Producing a Unified Graph Representation from Multiple Social Network Views

TL;DR

This paper introduces an unsupervised method to integrate multiple social network data views into a single unified graph, facilitating community detection and data visualization.

Contribution

It presents a novel approach combining k-nearest neighbor sets from different data views to produce a unified graph representation, applicable to relation-based and feature-based data.

Findings

Supports community structure discovery in multi-view Twitter datasets

Effective in integrating heterogeneous social network data

Enhances data analysis tasks like visualization and clustering

Abstract

In many social networks, several different link relations will exist between the same set of users. Additionally, attribute or textual information will be associated with those users, such as demographic details or user-generated content. For many data analysis tasks, such as community finding and data visualisation, the provision of multiple heterogeneous types of user data makes the analysis process more complex. We propose an unsupervised method for integrating multiple data views to produce a single unified graph representation, based on the combination of the k-nearest neighbour sets for users derived from each view. These views can be either relation-based or feature-based. The proposed method is evaluated on a number of annotated multi-view Twitter datasets, where it is shown to support the discovery of the underlying community structure in the data.