Broad Learning Through Fusions: An Application on Social Networks

Description

This book offers a clear and comprehensive introduction to broad learning, one of the novel learning problems studied in data mining and machine learning. Broad learning aims at fusing multiple large-scale information sources of diverse varieties together, and carrying out synergistic data mining tasks across these fused sources in one unified analytic. This book takes online social networks as an application example to introduce the latest alignment and knowledge discovery algorithms. Besides the overview of broad learning, machine learning and social network basics, specific topics covered in this book include network alignment, link prediction, community detection, information diffusion, viral marketing, and network embedding. Front Matter ....Pages i-xv Front Matter ....Pages 1-1 Broad Learning Introduction (Jiawei Zhang, Philip S. Yu)....Pages 3-17 Machine Learning Overview (Jiawei Zhang, Philip S. Yu)....Pages 19-75 Social Network Overview (Jiawei Zhang, Philip S. Yu)....Pages 77-126 Front Matter ....Pages 127-127 Supervised Network Alignment (Jiawei Zhang, Philip S. Yu)....Pages 129-164 Unsupervised Network Alignment (Jiawei Zhang, Philip S. Yu)....Pages 165-202 Semi-supervised Network Alignment (Jiawei Zhang, Philip S. Yu)....Pages 203-226 Front Matter ....Pages 227-227 Link Prediction (Jiawei Zhang, Philip S. Yu)....Pages 229-273 Community Detection (Jiawei Zhang, Philip S. Yu)....Pages 275-314 Information Diffusion (Jiawei Zhang, Philip S. Yu)....Pages 315-349 Viral Marketing (Jiawei Zhang, Philip S. Yu)....Pages 351-384 Network Embedding (Jiawei Zhang, Philip S. Yu)....Pages 385-413 Front Matter ....Pages 415-415 Frontier and Future Directions (Jiawei Zhang, Philip S. Yu)....Pages 417-419