TY  - BOOK
AU  - 
AU  - 
AU  - 
AU  - 
ED  - SpringerLink (Online service)
TI  - Advances in Social Network Mining and Analysis: Second International Workshop, SNAKDD 2008, Las Vegas, NV, USA, August 24-27, 2008 
T2  - Lecture Notes in Computer Science,
SN  - 9783642149290
AV  - QA76.76.A65 
U1  - 005.7 23
PY  - 2010///
CY  - Berlin, Heidelberg
PB  - Springer Berlin Heidelberg
KW  - Computer science
KW  - Computer Communication Networks
KW  - Database management
KW  - Data mining
KW  - Information storage and retrieval systems
KW  - Information systems
KW  - Artificial intelligence
KW  - Computer Science
KW  - Information Systems Applications (incl.Internet)
KW  - Artificial Intelligence (incl. Robotics)
KW  - Database Management
KW  - Information Storage and Retrieval
KW  - Data Mining and Knowledge Discovery
N1  - Leveraging Label-Independent Features for Classification in Sparsely Labeled Networks: An Empirical Study -- Community Detection Using a Measure of Global Influence -- Communication Dynamics of Blog Networks -- Finding Spread Blockers in Dynamic Networks -- Social Network Mining with Nonparametric Relational Models -- Using Friendship Ties and Family Circles for Link Prediction -- Information Theoretic Criteria for Community Detection; ZDB-2-SCS; ZDB-2-LNC
N2  - This years volume of Advances in Social Network Analysis contains the p- ceedings for the Second International Workshop on Social Network Analysis (SNAKDD 2008). The annual workshop co-locates with the ACM SIGKDD - ternational Conference on Knowledge Discovery and Data Mining (KDD). The second SNAKDD workshop was held with KDD 2008 and received more than 32 submissions on social network mining and analysis topics. We accepted 11 regular papers and 8 short papers. Seven of the papers are included in this volume. In recent years, social network research has advanced signi?cantly, thanks to the prevalence of the online social websites and instant messaging systems as well as the availability of a variety of large-scale o?ine social network systems. These social network systems are usually characterized by the complex network structures and rich accompanying contextual information. Researchers are - creasingly interested in addressing a wide range of challenges residing in these disparate social network systems, including identifying common static topol- ical properties and dynamic properties during the formation and evolution of these social networks, and how contextual information can help in analyzing the pertaining socialnetworks.These issues haveimportant implications oncom- nitydiscovery,anomalydetection,trendpredictionandcanenhanceapplications in multiple domains such as information retrieval, recommendation systems, - curity and so on
UR  - http://dx.doi.org/10.1007/978-3-642-14929-0
ER  -