Higher-order Knowledge Transfer for Dynamic Community Detection with Great Changes

Network structure evolves with time in the real world, and the discovery of changing communities in dynamic networks is an important research topic that poses challenging tasks. Most existing methods assume that no significant change in the network occurs; namely, the difference between adjacent snapshots is slight. However, great change exists in the real world usually. The great change in the network will result in the community detection algorithms are difficulty obtaining valuable information from the previous snapshot, leading to negative transfer for the next time steps. This paper focuses on dynamic community detection with substantial changes by integrating higher-order knowledge from the previous snapshots to aid the subsequent snapshots. Moreover, to improve search efficiency, a higher-order knowledge transfer strategy is designed to determine first-order and higher-order knowledge by detecting the similarity of the adjacency matrix of snapshots. In this way, our proposal can better keep the advantages of previous community detection results and transfer them to the next task. We conduct the experiments on four real-world networks, including the networks with great or minor changes. Experimental results in the low-similarity datasets demonstrate that higher-order knowledge is more valuable than first-order knowledge when the network changes significantly and keeps the advantage even if handling the high-similarity datasets. Our proposal can also guide other dynamic optimization problems with great changes.Comment: Submitted to IEEE TEV

Detecting the evolving community structure in dynamic social networks

© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Identifying the evolving community structure of social networks has recently drawn increasing attention. Evolutionary clustering, previously proposed to detect the evolution of clusters over time, presents a temporal smoothness framework to simultaneously maximize clustering accuracy and minimize the clustering drift between two successive time steps. Under this framework, evolving patterns of communities in dynamic networks were detected by finding the best trade-off between the clustering accuracy and temporal smoothness. However, two main drawbacks in previous methods limit the effectiveness of dynamic community detection. One is that the classic operators implemented by existing methods cannot avoid that a node is often inter-connected to most of its neighbors. The other is that those methods take it for granted that an inter-connection cannot exist between nodes clustered into the same community, which results in a limited search space. In this paper, we propose a novel multi-objective evolutionary clustering algorithm called DECS, to detect the evolving community structure in dynamic social networks. Specifically, we develop a migration operator cooperating with efficient operators to ensure that nodes and their most neighbors are grouped together, and use a genome matrix encoding the structure information of networks to expand the search space. DECS calculates the modularity based on the genome matrix as one of objectives to optimize. Experimental results on synthetic networks and real-world social networks demonstrate that DECS outperforms in both clustering accuracy and smoothness, contrasted with other state-of-the-art methods