Abstract
The problem of temporal community detection is discussed in this paper. Main existing methods are either structure-based or incremental analysis. The difficulty of the former is to select a suitable time window. The latter needs to know the initial structure of networks and the changing of networks should be stable. For most real data sets, these conditions hardly prevail. A streaming method called Temporal Label Walk (TLW) is proposed in this paper, where the aforementioned restrictions are eliminated. Modularity of the snapshots is used to evaluate our method. Experiments reveal the effectiveness of TLW on temporal community detection. Compared with other static methods in real data sets, our method keeps a higher modularity with the increase of window size.
Highlights
The concept of community defined by Newman and Girvan is widely accepted and used [1].Community detection is a significant task and of great value in practical applications
Some classic methods such as Kernighan–Lin [2] based on graph cut and Label Propagation Algorithm (LPA) are static methods [3]
We reveal that mixing temporal data to snapshots and measuring community structure is less effective with the course of time
Summary
The concept of community defined by Newman and Girvan is widely accepted and used [1]. The static methods divide a network into different communities based on static graph where the relationship between nodes and edges will not change. Increasing numbers of real data sets cannot be denoted by a static graph since edges and nodes are changing constantly. Structure-based methods and incremental analysis are the two most common methods used for community detection in a temporal network. Much research has used incremental analysis for community detection in temporal networks. Propose a new method called Temporal Label Walk (TLW) to detect community structure in temporal networks without time window. We reveal that mixing temporal data to snapshots and measuring community structure is less effective with the course of time
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