Continuous-Time Graph Representation with Sequential Survival Process

Abdulkadir Çelikkanat, Nikolaos Nakis, Morten Mørup

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Over the past two decades, there has been a tremendous increase in the growth of representation learning methods for graphs, with numerous applications across various fields, including bioinformatics, chemistry, and the social sciences. However, current dynamic network approaches focus on discrete-time networks or treat links in continuous-time networks as instantaneous events. Therefore, these approaches have limitations in capturing the persistence or absence of links that continuously emerge and disappear over time for particular durations. To address this, we propose a novel stochastic process relying on survival functions to model the durations of links and their absences over time. This forms a generic new likelihood specification explicitly accounting for intermittent edge-persistent networks, namely GRAS2P: Graph Representation with Sequential Survival Process. We apply the developed framework to a recent continuous time dynamic latent distance model characterizing network dynamics in terms of a sequence of piecewise linear movements of nodes in latent space. We quantitatively assess the developed framework in various downstream tasks, such as link prediction and network completion, demonstrating that the developed modeling framework accounting for link persistence and absence well tracks the intrinsic trajectories of nodes in a latent space and captures the underlying characteristics of evolving network structure.
Original languageEnglish
Title of host publicationProceedings of the 38th AAAI Conference on Artificial Intelligence
PublisherAAAI Press
Publication date2024
ISBN (Electronic)978-1-57735-887-9
Publication statusPublished - 2024
Event38th AAAI Conference on Artificial Intelligence - Vancouver , Canada
Duration: 20 Feb 202427 Feb 2024


Conference38th AAAI Conference on Artificial Intelligence


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