Network Composition from Multi-layer Data

TL;DR

This paper introduces a unified framework for integrating multi-layer social network data into a single weighted network using dynamical processes, enabling better analysis of complex heterogeneous social structures.

Contribution

It proposes a novel principled approach to compose multi-layer networks into a unified structure based on dynamical transformations, addressing heterogeneity in social network layers.

Findings

Framework effectively integrates multi-layer data into a single network

Demonstrates improved analysis capabilities for complex social structures

Provides practical examples for network analysis and design

Abstract

It is common for people to access multiple social networks, for example, using phone, email, and social media. Together, the multi-layer social interactions form a "integrated social network." How can we extend well developed knowledge about single-layer networks, including vertex centrality and community structure, to such heterogeneous structures? In this paper, we approach these challenges by proposing a principled framework of network composition based on a unified dynamical process. Mathematically, we consider the following abstract problem: Given multi-layer network data and additional parameters for intra and inter-layer dynamics, construct a (single) weighted network that best integrates the joint process. We use transformations of dynamics to unify heterogeneous layers under a common dynamics. For inter-layer compositions, we will consider several cases as the inter-layer dynamics plays different roles in various social or technological networks. Empirically, we provide examples to highlight the usefulness of this framework for network analysis and network design.