Graph Hawkes Neural Network for Forecasting on Temporal Knowledge Graphs

Zhen Han, Yunpu Ma, Yuyi Wang, Stephan Günnemann, Volker Tresp

Keywords: Hawkes process, dynamic graphs, temporal knowledge graphs, point processes.

Subject Areas: Knowledge Representation, Semantic Web and Search, Relational AI

Abstract: The Hawkes process has become a standard method for modeling self-exciting event sequences with different event types. A recent work has generalized the Hawkes process to a neurally self-modulating multivariate point process, which enables the capturing of more complex and realistic impacts of past events on future events. However, this approach is limited by the number of possible event types, making it impossible to model the dynamics of evolving graph sequences, where each possible link between two nodes can be considered as an event type. The number of event types increases even further when links are directional and labeled. To address this issue, we propose the Graph Hawkes Neural Network that can capture the dynamics of evolving graph sequences and can predict the occurrence of a fact in a future time instance. Extensive experiments on large-scale temporal multi-relational databases, such as temporal knowledge graphs, demonstrate the effectiveness of our approach.

Archival Status: Archival

TL;DR: We propose the Graph Hawkes Neural Network for predicting future events on large-scale temporal knowledge graphs.