Partitioning sparse matrices with eigenvectors of graphs
SIAM Journal on Matrix Analysis and Applications
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Introduction to Algorithms
On clusterings: Good, bad and spectral
Journal of the ACM (JACM)
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
IEEE Transactions on Knowledge and Data Engineering
GA-Net: A Genetic Algorithm for Community Detection in Social Networks
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
A Multi-objective Genetic Algorithm for Community Detection in Networks
ICTAI '09 Proceedings of the 2009 21st IEEE International Conference on Tools with Artificial Intelligence
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
An Evolutionary Approach to Multiobjective Clustering
IEEE Transactions on Evolutionary Computation
Identification of multi-resolution network structures with multi-objective immune algorithm
Applied Soft Computing
Using coalitional games to detect communities in social networks
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
A game theory based approach for community detection in social networks
BNCOD'13 Proceedings of the 29th British National conference on Big Data
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Community detection in social network analysis is usually considered as a single objective optimization problem, in which different heuristics or approximate algorithms are employed to optimize a objective function that capture the notion of community. Due to the inadequacy of those single-objective solutions, this paper first formulates a multi-objective framework for community detection and proposes a multi-objective evolutionary algorithm for finding efficient solutions under the framework. After analyzing and comparing a variety of objective functions that have been used or can potentially be used for community detection, this paper exploits the concept of correlation between objective which charcterizes the relationship between any two objective functions. Through extensive experiments on both artifical and real networks, this paper demonstrates that a combination of two negatively correlated objectives under the multi-objective framework usually leads to remarkably better performance compared with either of the orignal single objectives, including even many popular algorithms..