DynACPD Embedding Algorithm for Prediction Tasks in Dynamic Networks

TL;DR

This paper introduces DynACPD, a novel tensor-based embedding algorithm for dynamic networks that improves link prediction by capturing temporal and spatial relationships more effectively than existing methods.

Contribution

The paper proposes a new tensor decomposition-based embedding method specifically designed for dynamic networks, enhancing prediction tasks over traditional static approaches.

Findings

Outperforms baseline methods in link prediction accuracy

Demonstrates efficiency on real-world dynamic networks

Provides mathematical rationale for embedding effectiveness

Abstract

Classical network embeddings create a low dimensional representation of the learned relationships between features across nodes. Such embeddings are important for tasks such as link prediction and node classification. In the current paper, we consider low dimensional embeddings of dynamic networks, that is a family of time varying networks where there exist both temporal and spatial link relationships between nodes. We present novel embedding methods for a dynamic network based on higher order tensor decompositions for tensorial representations of the dynamic network. In one sense, our embeddings are analogous to spectral embedding methods for static networks. We provide a rationale for our algorithms via a mathematical analysis of some potential reasons for their effectiveness. Finally, we demonstrate the power and efficiency of our approach by comparing our algorithms' performance on the link prediction task against an array of current baseline methods across three distinct real-world dynamic networks.