Exploring the Importance of Negative Links through the European Parliament Social Graph

In this paper, we study the informative value of negative links in social networks, more specifically signed networks. We first process a selection of networks, generated from the 7th term (2009-2014), that were constructed by considering voting similarities between Members of the European Parliament. We propose a rework of Parallel Iterative Local Search; An algorithm to partition networks by means of solving the Correlation Clustering Problem, which takes in consideration the sign of the edges when generating selecting the communities for each node. Our rework reduces the termination time of the algorithm, and performs a faster exploration of the solution space, by means of limiting the number of iterations in local searches and also imposes a condition for a perturbation to be processed. Our approach reduced the termination required time to around 10% of the original approach, besides being able to reach better results when compared with a selection of community detection algorithms like Infomap and Walktrap, designed to process only positive links.

[1]  Rosa Figueiredo,et al.  Relevance of Negative Links in Graph Partitioning: A Case Study Using Votes from the European Parliament , 2015, 2015 Second European Network Intelligence Conference.

[2]  Maoguo Gong,et al.  Complex Network Clustering by Multiobjective Discrete Particle Swarm Optimization Based on Decomposition , 2014, IEEE Transactions on Evolutionary Computation.

[3]  Rosa Figueiredo,et al.  Mixed integer programming formulations for clustering problems related to structural balance , 2013, Soc. Networks.

[4]  Mahdi Jalili,et al.  Mesoscopic analysis of online social networks - The role of negative ties , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  P. Doreian,et al.  A partitioning approach to structural balance , 1996 .

[6]  M. Elsner,et al.  Bounding and Comparing Methods for Correlation Clustering Beyond ILP , 2009, ILP 2009.

[7]  F. Heider.  ATTITUDES AND COGNITIVE ORGANIZATION , 1977 .

[8]  Patrick Doreian,et al.  Partitioning signed social networks , 2009, Soc. Networks.

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

[10]  V. Traag,et al.  Community detection in networks with positive and negative links. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  Buzhou Tang,et al.  Overlapping community detection in networks with positive and negative links , 2014 .

[12]  Mason A. Porter,et al.  Community Structure in the United Nations General Assembly , 2010, ArXiv.

[13]  Peter Abell,et al.  Structural Balance: A Dynamic Perspective , 2009 .

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

[15]  Malik Magdon-Ismail,et al.  Communities and Balance in Signed Networks: A Spectral Approach , 2012, 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.

[16]  Ambuj K. Singh,et al.  Towards Community Discovery in Signed Collaborative Interaction Networks , 2010, 2010 IEEE International Conference on Data Mining Workshops.

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

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

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

[20]  Dayou Liu,et al.  A Heuristic Clustering Algorithm for Mining Communities in Signed Networks , 2007, Journal of Computer Science and Technology.

[21]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Patrick Doreian,et al.  Partitioning Signed Two-Mode Networks , 2009 .

[23]  James A. Davis.  Clustering and Structural Balance in Graphs , 1977 .

[24]  Avrim Blum,et al.  Correlation Clustering , 2004, Machine Learning.

[25]  Maoguo Gong,et al.  Discrete particle swarm optimization for identifying community structures in signed social networks , 2014, Neural Networks.

[26]  Lúcia Maria de A. Drummond,et al.  An ILS algorithm to evaluate structural balance in signed social networks , 2015, SAC.

[27]  Jiming Liu,et al.  Community Mining from Signed Social Networks , 2007, IEEE Transactions on Knowledge and Data Engineering.

[28]  Jing Liu,et al.  A comparative analysis of evolutionary and memetic algorithms for community detection from signed social networks , 2013, Soft Computing.

[29]  F. Harary,et al.  STRUCTURAL BALANCE: A GENERALIZATION OF HEIDER'S THEORY1 , 1977 .