Segmentation and Automated Social Hierarchy Detection through Email Network Analysis

Creamer, Germán G.; Rowe, Ryan; Hershkop, Shlomo; Stolfo, Salvatore J.

doi:10.1007/978-3-642-00528-2_3

Cited by 35 publications

(23 citation statements)

References 19 publications

(16 reference statements)

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“…We are not aware of any existing work on querying maximal cliques and hence we only report the results of our algorithms for different types of queries under different settings. We processed each query using 4,8,16,32, and 64 machines, respectively, and recorded the elapsed running time (in seconds).…”

Section: Results Of Querying Maximal Cliquesmentioning

confidence: 99%

“…MCE is a fundamental problem in graph theory and closely related to many other important graph problems, such as maximal independent sets (or minimal vertex covers), graph coloring, maximal common induced subgraphs, etc. Apart from graph theory, maximal cliques are used in a broad range of applications such as social network analysis [1], financial network analysis [2], dynamic network clustering [3], email network hierarchy detection [4], emergent pattern detection in terrorist networks [5], structural study in behavioral and cognitive networks [6], and various analytical tasks in computational biology [7].…”

Section: Introductionmentioning

confidence: 99%

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Distributed Maximal Clique Computation and Management

Cheng

2016

IEEE Trans. Serv. Comput.

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Abstract-Maximal cliques are elementary substructures in a graph and instrumental in graph analysis such as the structural analysis of many complex networks, graph clustering and community detection, network hierarchy detection, emerging pattern mining, vertex importance measures, etc. However, the number of maximal cliques is also notoriously large even for many small real world graphs. This size problem gives rise to challenges in both computing and managing the set of maximal cliques. Many algorithms for computing maximal cliques have been proposed in the literature; however, most of them are sequential algorithms that cannot scale due to the high complexity of the problem, while existing parallel algorithms for computing maximal cliques are mostly immature and especially suffer from skewed workload. As for managing the set of maximal cliques, which is essential due to its large size, there is barely any efficient method for querying or updating the set of maximal cliques. In this paper, we first propose a distributed algorithm built on a share-nothing architecture for computing the set of maximal cliques. We effectively address the problem of skewed workload distribution due to high-degree vertices, which also leads to drastically reduced worst-case time complexity for computing maximal cliques in common real-world graphs. Then, we propose a set of fundamental query operations and efficient algorithms to process the queries, to aid more efficient and effective analysis of the set of maximal cliques. Finally, we also devise algorithms to support efficient update maintenance of the set of maximal cliques when the underlying graph is updated. We verify the efficiency of our algorithms for computing, querying, and updating the set of maximal cliques with a range of real-world graphs from different application domains.

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Section: Results Of Querying Maximal Cliquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed Maximal Clique Computation and Management

Cheng

2016

IEEE Trans. Serv. Comput.

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“…Many of the social networks that have been studied appear to be complex. Examples of such include the World Wide Web [3][37], citation network [25] [28], email exchange network [11], among others.…”

Section: A Social Network Analysismentioning

confidence: 99%

Hierarchical social network analysis using multi-agent systems: A school system case

2014

2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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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. We 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, we also build 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, we use real school data from Bexar County's 15 school districts in Texas. The first result shows that our interaction-based method is able to generate meaningful clustering and dendrograms for social networks. Additionally our 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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“…It is closely related to a number of fundamental graph problems, such as maximal independent sets (or minimal vertex covers) [Tsukiyama et al 1977], graph coloring [Byskov 2003], maximal common induced subgraphs [Koch 2001], maximal common edge subgraphs [Koch 2001], etc. Its significance is not just limited to graph theory but also in numerous applications in various realworld networks, such as social network analysis [Faust and Wasserman 1995], hierarchy detection through email networks [Creamer et al 2007], study of structures in Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored.…”

Section: Maximal Clique Enumerationmentioning

confidence: 99%

Finding maximal cliques in massive networks

Cheng

et al. 2011

ACM Trans. Database Syst.

121

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Maximal clique enumeration is a fundamental problem in graph theory and has important applications in many areas such as social network analysis and bioinformatics. The problem is extensively studied; however, the best existing algorithms require memory space linear in the size of the input graph. This has become a serious concern in view of the massive volume of today's fast-growing networks. We propose a general framework for designing external-memory algorithms for maximal clique enumeration in large graphs. The general framework enables maximal clique enumeration to be processed recursively in small subgraphs of the input graph, thus allowing in-memory computation of maximal cliques without the costly random disk access. We prove that the set of cliques obtained by the recursive local computation is both correct (i.e., globally maximal) and complete. The subgraph to be processed each time is defined based on a set of base vertices that can be flexibly chosen to achieve different purposes. We discuss the selection of the base vertices to fully utilize the available memory in order to minimize I/O cost in static graphs, and for update maintenance in dynamic graphs. We also apply our framework to design an external-memory algorithm for maximum clique computation in a large graph.

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Segmentation and Automated Social Hierarchy Detection through Email Network Analysis

Cited by 35 publications

References 19 publications

Distributed Maximal Clique Computation and Management

Distributed Maximal Clique Computation and Management

Hierarchical social network analysis using multi-agent systems: A school system case

Finding maximal cliques in massive networks

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