Advances in Social Network Mining and Analysis Second International Workshop SNAKDD 2008 Las Vegas NV USA August 24 27 2008 Revised Selected Papers 1st edition by Lee Giles, ‎Marc Smith, ‎John Yen 9783642149290 3642149294
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Authors:C. Lee Giles; Marc Smith; John Yen; Haizheng Zhang , Series:IT & Computer [182] , Tags:Computers; Artificial Intelligence; General; Database Administration & Management; Data Science; Data Analytics; System Administration; Storage & Retrieval; Information Technology; Distributed Systems; Software Development & Engineering; Networking; Hardware; Springer 2010 , Author sort:Giles, C. Lee & Smith, Marc & Yen, John & Zhang, Haizheng , Ids:Google; 9783642149283 , Languages:Languages:eng , Published:Published:Aug 2010 , Publisher:Springer Science & Business Media , Comments:Comments:This year’s 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.
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ISBN 10: 3642149294
ISBN 13: 9783642149290
Author: C. Lee Giles; Marc Smith; John Yen
Advances in Social Network Mining and Analysis Second International Workshop SNAKDD 2008 Las Vegas NV USA August 24 27 2008 Revised Selected Papers 1st Table of contents:
Leveraging Label-Independent Features for Classification in Sparsely Labeled Networks: An Empirical
Introduction
Related Work
Label-Dependent vs. Label-Independent Features
Extracting Label-Independent Features
Experimental Design
Classifiers
Collective Classification
Experimental Methodology
Data Sets
Experimental Results
Effects of Learning Label Dependencies
Effects of Label-Independent Features
Performance of Specific LI Features
Observations about Our Problem Domains and Data Sets
Conclusion
References
Community Detection Using a Measure of Global Influence
Introduction
A Measure of Global Influence
Influence-Based Modularity
Detecting Community Structure
A Generalized Model of Influence
Evaluation
Zachary’s Karate Club
College Football
Political Books
Flickr Social Network
Related Research
Conclusion and Future Work
References
Communication Dynamics of Blog Networks
Introduction
Our Contributions
Clusters
Data
Stable Statistics
Modeling
A Locality Based Model
Locality Models
Attachment Models
Experiments and Results
Global Area Model
$k$-Neighborhood Area Model
Union of Clusters Area Model
Conclusion
References
Finding Spread Blockers in Dynamic Networks
Introduction
Related Work
Definitions
Dynamic Network
Aggregate Network
Spread Blockers
Network Structural Measures
Spreading Model
Experimental Setup
The Protocol
Probability of Activation
Lower Bound: Best Blockers
Datasets
Results and Discussion
Conclusions and Future Work
References
Social Network Mining with Nonparametric Relational Models
Introduction
ModelDescription
Hidden Relational Model
Infinite Hidden Relational Model
Generative Model
Inference
Inference with Gibbs Sampling
Inference with Variational Approximation
Experimental Analysis
Monastery Data
Bernard & Killworth Data
MovieLens Data
Related Work
Extension: Conditional IHRM
Conclusions
References
Using Friendship Ties and Family Circles for Link Prediction
Introduction
Link Prediction Problem
SocialNetworkModel
A Feature Taxonomy for Multi-relational Social Networks
Descriptive Attributes
Structural Features
Group Features
Alternative Network Representations
Experimental Evaluation
Social Media Data Sets
Data Description
Experimental setup
Link-Prediction Results
Related Work
Discussion
Conclusions and Future Work
References
Information Theoretic Criteria for Community Detection
Introduction
Minimum Description Length Encodings
Sparse Graph Encoding (SGE)
Empirical Evaluation
Evaluation Criteri
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