Higher Order Knowledge Transfer for Dynamic Community Detection With Great Changes
针对动态网络中社区结构发生剧烈变化时传统方法失效的问题，提出利用前一时刻的高阶知识辅助后续时刻的社区检测，并在低相似度数据集上验证了高阶知识比一阶知识更有价值。
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 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 article 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 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.