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.
翻译:网络结构随着时间在现实世界中的变化而变化,在动态网络中发现变化中的社区是一个具有挑战性任务的重要研究课题,大多数现有方法都假定网络没有发生重大变化;即相邻快照之间的差别很小;然而,现实世界通常存在巨大的变化。网络的巨大变化将导致社区检测算法难以从先前的快照中获取宝贵的信息,从而导致下一个阶段的负转移。本文件侧重于动态社区检测,通过整合前一快照的较高层次知识来进行重大变革,以帮助随后的快照。此外,为了提高搜索效率,设计了较高层次的知识转移战略,通过检测相邻快照矩阵的相似性来确定一阶和更高层次知识。这样,我们的建议可以更好地保留先前社区检测结果的优势,并将它们转移到下一个任务。我们对四个真实世界网络进行实验,包括网络进行巨大或微小变化的网络。低层次数据集的实验结果表明,在网络发生巨大变化时,高层次知识比第一层次知识更有价值,在网络发生巨大变化时,我们高层次的建议也能保持优势。