A Deep Probabilistic Framework for Continuous Time Dynamic Graph Generation

TL;DR

This paper introduces DG-Gen, a novel probabilistic framework for generating continuous-time dynamic graphs, enabling scalable, assumption-free synthesis of evolving graph data and improving link prediction accuracy.

Contribution

It presents a new generative model that directly captures temporal edge formation probabilities, advancing dynamic graph synthesis beyond static augmentation methods.

Findings

DG-Gen produces higher fidelity dynamic graphs.

It significantly improves link prediction performance.

The method is scalable and inductive.

Abstract

Recent advancements in graph representation learning have shifted attention towards dynamic graphs, which exhibit evolving topologies and features over time. The increased use of such graphs creates a paramount need for generative models suitable for applications such as data augmentation, obfuscation, and anomaly detection. However, there are few generative techniques that handle continuously changing temporal graph data; existing work largely relies on augmenting static graphs with additional temporal information to model dynamic interactions between nodes. In this work, we propose a fundamentally different approach: We instead directly model interactions as a joint probability of an edge forming between two nodes at a given time. This allows us to autoregressively generate new synthetic dynamic graphs in a largely assumption free, scalable, and inductive manner. We formalize this approach as DG-Gen, a generative framework for continuous time dynamic graphs, and demonstrate its effectiveness over five datasets. Our experiments demonstrate that DG-Gen not only generates higher fidelity graphs compared to traditional methods but also significantly advances link prediction tasks.