Abstract
Various networks exist in the world today including biological, social, information, and communication networks with the Internet as the largest network of all. One salient structural feature of these networks is the formation of groups or communities of vertices that tend to be more connected to each other within the same group than to those outside. Therefore, the detection of these communities is a topic of great interest and importance in many applications and different algorithms including label propagation have been developed for such purpose. Speaker-listener label propagation algorithm (SLPA) enjoys almost linear time complexity, so desirable in dealing with large networks. As an extension of SLPA, this study presented a novel weighted label propagation algorithm (WLPA), which was tested on four real world social networks with known community structures including the famous Zachary's karate club network. Wilcoxon tests on the communities found in the karate club network by WLPA demonstrated an improved statistical significance over SLPA. Withthehelp of Wilcoxon tests again, we were able to determine the best possible formation of two communities in this network relative to the ground truth partition, which could be used as a new benchmark for assessing community detection algorithms. Finally WLPA predicted better communities than SLPA in two of the three additional real social networks, when compared to the ground truth.
Highlights
Any collection of interacting entities could be described as a network, in which each entity is a vertex or node and any pair of interacting vertices is connected with an edge
As an extension of Speaker-listener label propagation algorithm (SLPA), this study presented a novel weighted label propagation algorithm (WLPA), which was tested on four real world social networks with known community structures including the famous Zachary's karate club network
The aim of this study was to propose a novel weighted label propagation algorithm (WLPA), as an extension of SLPA, by introducing a similarity between any two vertices in a network based on the labels each vertex has received during label propagation and using this similarity as a weight of the edge between the two vertices in the iteration of label propagation
Summary
Any collection of interacting entities could be described as a network, in which each entity is a vertex or node and any pair of interacting vertices is connected with an edge. A network can be considered as a graph in mathematics, thereby opening a door for introducing many mature techniques into study of networks. In a random graph, the distribution of edges among the vertices is highly homogeneous. On the other hand, are often not random at all. There is an observed tendency for vertices to be gathered into groups or clusters. The vertices within a group are related in some way, and a vertex presents in more than one group may be an indicator of its special role in this network
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