With the recent flourishing research activities on Web search and mining, social network analysis, information network analysis, information retrieval, link analysis, and structural data mining, research on link mining has been rapidly growing, forming a new field of data mining. Traditional data mining focuses on "flat" or “isolated” data in which each data object is represented as an independent attribute vector. However, many real-world data sets are inter-connected, much richer in structure, involving objects of heterogeneous types and complex links. Hence, the study of link mining will have a high impact in various important applications such as Web and text mining, social network analysis, collaborative filtering, and bioinformatics. Link Mining: Models, Algorithms and Applications focuses on the theory and techniques as well as the related applications for link mining, especially from an interdisciplinary point of view. Due to the high popularity of linkage data, extensive applications ranging from governmental organizations to commercial businesses to people's daily life call for exploring the techniques of mining linkage data. This book provides a comprehensive coverage of the link mining models, techniques and applications. Each chapter is contributed from some well known researchers in the field. Link Mining: Models, Algorithms and Applications is designed for researchers, teachers, and advanced-level students in computer science. This book is also suitable for practitioners in industry.
Author(s): Zhongfei (Mark) Zhang, Bo Long, Zhen Guo, Tianbing Xu, Philip S. Yu (auth.), Philip S. Yu, Jiawei Han, Christos Faloutsos (eds.)
Edition: 1
Publisher: Springer-Verlag New York
Year: 2010
Language: English
Pages: 586
Tags: Bioinformatics; Data Mining and Knowledge Discovery; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences
Front Matter....Pages i-xiii
Front Matter....Pages 1-1
Machine Learning Approaches to Link-Based Clustering....Pages 3-44
Scalable Link-Based Similarity Computation and Clustering....Pages 45-71
Community Evolution and Change Point Detection in Time-Evolving Graphs....Pages 73-104
Front Matter....Pages 105-105
A Survey of Link Mining Tasks for Analyzing Noisy and Incomplete Networks....Pages 107-133
Markov Logic: A Language and Algorithms for Link Mining....Pages 135-161
Understanding Group Structures and Properties in Social Media....Pages 163-185
Time Sensitive Ranking with Application to Publication Search....Pages 187-209
Proximity Tracking on Dynamic Bipartite Graphs: Problem Definitions and Fast Solutions....Pages 211-236
Discriminative Frequent Pattern-Based Graph Classification....Pages 237-262
Front Matter....Pages 263-263
Information Integration for Graph Databases....Pages 265-281
Veracity Analysis and Object Distinction....Pages 283-304
Front Matter....Pages 305-305
Dynamic Community Identification....Pages 307-336
Structure and Evolution of Online Social Networks....Pages 337-357
Toward Identity Anonymization in Social Networks....Pages 359-385
Front Matter....Pages 387-387
Interactive Graph Summarization....Pages 389-409
InfoNetOLAP: OLAP and Mining of Information Networks....Pages 411-438
Integrating Clustering with Ranking in Heterogeneous Information Networks Analysis....Pages 439-473
Mining Large Information Networks by Graph Summarization....Pages 475-501
Front Matter....Pages 503-503
Finding High-Order Correlations in High-Dimensional Biological Data....Pages 505-534
Functional Influence-Based Approach to Identify Overlapping Modules in Biological Networks....Pages 535-556
Front Matter....Pages 503-503
Gene Reachability Using Page Ranking on Gene Co-expression Networks....Pages 557-568
Back Matter....Pages 569-586
