CEP3: Community Event Prediction with Neural Point Process on Graph

TL;DR

This paper introduces CEP3, a neural point process model on graphs that jointly predicts multiple community link events and timestamps in continuous-time dynamic graphs, outperforming previous methods.

Contribution

The paper proposes a novel neural model combining GNNs and MTPP for joint event and timestamp prediction on CTDGs, with a scalable factorization approach.

Findings

Superior accuracy over existing models

Enhanced training efficiency

Effective scalability to large graphs

Abstract

Many real world applications can be formulated as event forecasting on Continuous Time Dynamic Graphs (CTDGs) where the occurrence of a timed event between two entities is represented as an edge along with its occurrence timestamp in the graphs.However, most previous works approach the problem in compromised settings, either formulating it as a link prediction task on the graph given the event time or a time prediction problem given which event will happen next. In this paper, we propose a novel model combining Graph Neural Networks and Marked Temporal Point Process (MTPP) that jointly forecasts multiple link events and their timestamps on communities over a CTDG. Moreover, to scale our model to large graphs, we factorize the jointly event prediction problem into three easier conditional probability modeling problems.To evaluate the effectiveness of our model and the rationale behind such a decomposition, we establish a set of benchmarks and evaluation metrics for this event forecasting task. Our experiments demonstrate the superior performance of our model in terms of both model accuracy and training efficiency.