Using Causality-Aware Graph Neural Networks to Predict Temporal Centralities in Dynamic Graphs

Published: 20 Oct 2023, Last Modified: 24 Nov 2023TGL Workshop 2023 LongPaperEveryoneRevisionsBibTeX
Keywords: Graph Neural Networks, Temporal Graphs, Node Regression, Temporal Centrality, Centrality, Higher Order Graphs
TL;DR: We use a time aware Graph Neural Network architecture to predict temporal node centralities in temporal graphs
Abstract: Node centralities play a pivotal role in network science, social network analysis, and recommender systems. In temporal data, static path-based centralities like closeness or betweeness can give misleading results about the true importance of nodes in a temporal graph. To address this issue, temporal generalizations of betweeness and closeness have been defined that are based on the shortest time-respecting paths between pairs of nodes. However, a major issue of those generalizations is that the calculation of such paths is computationally expensive. Addressing this issue, we study the application of De Bruijn Graph Neural Networks (DBGNN), a causality-aware graph neural network architecture, to predict temporal path-based centralities in time series data. We experimentally evaluate our approach in 13 temporal graphs from biological and social systems and show that it considerably improves the prediction of both betweenness and closeness centrality compared to a static Graph Convolutional Neural Network.
Supplementary Material: pdf
Format: Long paper, up to 8 pages. If the reviewers recommend it to be changed to a short paper, I would be willing to revise my paper to fit within 4 pages.
Submission Number: 27