Springer Link Mining: Models, Algorithms, and Applications

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.