TY - GEN
T1 - Egocentric information abstraction for heterogeneous social networks
AU - Li, Cheng-Te
AU - Lin, Shou De
PY - 2009/10/15
Y1 - 2009/10/15
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=70349826464&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70349826464&partnerID=8YFLogxK
U2 - 10.1109/ASONAM.2009.38
DO - 10.1109/ASONAM.2009.38
M3 - Conference contribution
AN - SCOPUS:70349826464
SN - 9780769536897
T3 - Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009
SP - 255
EP - 260
BT - Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009
T2 - 2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009
Y2 - 20 July 2009 through 22 July 2009
ER -
