Title: Graph Generation via Temporal-Aware Biased Walks
Primary area: learning on graphs and other geometries & topologies
Keywords: Graph Generation, Biased Random Walk, Transformer

Abstract: Some real networks keep a fixed structure (e.g., roads, sensors and their connections) while node or edge signals evolve over time. Existing graph generators either model topology changes (i.e., edge additions/deletions) or focus only on static graph properties (such as degree distributions or motifs), without considering how temporal signals shape the generated structure. By approaching the problem from an unconventional perspective, we introduce temporally attributed graphs, named TANGEM, that integrate a temporal similarity matrix into biased random walks, thereby coupling signals with structure to generate graphs that highlight patterns reflecting how nodes co-activate over time. We evaluate TANGEM using an approach that separates structural fidelity (clustering, spectral metrics) from downstream temporal consistency, allowing us to clearly isolate the impact of the topology generator itself. In time series benchmarks, TANGEM consistently outperforms strong baselines in structural metrics while remaining lightweight, learning from a single graph. These results show that adding temporal bias to structural sampling produces more realistic graphs and establishes TANGEM as a basis for future models that further integrate evolving signals and structure.

TL;DR: We propose a graph generation framework that uses a strategically designed Temporal-Aware Biased Random Walk sampling procedure to synthesize large, structurally faithful graphs.