Low-Rank Representations Towards Classification Problem of Complex Networks

TL;DR

This paper investigates how low-rank Euclidean embeddings of complex networks can be utilized for network classification tasks, aiming to improve understanding and prediction of network characteristics.

Contribution

It explores the effectiveness of low-rank representations in classifying real-world complex networks, providing insights into their utility for network analysis.

Findings

Low-rank embeddings capture essential network features.

Embeddings improve classification accuracy.

Insights into network structure and predictability.

Abstract

Complex networks representing social interactions, brain activities, molecular structures have been studied widely to be able to understand and predict their characteristics as graphs. Models and algorithms for these networks are used in real-life applications, such as search engines, and recommender systems. In general, such networks are modelled by constructing a low-dimensional Euclidean embedding of the vertices of the network, where proximity of the vertices in the Euclidean space hints the likelihood of an edge (link). In this work, we study the performance of such low-rank representations of real-life networks on a network classification problem.