Virtual Nodes Go Temporal

Sofiane ENNADIR, Yassir Jedra, Oleg Smirnov, Lele Cao

Keywords: Temporal Graphs, Dynamic Graph Representation Learning, Virtual Nodes

TL;DR: We show that adding community-based virtual nodes to Continuous-Time Dynamic Graph models improves long-range information flow and overall performance.

Abstract: Learning representations of temporally evolving graphs, also known as Continuous-Time Dynamic Graphs (CTDGs), has gained considerable attention due to their ability to model a wide range of real-world phenomena. Recent efforts extend the well-established message-passing paradigm and Graph Neural Network (GNN) models, originally designed for static graphs, to account for the temporal dimension of dynamic graphs. Although these methods have shown promising results, they often inherit limitations from their static counterparts, particularly regarding the capture of long-range interactions. In static settings, adding Virtual Nodes (VNs) has proven effective in overcoming locality constraints and boosting performance. In this work, we conduct a theoretical analysis of the impact of VNs in CTDG-based models. Specifically, we introduce the concept of information flow, which examines how information propagates through a graph following an event. From this perspective, we highlight inherent limitations of existing CTDG-based approaches and demonstrate how adding VNs can address these constraints. Building on these insights, we propose *k*-TVNs, a framework that incorporates a set of fully connected VNs, each representing a distinct community within the graph. Through both theoretical investigation and empirical validation, we show that incorporating VNs substantially improves the performance of CTDG models.

Submission Type: Full paper proceedings track submission (max 9 main pages).

Submission Number: 80