Design of Japanese Tree Frog Algorithm for Community Finding Problems

Community Finding Problems (CFPs) have become very popular in the last years, due to the high number of users that connect everyday to Social Networks (SNs). The goal of these problems is to group the users that compose the SN in several communities, or circles, in such a way similar users belong to the same community, whereas different users are assigned to different communities. Due to the high complexity of this problem, it is common that researchers use heuristic algorithms to perform this task in a reasonable computational time. This paper is focused on the applicability of a novel bio-inspired algorithm to solve CFPs. The selected algorithm is based on the real behaviour of the Japanese Tree Frog, that has been successfully used to colour maps and extract the Maximal Independent Set of a graph.

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

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

[3]  Christian Blum,et al.  Distributed graph coloring: an approach based on the calling behavior of Japanese tree frogs , 2010, Swarm Intelligence.

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

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

[6]  K. Wells.  The social behaviour of anuran amphibians , 1977, Animal Behaviour.

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

[8]  Jure Leskovec,et al.  Overlapping community detection at scale: a nonnegative matrix factorization approach , 2013, WSDM.

[9]  Edoardo M. Airoldi,et al.  Mixed Membership Stochastic Blockmodels , 2007, NIPS.

[10]  Ikkyu Aihara,et al.  Modeling synchronized calling behavior of Japanese tree frogs. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  Boleslaw K. Szymanski,et al.  Overlapping community detection in networks: The state-of-the-art and comparative study , 2011, CSUR.

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

[13]  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.

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

[15]  David M. Blei,et al.  Hierarchical relational models for document networks , 2009, 0909.4331.

[16]  Maoguo Gong,et al.  Greedy discrete particle swarm optimization for large-scale social network clustering , 2015, Inf. Sci..

[17]  Lada A. Adamic,et al.  The role of social networks in information diffusion , 2012, WWW.

[18]  Clara Pizzuti,et al.  GA-Net: A Genetic Algorithm for Community Detection in Social Networks , 2008, PPSN.

[19]  M. Murata,et al.  Frog call-inspired self-organizing anti-phase synchronization for wireless sensor networks , 2009, 2009 2nd International Workshop on Nonlinear Dynamics and Synchronization.