CEP3: Community Event Prediction with Neural Point Process on Graph
This addresses event forecasting in dynamic graphs for applications like social networks or recommendation systems, but it is incremental as it builds on existing methods with a novel decomposition approach.
The paper tackles the problem of jointly forecasting multiple link events and their timestamps on communities in Continuous Time Dynamic Graphs, proposing a model that combines Graph Neural Networks and Marked Temporal Point Process. The result demonstrates superior performance in model accuracy and training efficiency on established benchmarks.
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.