Social Network Analysis Practical Examples

Abdelrahim Al Aqqad

Chapter Chapter 18 in Fraud Analytics in Action, 2026, pp 443-456 from Springer

Abstract: Abstract Chapter 18 translates the social network analysis concepts introduced in Chapter 17 into hands-on Python practice, using real financial transaction datasets to detect fraud through graph-based techniques. The chapter centers on two interconnected practical examples, both drawing on transfers.csv and account_info.csv datasets. The first example constructs a transaction network graph using NetworkX, assigns account-type attributes to nodes, and computes the attribute assortativity coefficient to quantify homophily—the tendency of similar account types to cluster together. Color-coded visualizations make fraud rings immediately apparent, with suspicious accounts appearing distinctly clustered. The second example focuses on money mule detection, using subgraph analysis and degree ratios to estimate the probability that a given account is involved in money laundering. By comparing an account’s degree within a subgraph of known mules to its degree in the full network, investigators can prioritize high-risk accounts for deeper review. Together, these examples demonstrate how graph topology metrics—assortativity, in-degree, out-degree, and degree ratios—can transform raw transaction data into actionable fraud intelligence, complementing traditional machine learning approaches with the structural insights unique to network analysis.

Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-16023-2_18

Ordering information: This item can be ordered from
http://www.springer.com/9783032160232

DOI: 10.1007/978-3-032-16023-2_18

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().