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Accession Number ADA566334
Title Large-scale Heterogeneous Network Data Analysis.
Publication Date Jul 2012
Media Count 48p
Personal Author S. Lin
Abstract Large-scale network is a powerful data structure allowing the depiction of relationship information between entities. An unsupervised tensor-based mechanism was proposed, 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. Based on this model, solutions to tackle three critical issues in heterogeneous networks are presented. First, different aspects of central individuals are identified through three proposed measures, including contribution-based, diversity-based, and similarity-based centrality. Second, a role-based clustering method was proposed to identify nodes playing similar roles in the network. Third, to facilitate further explorations and visualization in a complex network data, the egocentric information abstraction was devised and three abstraction criteria was proposed to distill representative and significant information with respect to any given node. The evaluations are conducted on a real-world movie dataset, and an artificial crime dataset. The proposed centralities and role-based clustering can indeed find some meaningful results. The effectiveness of the egocentric abstraction is shown by providing more accurate, efficient, and confidential crime detection for human subjects.
Keywords Algorithms
Computer networks
Data processing
Egocentric abstraction
Foreign reports
Information systems
Knowledge discovery
Network analysis
Social networks

Source Agency Non Paid ADAS
NTIS Subject Category 62B - Computer Software
45C - Common Carrier & Satellite
Corporate Author National Taiwan Univ., Taipei.
Document Type Technical report
Title Note Final rept. 1 Jul 2011-30 Jun 2012.
NTIS Issue Number 1307
Contract Number FA2386-11-1-4063

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