Event Detection on Dynamic Graphs

• URL: http://arxiv.org/abs/2110.12148v1
• Date: Sat, 23 Oct 2021 05:52:03 GMT
• Title: Event Detection on Dynamic Graphs
• Authors: Mert Kosan, Arlei Silva, Sourav Medya, Brian Uzzi, Ambuj Singh
• Abstract summary: Event detection is a critical task for timely decision-making in graph analytics applications. We propose DyGED, a simple yet novel deep learning model for event detection on dynamic graphs.
• Score: 4.128347119808724
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Event detection is a critical task for timely decision-making in graph analytics applications. Despite the recent progress towards deep learning on graphs, event detection on dynamic graphs presents particular challenges to existing architectures. Real-life events are often associated with sudden deviations of the normal behavior of the graph. However, existing approaches for dynamic node embedding are unable to capture the graph-level dynamics related to events. In this paper, we propose DyGED, a simple yet novel deep learning model for event detection on dynamic graphs. DyGED learns correlations between the graph macro dynamics -- i.e. a sequence of graph-level representations -- and labeled events. Moreover, our approach combines structural and temporal self-attention mechanisms to account for application-specific node and time importances effectively. Our experimental evaluation, using a representative set of datasets, demonstrates that DyGED outperforms competing solutions in terms of event detection accuracy by up to 8.5% while being more scalable than the top alternatives. We also present case studies illustrating key features of our model.

Related papers

Err

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.