Egocentric Information Abstraction for Heterogeneous Social Networks.
Journal
2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009, 20-22 July 2009, Athens, Greece
Pages
255-260
Date Issued
2009
Author(s)
Li, Cheng-Te
Abstract
Social network is a powerful data structure that allows the depiction of relationship information between entities. However, real-world social networks are sometimes too complex for human to pursue further analysis. In this work, an unsupervised mechanism is proposed for egocentric information abstraction in heterogeneous social networks. To achieve this goal, we propose a vector space representation for heterogeneous social networks to identify linear combination of relations as features and compute statistical dependencies as feature values. Then we design several abstraction criteria to distill representative and important information to construct the abstracted graphs for visualization. The evaluations conducted on a real world movie dataset and an artificial crime dataset demonstrate that the abstractions can indeed retain important information and facilitate more accurate and efficient human analysis. © 2009 IEEE.
SDGs
Other Subjects
Data sets; Human analysis; Linear combinations; Real-world; Social Networks; Statistical dependencies; Vector space representation; Abstracting; Data structures; Electric network analysis; Heterogeneous networks
Type
conference paper