Impact of Dynamic Interactions on Multi-Scale Analysis of Community Structure in Networks

TL;DR

This paper explores how different interaction dynamics, conservative and non-conservative, influence multi-scale community detection in networks, using synchronization models and real social media data to reveal structural insights.

Contribution

It introduces a novel synchronization model with non-conservative interactions and demonstrates their impact on community detection compared to traditional conservative models.

Findings

Non-conservative interactions produce different community structures than conservative ones.

Synchronization-based similarity effectively reveals multi-scale communities.

Community quality correlates with user activity in social media networks.

Abstract

To find interesting structure in networks, community detection algorithms have to take into account not only the network topology, but also dynamics of interactions between nodes. We investigate this claim using the paradigm of synchronization in a network of coupled oscillators. As the network evolves to a global steady state, nodes belonging to the same community synchronize faster than nodes belonging to different communities. Traditionally, nodes in network synchronization models are coupled via one-to-one, or conservative interactions. However, social interactions are often one-to-many, as for example, in social media, where users broadcast messages to all their followers. We formulate a novel model of synchronization in a network of coupled oscillators in which the oscillators are coupled via one-to-many, or non-conservative interactions. We study the dynamics of different interaction models and contrast their spectral properties. To find multi-scale community structure in a network of interacting nodes, we define a similarity function that measures the degree to which nodes are synchronized and use it to hierarchically cluster nodes. We study real-world social networks, including networks of two social media providers. To evaluate the quality of the discovered communities in a social media network we propose a community quality metric based on user activity. We find that conservative and non-conservative interaction models lead to dramatically different views of community structure even within the same network. Our work offers a novel mathematical framework for exploring the relationship between network structure, topology and dynamics.