on Sharing-Bikes' Trajectories , 2017, KDD.

[46]  Dongqing Zhou,et al.  A Neighborhood-Impact Based Community Detection Algorithm via Discrete PSO , 2016 .

[47]  Valdis E. Krebs,et al.  Mapping Networks of Terrorist Cells , 2001 .