GoT-WAVE: Temporal network alignment using graphlet-orbit transitions

TL;DR

GoT-WAVE introduces a novel graphlet-orbit transition-based measure for temporal network alignment, significantly improving accuracy and efficiency over previous methods, and supporting directed edges.

Contribution

It replaces DGDVs with GoTs as a dynamic node similarity measure in DynaWAVE, creating a new, more effective approach for temporal network alignment.

Findings

GoT-WAVE improves accuracy by 25% on synthetic networks.

It achieves 64% faster performance compared to DynaWAVE.

Supports directed edges, unlike previous methods.

Abstract

Global pairwise network alignment (GPNA) aims to find a one-to-one node mapping between two networks that identifies conserved network regions. GPNA algorithms optimize node conservation (NC) and edge conservation (EC). NC quantifies topological similarity between nodes. Graphlet-based degree vectors (GDVs) are a state-of-the-art topological NC measure. Dynamic GDVs (DGDVs) were used as a dynamic NC measure within the first-ever algorithms for GPNA of temporal networks: DynaMAGNA++ and DynaWAVE. The latter is superior for larger networks. We recently developed a different graphlet-based measure of temporal node similarity, graphlet-orbit transitions (GoTs). Here, we use GoTs instead of DGDVs as a new dynamic NC measure within DynaWAVE, resulting in a new approach, GoT-WAVE. On synthetic networks, GoT-WAVE improves DynaWAVE's accuracy by 25% and speed by 64%. On real networks, when optimizing only dynamic NC, each method is superior ~50% of the time. While DynaWAVE benefits more from also optimizing dynamic EC, only GoT-WAVE can support directed edges. Hence, GoT-WAVE is a promising new temporal GPNA algorithm, which efficiently optimizes dynamic NC. Future work on better incorporating dynamic EC may yield further improvements.