Mining Heterogeneous Social Networks: Centrality, Clustering, and Abstraction
Date Issued
2009
Date
2009
Author(s)
Li, Cheng-Te
Abstract
Social network is a powerful data structure allowing the depiction of relationship information between entities. Recent researchers have proposed many successful methods on analyzing homogeneous social networks, assuming only a single type of node and relation. Nevertheless, real-world complex networks are usually heterogeneous, which presumes a network can be composed of different types of nodes and relations. n this thesis, we propose an unsupervised tensor-based mechanism, considering higher-order relational information, to model the complex semantics of nodes. The signature profiles are derived as a vector-based representation to enable further mining algorithms. Moreover, based on this model, we present solutions to tackle three critical issues in heterogeneous networks. First, we identify different aspects of central individuals through three proposed measures, including contribution-based, diversity-based, and similarity-based centrality. Second, we propose a role-based clustering method to identify nodes playing similar roles in the network. Third, to facilitate further explorations and visualization in a complex network data, we devise the egocentric information abstraction and address it by proposing three abstraction criteria to distill representative and significant information with respect to any given node. In the end, the evaluations are conducted on a real-world movie dataset, and an artificial crime dataset. We demonstrate the proposed centralities and role-based clustering can indeed find some meaningful results. And the effectiveness of the egocentric abstraction is shown by providing more accurate, efficient, and confidential crime detection for human subjects.
Subjects
Social Network
Centrality
Clustering
Information Abstraction
Heterogeneous Network
SDGs
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-98-R96944015-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):1e289589ca1395b5e428c98313c20ac3