Hierarchical Social Network Analysis Using a Multi-Agent System: A School System Case

Hierarchical Social Network Analysis Using a Multi-Agent System: A School System Case

Lizhu Ma (Department of Computer Science, Trinity University, San Antonio, TX, USA) and Xin Zhang (College of Business, Austin Peay State University, Clarksville, TN, USA)
Copyright: © 2013 |Pages: 19
DOI: 10.4018/ijats.2013070102
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Abstract

The quality of K-12 education has been a major concern in the nation for years. School systems, just like many other social networks, appear to have a hierarchical structure. Understanding this structure could be the key to better evaluating student performance and improving school quality. Many studies have been focusing on detecting hierarchical structure by using hierarchical clustering algorithms. The authors design an interaction-based similarity measure to accomplish hierarchical clustering in order to detect hierarchical structures in social networks (e.g. school district networks). This method uses a multi-agent system, for it is based on agent interactions. With the network structure detected, they also built a model, which is based on the MAXQ algorithm, to decompose the funding policy task into subtasks and then evaluate these subtasks by using funding distribution policies from past years and looking for possible relationships between student performances and funding policies. For the experiment, the authors used real school data from Bexar county’s 15 school districts in Texas. The first result shows that their interaction-based method is able to generate meaningful clustering and dendrograms for social networks. Additionally the authors’ policy evaluation model is able to evaluate funding policies from the past three years in Bexar County and conclude that increasing funding does not necessarily have a positive impact on student performance and it is generally not the case that the more is spent, the better.
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1. Introduction

The quality of K-12 education has been a very big concern for years. Many studies have been conducted in the field. There are also many factors that have been studied, such as, school choice (Bettinger 2005; Lubienski & Lubienski 2006), school size (Slate & Jones 2005), teacher quality (Rockoff 2004; Harris & Sass, 2007), school/school district administrator quality (Meier et al. 2003; Clark 2010), funding (Crampton 2009; Anderson 2011), etc. Because previous research in this field mostly studied the impact of one or two of those factors on school performance, the results they provide can be limited.

A social network is a set of people (or organizations or other entities), which are represented by nodes, connected by a set of socially meaningful relationships, which are represented by edges (Wellman 1997). A school district system, which is a set of many different actors, such as students, teachers, principals, school staffs etc., is a social network. There might be underlying community structure within a network, which is the division of network nodes into groups within which network connections are dense (Newman and Girvan 2003).

Social network analysis has been an emerging field in recent years. It views social relationships in terms of nodes (agents) and edges (ties). Research has shown that social networks play a critical role in determining the way problems are solved, organizations are run, etc. (Andrighetto et al. 2009).

A multi-agent system (MAS) is a set of autonomous and interactive entities called agents (Guessoum et al. 2003). Multi-agent system and social network analysis share some similarities (e.g. agents, relationships, etc.). Much research has successfully combined these two together (Grant 2009; Ma et al. 2009). In multi-agents simulations, when agents communicate with each other or work together on a common goal, agents are often organized into networks. For a survey on networks, see Newman (2003).

It is often very important to find underlying structure for social networks in order to better understand how they work. Hierarchical clustering algorithms can find multi-level clustering for a network. They are further divided into two classes: agglomerative algorithms and divisive algorithms. Agglomerative or bottom-up algorithms start with each node in its own singleton cluster, and at each step merge these clusters into larger ones until all clusters are merged into one big cluster (Schaeffer 2007).

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