Individual and collective graph mining: principles, algorithms, and applications
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
[San Rafael, California]
Morgan & Claypool Publishers
[2018]
|
| Schriftenreihe: | Synthesis lectures on data mining and knowledge discovery
14 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | xi, 194 Seiten Illustrationen |
| ISBN: | 9781681730394 9781681732473 |
Internformat
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Datensatz im Suchindex
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|---|---|
| adam_text | Acknowledgments ...........................................................xi
1 Introduction....................................................................1
1.1 Overview ............................................................ 1
1.2 Organization of This Book..............................................2
1.2.1 Part I: Individual Graph Mining..................................2
1.2.2 Part II: Collective Graph Mining.................................3
1.2.3 Code and Supporting Materials on the Web ........................5
1.3 Preliminaries..........................................................5
1.3.1 Graph Definitions................................................5
1.3.2 Graph-theoretic Data Structures..................................8
1.3.3 Linear Algebra Concepts..........................................9
1.3.4 Select Graph Properties.........................................11
1.4 Common Symbols........................................................12
PART I individual Graph Mining....................................... 15
2 Summarization of Static Graphs..........................................17
2.1 Overview and Motivation..........................................18
2.2 Problem Formulation..............................................19
2.2.1 MDL for Graph Summarization................................21
2.2.2 Encoding the Model....................................... 23
2.2.3 Encoding the Errors........................................25
2.3 VoG: Vocabulary-based Summarization of Graphs....................25
2.3.1 Subgraph Generation........................................26
2.3.2 Subgraph Labeling..........................................26
2.3.3 Summary Assembly...........................................28
2.3.4 Toy Example................................................29
2.3.5 Time Complexity............................................29
2.4 Empirical Results................................................30
Vlll
2.4.1 Quantitative Analysis ............................................31
2.4.2 Qualitative Analysis.............................................35
2.4.3 Scalability.......................................................43
2.5 Discussion...............................................................44
2.6 Related Work.............................................................46
3 Inference in a Graph..............................................................49
3.1 Guilt-by-association Techniques..........................................50
3.1.1 Random Walk with Restarts (RWR)...................................50
3.1.2 Semi-supervised Learning (SSL)....................................51
3.1.3 Belief Propagation (BP)...........................................51
3.1.4 Summary...........................................................53
3.2 FaBP: Fast Belief Propagation............................................53
3.2.1 Derivation........................................................58
3.2.2 Analysis of Convergence...........................................63
3.2.3 Algorithm.........................................................64
3.3 Extension to Multiple Classes............................................65
3.4 Empirical Results........................................................68
3.4.1 Accuracy..........................................................69
3.4.2 Convergence.......................................................69
3.4.3 Robustness........................................................70
3.4.4 Scalability.......................................................70
PART II Collective Graph Mining....................................... 73
4 Summarization of Dynamic Graphs.........................................75
4.1 Problem Formulation...............................................77
4.1.1 MDL for Dynamic Graph Summarization..........................79
4.1.2 Encoding the Model...........................................80
4.1.3 Encoding the Errors..........................................81
4.2 TimeCrunch: Vocabulary-based Summarization of Dynamic Graphs......83
4.2.1 Generating Candidate Static Structures.......................83
4.2.2 Labeling Candidate Static Structures....................... 84
4.2.3 Stitching Candidate Temporal Structures......................84
4.2.4 Composing the Summary........................................86
4.3 Empirical Results........................................................87
4.3.1 Quantitative Analysis.............................................88
4.3.2 Qualitative Analysis .............................................90
4.3.3 Scalability.......................................................92
4.4 Related Work.............................................................93
5 Graph Similarity.............................................................97
5.1 Intuition................................................................97
5.1.1 Overview..........................................................99
5.1.2 Measuring Node Affinities.........................................99
5.1.3 Leveraging Belief Propagation....................................100
5.1.4 Desired Properties for Similarity Measures.......................101
5.2 DeltaCon: “ 5” Connectivity Change Detection............................102
5.2.1 Algorithm Description............................................102
5.2.2 Faster Computation ..............................................103
5.2.3 Desired Properties...............................................106
5.3 DeltaCon-Attr: Adding Node and Edge Attribution.........................112
5.3.1 Algorithm Description............................................113
5.3.2 Scalability Analysis.............................................115
5.4 Empirical Results.......................................................115
5.4.1 Intuitiveness of DeltaCon........................................115
5.4.2 Intuitiveness of DeltaCon-Attr ..................................123
5.4.3 Scalability......................................................130
5.4.4 Robustness.......................................................131
5.5 Applications............................................................132
5.5.1 Enron............................................................132
5.5.2 Brain Connectivity Graph Clustering..............................134
5.5.3 Recovery of Connectome Correspondences...........................135
5.6 Related Work............................................................138
6 Graph Alignment..............................................................143
6.1 Problem Formulation.....................................................144
6.2 BiG-Align: Bipartite Graph Alignment....................................146
6.2.1 Mathematical Formulation.........................................146
6.2.2 Problem-specific Optimizations...................................149
6.2.3 Algorithm Description............................................154
6.3 Uni-Align: Extension to Unipartite Graph Alignment......................154
6.4 Empirical Results...............................................157
6.4.1 Accuracy and Runtime of BiG-Align ........................157
6.4.2 Accuracy and Runtime of Uni-Align ........................161
6.5 Discussion......................................................163
6.6 Related Work....................................................163
7 Conclusions and Further Research Problems............................167
Bibliography.........................................................171
Authors’ Biographies.................................................193
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: We show how to interpretably summarize a single graph by identifying its
important graph structures. We complement summarization with inference, which leverages information about few
entities (obtained via summarization or other methods) and the network structure to efficiently and effectively learn
information about the unknown entities.
• Collective Graph Mining: We extend the idea of individual-graph summarization to time-evolving graphs,
and show how to scalably discover temporal patterns. Apart from summarization, we claim that graph similarity is
often the underlying problem in a host of applications where multiple graphs occur (e.g., temporal anomaly detection,
discovery of behavioral patterns), and we present principled, scalable algorithms for aligning networks and measuring
their similarity.
The methods that we present in this book leverage techniques from diverse areas, such as matrix algebra, graph
theory, optimization, information theory, machine learning, finance, and social science, to solve real-world problems.
We present applications of our exploration algorithms to massive datasets, including a Web graph of 6.6 billion edges,
a Twitter graph of 1.8 billion edges, brain graphs with up to 90 million edges, collaboration, peer-to-peer networks, and
browser logs, all spanning millions of users and interactions.
|
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| discipline | Informatik |
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| isbn | 9781681730394 9781681732473 |
| language | English |
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| spelling | Koutra, Danai Verfasser (DE-588)1161188754 aut Individual and collective graph mining principles, algorithms, and applications Danai Koutra (University of Michigan, Ann Arbor), Christos Faloutsos (Carnegie Mellon University) [San Rafael, California] Morgan & Claypool Publishers [2018] © 2018 xi, 194 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier Synthesis lectures on data mining and knowledge discovery 14 Graphentheorie (DE-588)4113782-6 gnd rswk-swf Graph (DE-588)4021842-9 gnd rswk-swf Datenverarbeitung (DE-588)4011152-0 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Graph (DE-588)4021842-9 s Data Mining (DE-588)4428654-5 s DE-604 Graphentheorie (DE-588)4113782-6 s Datenverarbeitung (DE-588)4011152-0 s Faloutsos, Christos Verfasser (DE-588)1161189874 aut Erscheint auch als Online-Ausgabe 978-1-68173-040-0 Synthesis lectures on data mining and knowledge discovery 14 (DE-604)BV044754814 14 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030487046&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030487046&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Koutra, Danai Faloutsos, Christos Individual and collective graph mining principles, algorithms, and applications Synthesis lectures on data mining and knowledge discovery Graphentheorie (DE-588)4113782-6 gnd Graph (DE-588)4021842-9 gnd Datenverarbeitung (DE-588)4011152-0 gnd Data Mining (DE-588)4428654-5 gnd |
| subject_GND | (DE-588)4113782-6 (DE-588)4021842-9 (DE-588)4011152-0 (DE-588)4428654-5 |
| title | Individual and collective graph mining principles, algorithms, and applications |
| title_auth | Individual and collective graph mining principles, algorithms, and applications |
| title_exact_search | Individual and collective graph mining principles, algorithms, and applications |
| title_full | Individual and collective graph mining principles, algorithms, and applications Danai Koutra (University of Michigan, Ann Arbor), Christos Faloutsos (Carnegie Mellon University) |
| title_fullStr | Individual and collective graph mining principles, algorithms, and applications Danai Koutra (University of Michigan, Ann Arbor), Christos Faloutsos (Carnegie Mellon University) |
| title_full_unstemmed | Individual and collective graph mining principles, algorithms, and applications Danai Koutra (University of Michigan, Ann Arbor), Christos Faloutsos (Carnegie Mellon University) |
| title_short | Individual and collective graph mining |
| title_sort | individual and collective graph mining principles algorithms and applications |
| title_sub | principles, algorithms, and applications |
| topic | Graphentheorie (DE-588)4113782-6 gnd Graph (DE-588)4021842-9 gnd Datenverarbeitung (DE-588)4011152-0 gnd Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Graphentheorie Graph Datenverarbeitung Data Mining |
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