Resumen
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.