Community Detection in Dynamic Attributed Graphs

Community detection is one of the most widely studied tasks in network analysis because community structures are ubiquitous across real-world networks. These real-world networks are often both attributed and dynamic in nature. In this paper, we propose a community detection algorithm for dynamic attributed graphs that, unlike existing community detection methods, incorporates both temporal and attribute information along with the structural properties of the graph. Our proposed algorithm handles graphs with heterogeneous attribute types, as well as changes to both the structure and the attribute information, which is essential for its applicability to real-world networks. We evaluated our proposed algorithm on a variety of synthetically generated benchmark dynamic attributed graphs, as well as on large-scale real-world networks. The results obtained show that our proposed algorithm is able to identify graph partitions of high modularity and high attribute similarity more efficiently than state-of-the-art methods for community detection.