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

TL;DR

This paper introduces a higher-order knowledge transfer method for dynamic community detection in networks with significant changes, improving accuracy by selectively leveraging past information based on snapshot similarity.

Contribution

It proposes a novel higher-order knowledge transfer strategy that adapts to substantial network changes, enhancing dynamic community detection performance.

Findings

Higher-order knowledge is more effective than first-order in highly changed networks.

The method maintains advantages even in datasets with minor changes.

Experimental results outperform existing approaches on real-world networks.

Abstract

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.