Link Mining: Models, Algorithms, and Applications

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; people need such a reference book to systematically apply the link mining techniques to these applications to develop the related technologies. Therefore, such a book is in high demand on the market.

Table of Contents

Part I Link-Based Clustering
Chapter 1 Machine Learning Approaches to Link-Based Clustering
Chapter 2 Scalable Link-Based Similarity Computation and Clustering
Chapter 3 Community Evolution and Change Point Detection in Time-Evolving Graphs

Chapter Part II Graph Mining and Community Analysis
Chapter 4 A Survey of Link Mining Tasks for Analyzing Noisy and Incomplete Networks
Chapter 5 Markov Logic: A Language and Algorithms for Link Mining
Chapter 6 Understanding Group Structures and Properties in Social Media
Chapter 7 Time Sensitive Ranking with Application to Publication Search
Chapter 8 Proximity Tracking on Dynamic Bipartite Graphs: Problem Definitions and Fast Solutions
Chapter 9 Discriminative Frequent Pattern-Based Graph Classification

Chapter Part III Link Analysis for Data Cleaning and Information Integration
Chapter 10 Information Integration for Graph Databases
Chapter 11 Veracity Analysis and Object Distinction

Part IV Social Network Analysis
Chapter 12 Dynamic Community Identification
Chapter 13 Structure and Evolution of Online Social Networks
Chapter 14 Toward Identity Anonymization in Social Networks

Part V Summarization and OLAP of Information Networks
Chapter 15 Interactive Graph Summarization
Chapter 16 InfoNetOLAP: OLAP and Mining of Information Networks
Chapter 17 Integrating Clustering with Ranking in Heterogeneous Information Networks Analysis
Chapter 18 Mining Large Information Networks by Graph Summarization

Part VI Analysis of Biological Information Networks
Chapter 19 Finding High-Order Correlations in High-Dimensional Biological Data
Chapter 20 Functional Influence-Based Approach to Identify Overlapping Modules in Biological Networks
Chapter 21 Gene Reachability Using Page Ranking on Gene Co-expression Networks