Link Mining: Models, Algorithms, and Applications, 2010

This book presents in-depth surveys and systematic discussions on models, algorithms and applications for link mining. Link mining is an important field of data mining. Traditional data mining focuses on "flat" data in which each data object is represented as a fixed-length attribute vector. However, many real-world data sets are much richer in structure, involving objects of multiple types that are related to each other. Hence, recently link mining has become an emerging field of data mining, which has a high impact in various important applications such as text mining, social network analysis, collaborative filtering, and bioinformatics. At present, there are no books in the market focusing on the theory and techniques as well as the related applications for link mining. On the other hand, 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

Link-Based Clustering.- Machine Learning Approaches to Link-Based Clustering.- Scalable Link-Based Similarity Computation and Clustering.- Community Evolution and Change Point Detection in Time-Evolving Graphs.- Graph Mining and Community Analysis.- A Survey of Link Mining Tasks for Analyzing Noisy and Incomplete Networks.- Markov Logic: A Language and Algorithms for Link Mining.- Understanding Group Structures and Properties in Social Media.- Time Sensitive Ranking with Application to Publication Search.- Proximity Tracking on Dynamic Bipartite Graphs: Problem Definitions and Fast Solutions.- Discriminative Frequent Pattern-Based Graph Classification.- Link Analysis for Data Cleaning and Information Integration.- Information Integration for Graph Databases.- Veracity Analysis and Object Distinction.- Social Network Analysis.- Dynamic Community Identification.- Structure and Evolution of Online Social Networks.- Toward Identity Anonymization in Social Networks.- Summarization and OLAP of Information Networks.- Interactive Graph Summarization.- InfoNetOLAP: OLAP and Mining of Information Networks.- Integrating Clustering with Ranking in Heterogeneous Information Networks Analysis.- Mining Large Information Networks by Graph Summarization.- Analysis of Biological Information Networks.- Finding High-Order Correlations in High-Dimensional Biological Data.- Functional Influence-Based Approach to Identify Overlapping Modules in Biological Networks.- Gene Reachability Using Page Ranking on Gene Co-expression Networks.

Link mining has become an emerging field of data mining, which has a high impact in various important applications such as text mining, social network analysis, collaborative filtering, and bioinformatics This will be the first book on the market focusing on the theory and techniques as well as the related applications for link mining Presents in-depth surveys and systematic discussions on models, algorithms and applications for link mining Includes supplementary material: sn.pub/extras