Individual and collective graph mining : principles, algorithms, and applications
- Cham : Springer, 2018
- xi, 194 p. ; ill., (some col.), 24 cm
- Synthesis Lectures on Data Mining and Knowledge Discovery ; v14 .
Includes bibliographical references.
Graphs naturally represent information ranging from links between web pages, to communication in email networks, to connections between neurons in our brains. These graphs often span billions of nodes and interactions between them. Within this deluge of interconnected data, how can we find the most important structures and summarize them? How can we efficiently visualize them? How can we detect anomalies that indicate critical events, such as an attack on a computer system, disease formation in the human brain, or the fall of a company? This book presents scalable, principled discovery algorithms that combine globality with locality to make sense of one or more graphs. In addition to fast algorithmic methodologies, we also contribute graph-theoretical ideas and models, and real-world applications in two main areas : Individual Graph Mining and Collective Graph Mining.
9783031007835
Data Mining Knowledge Discovery Statistics Graph Mining