Graph Mining by Deepayan Chakrabarti
What does the Web look like? How can we find patterns, communities, outliers, in a social network? Which are the most central nodes in a network? These are the questions that motivate this work. Networks and graphs appear in many diverse settings, for example in social networks, computer-communication networks (intrusion detection, traffic management), protein-protein interaction networks in biology, document-text bipartite graphs in text retrieval, person-account graphs in financial fraud detection, and others. In this work, first we list several surprising patterns that real graphs tend to follow. Then we give a detailed list of generators that try to mirror these patterns. Generators are important, because they can help with "what if" scenarios, extrapolations, and anonymization. Then we provide a list of powerful tools for graph analysis, and specifically spectral methods (Singular Value Decomposition (SVD)), tensors, and case studies like the famous "pageRank" algorithm and the "HITS" algorithm for ranking web search results. Finally, we conclude with a survey of tools and observations from related fields like sociology, which provide complementary viewpoints. Table of Contents: Introduction / Patterns in Static Graphs / Patterns in Evolving Graphs / Patterns in Weighted Graphs / Discussion: The Structure of Specific Graphs / Discussion: Power Laws and Deviations / Summary of Patterns / Graph Generators / Preferential Attachment and Variants / Incorporating Geographical Information / The RMat / Graph Generation by Kronecker Multiplication / Summary and Practitioner's Guide / SVD, Random Walks, and Tensors / Tensors / Community Detection / Influence/Virus Propagation and Immunization / Case Studies / Social Networks / Other Related Work / Conclusions-
Deep Learning for Polymer Discovery
-
Provenance Data in Social Media
-
Mining Structures of Factual Knowledge from Text
-
Exploratory Causal Analysis with Time Series Data
-
Probabilistic Approaches to Recommendations
-
Detecting Fake News on Social Media
-
Phrase Mining from Massive Text and Its Applications
-
Modeling and Data Mining in Blogosphere
-
Outlier Detection for Temporal Data
-
Privacy in Social Networks
-
Correlation Clustering
-
Exploiting the Power of Group Differences
-
Multidimensional Mining of Massive Text Data
-
Individual and Collective Graph Mining
-
Automated Taxonomy Discovery and Exploration
-
Advances in Graph Neural Networks
-
Mining Human Mobility in Location-Based Social Networks
-
Ensemble Methods in Data Mining
-
Mining Heterogeneous Information Networks
-
Community detection and mining in social media
-
Mining Latent Entity Structures
Dr. Deepayan Chakrabarti obtained his Ph.D. from Carnegie Mellon University in 2005. He was a Senior Research Scientist with Yahoo, and now with Facebook Inc. He has published over 35 refereed articles and is the co-inventor of the RMat graph generator (the basis of the graph500 supercomputer benchmark). He is the co-inventor in over 20 patents (issued or pending). He has given tutorials in CIKM and KDD, and his interests include graph mining, computational advertising, and web search. Christos Faloutsos is a Professor at Carnegie Mellon University and an ACM Fellow. He has received the Research Contributions Award in ICDM 2006, the SIGKDD Innovations Award (2010), 18 "best paper" awards (including two "test of time" awards), and four teaching awards. He has published over 200 refereed articles, and has given over 30 tutorials. His research interests include data mining for graphs and streams, fractals, and self-similarity, database performance, and indexing for multimedia and bio-informatics data.
| SKU | Unavailable |
| ISBN 13 | 9783031007750 |
| ISBN 10 | 3031007751 |
| Title | Graph Mining |
| Author | Deepayan Chakrabarti |
| Series | Synthesis Lectures On Data Mining And Knowledge Discovery |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer International Publishing AG |
| Year published | 2012-10-18 |
| Number of pages | 191 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |