Anomaly Detection for Inferring Social Structure

Anomaly Detection for Inferring Social Structure

Lisa Friedland (University of Massachusetts Amherst, USA)
Copyright: © 2009 |Pages: 6
DOI: 10.4018/978-1-60566-010-3.ch007
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In traditional data analysis, data points lie in a Cartesian space, and an analyst asks certain questions: (1) What distribution can I fit to the data? (2) Which points are outliers? (3) Are there distinct clusters or substructure? Today, data mining treats richer and richer types of data. Social networks encode information about people and their communities; relational data sets incorporate multiple types of entities and links; and temporal information describes the dynamics of these systems. With such semantically complex data sets, a greater variety of patterns can be described and views constructed of the data. This article describes a specific social structure that may be present in such data sources and presents a framework for detecting it. The goal is to identify tribes, or small groups of individuals that intentionally coordinate their behavior—individuals with enough in common that they are unlikely to be acting independently. While this task can only be conceived of in a domain of interacting entities, the solution techniques return to the traditional data analysis questions. In order to find hidden structure, we use an anomaly detection approach: develop a model to describe the data, then identify outliers.
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This article refers throughout to the case study by Friedland and Jensen (2007) that introduced the tribes task. The National Association of Securities Dealers (NASD) regulates the securities industry in the United States. (Since the time of the study, NASD has been renamed the Financial Industry Regulatory Authority.) NASD monitors over 5000 securities firms, overseeing their approximately 170,000 branch offices and 600,000 employees that sell securities to the public. One of NASD’s primary activities is to predict and prevent fraud among these employees, called registered representatives, or reps. Equipped with data about the reps’ past employments, education, and “disclosable events,” it must focus its investigatory resources on those reps most likely to engage in risky behavior. Publications by Neville et al. (2005) and Fast et al. (2007) describe the broader fraud detection problem within this data set.

NASD investigators suspect that fraud risk depends on the social structure among reps and their employers. In particular, some cases of fraud appear to be committed by what we have termed tribes—groups of reps that move from job to job together over time. They hypothesized such coordinated movement among jobs could be predictive of future risk. To test this theory, we developed an algorithm to detect tribe behavior. The algorithm takes as input the employment dates of each rep at each branch office, and outputs small groups of reps who have been co-workers to a striking, or anomalous, extent.

This task draws upon several themes from data mining and machine learning:

Inferring latent structure in data. The data we observe may be a poor view of a system’s underlying processes. It is often useful to reason about objects or categories we believe exist in real life, but that are not explicitly represented in the data. The hidden structures can be inferred (to the best of our ability) as a means to further analyses, or as an end in themselves. To do this, typically one assumes an underlying model of the full system. Then, a method such as the expectation-maximization algorithm recovers the best match between the observed data and the hypothesized unobserved structures. This type of approach is ubiquitous, appearing for instance in mixture models and clustering (MacKay, 2003), and applied to document and topic models (Hofmann, 1999; Steyvers, et al. 2004).

In relational domains, the latent structure most commonly searched for is clusters. Clusters (in graphs) can be described as groups of nodes densely connected by edges. Relational clustering algorithms hypothesize the existence of this underlying structure, then partition the data so as best to reflect the such groups (Newman, 2004; Kubica et al., 2002; Neville & Jensen, 2005). Such methods have analyzed community structures within, for instance, a dolphin social network (Lusseau & Newman, 2004) and within a company using its network of emails (Tyler et al., 2003). Other variations assume some alternative underlying structure. Gibson et al. (1998) use notions of hubs and authorities to reveal communities on the web, while a recent algorithm by Xu et al. (2007) segments data into three types—clusters, outliers, and hub nodes.

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