Schema-Guided Event Graph Completion
Hongwei Wang, Zixuan Zhang, Sha Li, Jiawei Han, Yizhou Sun, Hanghang, Tong, Joseph P. Olive, Heng Ji

TL;DR
This paper introduces a schema-guided approach for event graph completion, predicting missing event nodes by leveraging event schemas and local topology features, outperforming existing methods on multiple datasets.
Contribution
It proposes a novel schema-guided method that maps event graphs to schema subgraphs and predicts missing nodes using local topology, with a self-supervised training strategy.
Findings
Achieves 4.3% to 19.4% F1 improvement over baselines
Demonstrates effectiveness on four diverse datasets
Introduces a specialized inference algorithm for event graphs
Abstract
We tackle a new task, event graph completion, which aims to predict missing event nodes for event graphs. Existing link prediction or graph completion methods have difficulty dealing with event graphs because they are usually designed for a single large graph such as a social network or a knowledge graph, rather than multiple small dynamic event graphs. Moreover, they can only predict missing edges rather than missing nodes. In this work, we propose to utilize event schema, a template that describes the stereotypical structure of event graphs, to address the above issues. Our schema-guided event graph completion approach first maps an instance event graph to a subgraph of the schema graph by a heuristic subgraph matching algorithm. Then it predicts whether a candidate event node in the schema graph should be added to the instantiated schema subgraph by characterizing two types of local…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsComplex Network Analysis Techniques · Advanced Graph Neural Networks · Bioinformatics and Genomic Networks
