Higher-order memory guided temporal random walk for dynamic heterogeneous network embedding

Abstract

Network embedding (NE) aims at learning node embeddings via structure-based sampling. However, there are complex patterns in network structure (heterogeneity, higher-order dependence, dynamics) in the real world. The existing methods suffer from high dependence and constraints on manually designed higher-order structures and loss of fine-grained temporal information. To solve the above challenges, we propose a novel higher-order memory guided temporal random walk for dynamic heterogeneous network embedding (HoMo-DyHNE). The proposed model is a two-stage architecture consisting of a meta-structure-independent random walk algorithm namely HoMo-TRW with transition vectors and higher-order memory, and a Hawkes-based featured Skip-gram (HFSG) incorporating a multivariate Hawkes point process to measure the history-current association intensity. Extensive experiments demonstrate the superior effectiveness of our proposed method.