Advances in Social Network Mining and Analysis Second International Workshop SNAKDD 2008 Las Vegas NV USA August 24 27 2008 Revised Selected Papers Computer Science and General Issues 1st Edition by C Lee Giles, Marc Smith, John Yen, Haizheng Zhang ISBN 3642149286 9783642149283

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ISBN 10: 3642149286 
ISBN 13: 9783642149283
Author: C Lee Giles, Marc Smith, John Yen, Haizheng Zhang

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.

Advances in Social Network Mining and Analysis Second International Workshop SNAKDD 2008 Las Vegas NV USA August 24 27 2008 Revised Selected Papers Computer Science and General Issues 1st Table of contents:

1. Leveraging Label-Independent Features for Classification in Sparsely Labeled Networks: An Empirical
2. Introduction
3. Related Work
4. Label-Dependent vs. Label-Independent Features
5. Extracting Label-Independent Features
6. Experimental Design
7. Classifiers
8. Collective Classification
9. Experimental Methodology
10. Data Sets
11. Experimental Results
12. Effects of Learning Label Dependencies
13. Effects of Label-Independent Features
14. Performance of Specific LI Features
15. Observations about Our Problem Domains and Data Sets
16. Conclusion
17. References
18. Community Detection Using a Measure of Global Influence
19. Introduction
20. A Measure of Global Influence
21. Influence-Based Modularity
22. Detecting Community Structure
23. A Generalized Model of Influence
24. Evaluation
25. Zachary’s Karate Club
26. College Football
27. Political Books
28. Flickr Social Network
29. Related Research
30. Conclusion and Future Work
31. References
32. Communication Dynamics of Blog Networks
33. Introduction
34. Our Contributions
35. Clusters
36. Data
37. Stable Statistics
38. Modeling
39. A Locality Based Model
40. Locality Models
41. Attachment Models
42. Experiments and Results
43. Global Area Model
44. $k$-Neighborhood Area Model
45. Union of Clusters Area Model
46. Conclusion
47. References
48. Finding Spread Blockers in Dynamic Networks
49. Introduction
50. Related Work
51. Definitions
52. Dynamic Network
53. Aggregate Network
54. Spread Blockers
55. Network Structural Measures
56. Spreading Model
57. Experimental Setup
58. The Protocol
59. Probability of Activation
60. Lower Bound: Best Blockers
61. Datasets
62. Results and Discussion
63. Conclusions and Future Work
64. References
65. Social Network Mining with Nonparametric Relational Models
66. Introduction
67. ModelDescription
68. Hidden Relational Model
69. Infinite Hidden Relational Model
70. Generative Model
71. Inference
72. Inference with Gibbs Sampling
73. Inference with Variational Approximation
74. Experimental Analysis
75. Monastery Data
76. Bernard & Killworth Data
77. MovieLens Data
78. Related Work
79. Extension: Conditional IHRM
80. Conclusions
81. References
82. Using Friendship Ties and Family Circles for Link Prediction
83. Introduction
84. Link Prediction Problem
85. SocialNetworkModel
86. A Feature Taxonomy for Multi-relational Social Networks
87. Descriptive Attributes
88. Structural Features
89. Group Features
90. Alternative Network Representations
91. Experimental Evaluation
92. Social Media Data Sets
93. Data Description
94. Experimental setup
95. Link-Prediction Results
96. Related Work
97. Discussion
98. Conclusions and Future Work
99. References
100. Information Theoretic Criteria for Community Detection
101. Introduction
102. Minimum Description Length Encodings
103. Sparse Graph Encoding (SGE)
104. Empirical Evaluation
105. Evaluation Criteria
106. Experimental Procedure
107. Conclusion

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