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Methodology for Identifying Social Groups within a Transactional Graph

Maxence Morin, Baptiste Hemery, Fabrice Jeanne, Estelle Pawlowski-Cherrier

TL;DR

This paper introduces a novel framework specifically designed to identify groups of users within transactional graphs by focusing on the contextual and structural nuances that define these groups.

Abstract

Social network analysis is pivotal for organizations aiming to leverage the vast amounts of data generated from user interactions on social media and other digital platforms. These interactions often reveal complex social structures, such as tightly-knit groups based on common interests, which are crucial for enhancing service personalization or fraud detection. Traditional methods like community detection and graph matching, while useful, often fall short of accurately identifying specific groups of users. This paper introduces a novel framework specifically designed to identify groups of users within transactional graphs by focusing on the contextual and structural nuances that define these groups.

Methodology for Identifying Social Groups within a Transactional Graph

TL;DR

This paper introduces a novel framework specifically designed to identify groups of users within transactional graphs by focusing on the contextual and structural nuances that define these groups.

Abstract

Social network analysis is pivotal for organizations aiming to leverage the vast amounts of data generated from user interactions on social media and other digital platforms. These interactions often reveal complex social structures, such as tightly-knit groups based on common interests, which are crucial for enhancing service personalization or fraud detection. Traditional methods like community detection and graph matching, while useful, often fall short of accurately identifying specific groups of users. This paper introduces a novel framework specifically designed to identify groups of users within transactional graphs by focusing on the contextual and structural nuances that define these groups.

Paper Structure

This paper contains 13 sections, 13 equations, 3 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. Problem Statement
  3. Notations
  4. Problem Formalization
  5. Framework Definition
  6. First Approach
  7. Subgraphs Function
  8. Features Function
  9. Selection Step
  10. Second Approach
  11. How to Evaluate the Framework
  12. Conclusions and Future Work
  13. Acknowledgments.

Figures (3)

  • Figure 1: Differences between the observed transactional truth and the underlying and inaccessible social truth.
  • Figure 2: The first two subgraphs represent two different types of SGI. The difference is not in the topology of the subgraphs but in their context. A simple query like in the subgraph \ref{['fig:query']} is not enough to differentiate them.
  • Figure 3: An example of a multigraph. Dashed nodes are from a subgraph in $\mathbb{S}_n$. Dashed and green nodes compose three subgraphs from $\mathbb{S}$. Black nodes are from a subgraph that shares a similar topology but not the same context.