  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.
