On the analysis of the influence of the evaluation metric in community detection using GRASP

Community detection in social networks is becoming one of the key tasks in social network analysis, since it helps analyze groups of users with similar interests, detect radicalisms, or reduce the size of the data to be analyzed, among other applications. This paper presents a metaheuristic approach based on Greedy Randomized Adaptive Search Procedure methodology for detecting communities in social networks. The community detection is modeled as an optimization problem where the objective function to be optimized is the modularity of the network, a well known metric in community detection. The results obtained outperforms traditional methods of community detection as Edge Betweenness, Fast Greedy and Infomap over a set of real-life instances derived from Twitter.

[1]  Yuqing Zhai,et al.  An Improved Collaborative Filtering Recommendation Algorithm Based on Community Detection in Social Networks , 2015, GECCO.

[2]  Mauricio G. C. Resende,et al.  GRASP: Greedy Randomized Adaptive Search Procedures , 2014 .

[3]  Mariá Cristina Vasconcelos Nascimento,et al.  Community Detection by Consensus Genetic-based Algorithm for Directed Networks , 2016, KES.

[4]  Lillian Lee,et al.  Opinion Mining and Sentiment Analysis , 2008, Found. Trends Inf. Retr..

[5]  Jason J. Jung,et al.  ACO-based clustering for Ego Network analysis , 2017, Future Gener. Comput. Syst..

[6]  Antonio González-Pardo,et al.  RiskTrack: A New Approach for Risk Assessment on Radicalisation Based on Social Media Data , 2016, AfCAI.

[7]  Albert-László Barabási,et al.  Evolution of Networks: From Biological Nets to the Internet and WWW , 2004 .

[8]  David Camacho,et al.  Adaptive k-Means Algorithm for Overlapped Graph Clustering , 2012, Int. J. Neural Syst..

[9]  Mauricio G. C. Resende,et al.  A Greedy Randomized Adaptive Search Procedure for Maximum Independent Set , 1994, Oper. Res..

[10]  Christian Blum,et al.  FrogCOL and FrogMIS: new decentralized algorithms for finding large independent sets in graphs , 2015, Swarm Intelligence.

[11]  Julio César Hernández Castro,et al.  Detecting discussion communities on vaccination in twitter , 2017, Future Gener. Comput. Syst..

[12]  Gisela Bichler,et al.  Networks of Collaborating Criminals: Assessing the Structural Vulnerability of Drug Markets , 2011 .

[13]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  Haifeng Du,et al.  A genetic algorithm with local search strategy for improved detection of community structure , 2010, Complex..

[15]  Francisco J. Rodríguez,et al.  Optimizing network attacks by artificial bee colony , 2017, Inf. Sci..

[16]  Moustafa Ghanem,et al.  Multidimensional community detection in Twitter , 2013, 8th International Conference for Internet Technology and Secured Transactions (ICITST-2013).

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

[18]  Carl T. Bergstrom,et al.  The map equation , 2009, 0906.1405.

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

[20]  Antonio González-Pardo,et al.  Extracting Radicalisation Behavioural Patterns from Social Network Data , 2017, 2017 28th International Workshop on Database and Expert Systems Applications (DEXA).

[21]  Shintaro Okazaki,et al.  Combining social-based data mining techniques to extract collective trends from twitter , 2014 .

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

[23]  Mohammed J. Zaki,et al.  Is There a Best Quality Metric for Graph Clusters? , 2011, ECML/PKDD.

[24]  M. Resende,et al.  A probabilistic heuristic for a computationally difficult set covering problem , 1989 .

[25]  Djamal Benslimane,et al.  Measuring the Radicalisation Risk in Social Networks , 2017, IEEE Access.

[26]  Antonio González-Pardo,et al.  Statistical analysis of risk assessment factors and metrics to evaluate radicalisation in Twitter , 2017, Future Gener. Comput. Syst..

[27]  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.

[28]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[29]  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.

[30]  Jesús Sánchez-Oro,et al.  Iterated Greedy algorithm for performing community detection in social networks , 2018, Future Gener. Comput. Syst..

[31]  Javier Del Ser,et al.  Community detection in graphs based on surprise maximization using firefly heuristics , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[32]  Mariá Cristina Vasconcelos Nascimento,et al.  GA-LP: A genetic algorithm based on Label Propagation to detect communities in directed networks , 2017, Expert Syst. Appl..

[33]  Stephen G. Kobourov,et al.  Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale , 2016, PloS one.

[34]  Harith Alani,et al.  Understanding the Roots of Radicalisation on Twitter , 2018, WebSci.

[35]  D. Meadows-Klue.  The Tipping Point: How Little Things Can Make a Big Difference , 2004 .