A method for local community detection by finding maximal-degree nodes - Qiong Chen; Ting-Ting Wu (2010 International Conference on Machine Learning and Cybernetics, 2010)
Since obtaining complete information from large network is unrealistic nowadays, there is a growing emphasis on local community detection. However, some existing approaches are sensitive to the starting node's position, such as the communities discovered from nodes in boundary always have lower recall rate than those from nodes in the core. Thus, in this paper, we propose a new method to detect the local community for a given node. To start, we find the local maximal-degree nodes which associate with the given node, then find the enclosing communities by calculating the local modularity of community from the local maximal-degree node, finally we optimize the communities' structure and get the local community for the given node. Experiment results show that our method is quite effective and flexible, especially when the given node is in the community's boundary.