Information Extraction from Larger Multi-layer Social Networks

TL;DR

This paper presents a novel multi-criteria community detection method for multi-layer social networks, demonstrated on Twitter data with hashtags connected by behavioral and relational links.

Contribution

It introduces a Pareto optimality-based algorithm for extracting community structures from multi-layer networks, a novel approach in this domain.

Findings

Effective community detection on Twitter hashtags using the proposed method

Demonstrated the approach's ability to handle multi-layer network data

Showed the algorithm finds a diverse set of solutions along the Pareto frontier

Abstract

Social networks often encode community structure using multiple distinct types of links between nodes. In this paper we introduce a novel method to extract information from such multi-layer networks, where each type of link forms its own layer. Using the concept of Pareto optimality, community detection in this multi-layer setting is formulated as a multiple criterion optimization problem. We propose an algorithm for finding an approximate Pareto frontier containing a family of solutions. The power of this approach is demonstrated on a Twitter dataset, where the nodes are hashtags and the layers correspond to (1) behavioral edges connecting pairs of hashtags whose temporal profiles are similar and (2) relational edges connecting pairs of hashtags that appear in the same tweets.