Multi-temporal heterogeneous graph learning with pattern-aware attention for industrial chain risk detection

Ziheng Li, Yongjiao Sun, Xin Bi, Ruijin Wang, Shi Ying, Hangxu Ji

Abstract: Analyzing multi-channel data related to the industrial chain through graph representation learning is of significant value for industrial chain risk detection. Multi-channel data related to the industrial chain exhibits strong correlations, which enables such data to be modeled using graph structures. Furthermore, due to multi-channel data being multi-source, heterogeneous, and containing temporal information, industrial chain risk detection can be effectively conducted from the perspective of temporal heterogeneous graph representation learning. However, existing methods primarily obtain node features by sequentially processing the temporal heterogeneous information within a single graph, and they use attention based on discrete tokens to learn long-term information, which overlooks the multi-temporal information interactions between nodes and fails to effectively capture the temporal patterns in long-term information. To address these problems, we propose a multi-temporal heterogeneous graph learning with pattern-aware attention (MTHG-PA) for industrial chain risk detection. Firstly, we propose a method for interactive aggregation of multi-temporal heterogeneous information to learn node features, thereby avoiding the problem of insufficient information interaction between nodes across time. Secondly, we design a temporal pattern-aware attention module to learn the temporal patterns in long-term information, addressing the problem that traditional attention mechanisms can only handle discrete tokens. We conduct extensive experiments on three real-world datasets. The results show that our proposed method achieves superior performance in temporal heterogeneous graph learning compared to state-of-the-art methods.

External IDs:dblp:journals/www/LiSBWYJ24