Link Fraction Mixed Membership Reveals Community Diversity in Aggregated Social Networks

TL;DR

The paper introduces LFMM, a new mixed membership method for aggregated social networks that remains consistent across different levels of data aggregation, revealing community diversity and evolution.

Contribution

LFMM is a novel community detection method that maintains consistency under aggregation, unlike existing approaches, and uncovers community diversity in large-scale social networks.

Findings

LFMM reveals variation in community memberships across regions.

The method identifies urban hubs as melting pots of diverse communities.

Community memberships evolve over the last decade.

Abstract

Community detection is a critical tool for understanding the mesoscopic structure of large-scale networks. However, when applied to aggregated or coarse-grained social networks, disjoint community partitions cannot capture the diverse composition of community memberships within aggregated nodes. While existing mixed membership methods alleviate this issue, they may detect communities that are highly sensitive to the aggregation resolution, not reliably reflecting the community structure of the underlying individual-level network. This paper presents the Link Fraction Mixed Membership (LFMM) method, which computes the mixed memberships of nodes in aggregated networks. Unlike existing mixed membership methods, LFMM is consistent under aggregation. Specifically, we show that it conserves community membership sums at different scales. The method is utilized to study a population-scale social network of the Netherlands, aggregated at different resolutions. Experiments reveal variation in community membership across different geographical regions and evolution over the last decade. In particular, we show how our method identifies large urban hubs that act as the melting pots of diverse, spatially remote communities.