Quantification of network structural dissimilarities based on graph embedding

TL;DR

This paper introduces a novel network comparison method using graph embedding with DeepWalk, capturing global structural information to effectively quantify dissimilarities between complex networks.

Contribution

The paper proposes a new network comparison approach based on DeepWalk embeddings and spectral entropy, outperforming existing methods in distinguishing network structures.

Findings

Outperforms baseline methods in network dissimilarity tasks

Can distinguish networks using only global embedding-based distance distributions

Captures properties like average shortest path and link density

Abstract

Identifying and quantifying structural dissimilarities between complex networks is a fundamental and challenging problem in network science. Previous network comparison methods are based on the structural features, such as the length of shortest path, degree and graphlet, which may only contain part of the topological information. Therefore, we propose an efficient network comparison method based on network embedding, i.e., \textit{DeepWalk}, which considers the global structural information. In detail, we calculate the distance between nodes through the vector extracted by \textit{DeepWalk} and quantify the network dissimilarity by spectral entropy based Jensen-Shannon divergences of the distribution of the node distances. Experiments on both synthetic and empirical data show that our method outperforms the baseline methods and can distinguish networks perfectly by only using the global embedding based distance distribution. In addition, we show that our method can capture network properties, e.g., average shortest path length and link density. Moreover, the experiments of modularity further implies the functionality of our method.