Related papers: Event Detection on Dynamic Graphs

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.

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