Dynamic community detection including node attributes

Community detection is an important task in social network analysis. It is generally based on the links of a static network, where groups of connected nodes can be found. Real-world problems, however, are often characterized by behavior that changes over time. In such cases, we need dynamic community detection algorithms because they better capture the underlying dynamics. Furthermore, the inclusion of node attributes provides a more robust approach since dynamic attribute values could also indicate changes in the communities. We propose an algorithm for COmmunity DEtection in Dynamic Attributed NETworks (CoDeDANet), which allows us to find groups in dynamic attributed networks using both the link and node information. In the first phase, based on spectral clustering, the attributes' importance is optimized in a setting that joins the nodes' features with a topological structure. In a second phase, tensors are used to consider not only current but also past information. The algorithm was tested on four synthetic networks and two real-world social networks. The results show that CoDeDANet outperforms the other state-of-the-art community detection algorithms.

Automated summary

Community detection is a crucial task in social network analysis, but static networks may not capture the dynamics of real-world problems. We propose CoDeDANet, an algorithm that detects communities in dynamic attributed networks by considering both link and node information. By optimizing the importance of attributes based on spectral clustering and incorporating tensors to capture past information, CoDeDANet outperforms other state-of-the-art community detection algorithms in tests on synthetic and real-world networks.