09 August 2024
Representation learning for graphs has been gaining increasing attention over recent years. Such popularity often builds on the fact that complex phenomena are frequently understood as systems of interacting entities described as a graph. For such a reason, learning on graph-structured data through Deep Graph Networks (DGNs) has been adopted for solving problems in a variety of fields, such as biology, social science, and sensor networks.
However, modern graph representation learning works mostly under the assumption of dealing with regularly sampled temporal graph snapshots, which is far from realistic, e.g., social networks and physical systems are characterized by continuous dynamics and sporadic observations.
To address this limitation, EMERGE partners from the University of Pisa introduce in this work the Temporal Graph Ordinary Differential Equation (TG-ODE) framework, which learns both the temporal and spatial dynamics from graph streams where the intervals between observations are not regularly spaced. The authors empirically validate the proposed approach on several graph benchmarks, showing that TG-ODE can achieve state-of-the-art performance in irregular graph stream tasks.
Read the paper in the link below.
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