Dynamic Network Embeddings for Network Evolution Analysis

TL;DR

This paper introduces a novel dynamic network embedding method that effectively captures temporal evolution in networks using random walks and Bernoulli embeddings, outperforming existing methods in link prediction and node evolution tasks.

Contribution

The paper presents a new dynamic network embedding approach that preserves temporal continuity without alignment, improving analysis of network evolution.

Findings

Outperforms state-of-the-art methods in link prediction

Effectively detects evolving nodes in dynamic networks

Visualizes temporal trajectories of nodes in embedded space

Abstract

Network embeddings learn to represent nodes as low-dimensional vectors to preserve the proximity between nodes and communities of the network for network analysis. The temporal edges (e.g., relationships, contacts, and emails) in dynamic networks are important for network evolution analysis, but few existing methods in network embeddings can capture the dynamic information from temporal edges. In this paper, we propose a novel dynamic network embedding method to analyze evolution patterns of dynamic networks effectively. Our method uses random walk to keep the proximity between nodes and applies dynamic Bernoulli embeddings to train discrete-time network embeddings in the same vector space without alignments to preserve the temporal continuity of stable nodes. We compare our method with several state-of-the-art methods by link prediction and evolving node detection, and the experiments demonstrate that our method generally has better performance in these tasks. Our method is further verified by two real-world dynamic networks via detecting evolving nodes and visualizing their temporal trajectories in the embedded space.