Colloquium Archive

Many network systems of interest are rising from real world networks, e.g. social networks and biological networks. One typical issue considered by researchers is how to find the community structures in those networks. We proposed a community structure detection problem, which aims to analyze the relationships among the data via the network topology. We collect a series of unified definitions for community structures and formulate the community structure detection into a combinatorial optimization problem to identify as many communities as possible for a given network, and develop a heuristic algorithm based on greedy strategy. The experimental results on real networks show that the proposed algorithm is effective in terms of the number of valid communities and the modularity score.