An Automated System for Discovering Neighborhood Patterns in Ego Networks

TL;DR

This paper presents an automated, data-driven system that identifies and categorizes distinct ego-centric neighborhood patterns in social networks derived from smartphone data, aiding behavioral analysis over time.

Contribution

It introduces a novel method combining feature selection and clustering to discover and analyze neighborhood patterns in ego networks from real-world mobile data.

Findings

Eight distinct neighborhood patterns identified

Feature sets significantly influence clustering results

Patterns enable long-term behavioral trend analysis

Abstract

Generally, social network analysis has often focused on the topology of the network without considering the characteristics of individuals involved in them. Less attention is given to study the behavior of individuals, considering they are the basic entity of a graph. Given a mobile social network graph, what are good features to extract key information from the nodes? How many distinct neighborhood patterns exist for ego nodes? What clues does such information provide to study nodes over a long period of time? In this report, we develop an automated system in order to discover the occurrences of prototypical ego-centric patterns from data. We aim to provide a data-driven instrument to be used in behavioral sciences for graph interpretations. We analyze social networks derived from real-world data collected with smart-phones. We select 13 well-known network measures, especially those concerned with ego graphs. We form eight feature subsets and then assess their performance using unsupervised clustering techniques to discover distinguishing ego-centric patterns. From clustering analysis, we discover that eight distinct neighborhood patterns have emerged. This categorization allows concise analysis of users' data as they change over time. The results provide a fine-grained analysis for the contribution of different feature sets to detect unique clustering patterns. Last, as a case study, two datasets are studied over long periods to demonstrate the utility of this method. The study shows the effectiveness of the proposed approach in discovering important trends from data.