Network models for data science: theory, algorithms, and applications
"This text on the theory and applications of network science is aimed at beginning graduate students in statistics, data science, computer science, machine learning, and mathematics, as well as advanced students in business, computational biology, physics, social science, and engineering workin...
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Cambridge, United Kingdom ; New York, NY, USA ; Port Melbourne, VIC, Australia ; New Delhi, India ; Singapore
Cambridge University Press
2023
|
| Edition: | First published |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Summary: | "This text on the theory and applications of network science is aimed at beginning graduate students in statistics, data science, computer science, machine learning, and mathematics, as well as advanced students in business, computational biology, physics, social science, and engineering working with large, complex relational data sets. It provides an exciting array of analysis tools, including probability models, graph theory, and computational algorithms, exposing students to ways of thinking about types of data that are different from typical statistical data. Concepts are demonstrated in the context of real applications, such as relationships between financial institutions, between genes or proteins, between neurons in the brain, and between terrorist groups. Methods and models described in detail include random graph models, percolation processes, methods for sampling from huge networks, network partitioning, and community detection. In addition to static networks the book introduces dynamic networks such as epidemics, where time is an important component"-- |
| Item Description: | Literaturverzeichnis: Seite 441-473 |
| Physical Description: | xv, 484 Seiten Illustrationen, Diagramme, Karten |
| ISBN: | 9781108835763 |
Staff View
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| adam_text | Contents page xi Preface Introduction and Preview 1.1 1.2 1.3 1.4 1.5 1.6 1.7 What is a Network? Why Study Networks? A Little Bit of History Building Network Models Discovering Network Structure Preliminaries Further Reading 16 16 16 20 25 32 42 49 51 53 54 Examples of Networks 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 Introduction Technological Networks Information Networks Financial Networks Social Networks Biological Networks Ecological Networks Terrorist Networks Further Reading Exercises Graphs and Networks 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 3.11 1 1 2 4 8 10 11 15 Introduction Definitions The Adjacency Matrix The Incidence Matrix Paths and Connectivity Two Huge Real-World Networks Clusters and Hubs Graph-Searching Algorithms Minimum Spanning Trees Further Reading Exercises vii 56 56 56 61 62 63 65 67 70 73 73 73
CONTENTS 4 4.1 4.2 4.3 4.4 4.5 4.6 4.7 5 6 76 77 78 87 95 103 103 105 5.1 Introduction 5.2 Examples of Percolation 5.3 Discrete Percolation 5.4 Subcriticai Phase 5.5 Supercritical Phase 5.6 Critical Point 5.7 Power-Law Conjectures 5.8 The Number of Infinite Clusters 5.9 Further Reading 5.10 Exercises 105 106 112 116 120 120 121 128 134 134 Percolation Beyond Z¿ 136 Introduction Example: Polymer Gelation Percolation on Trees Percolation on Transitive Graphs Continuum Percolation Further Reading Exercises Introduction It’s a Small World The Watts-Strogatz Model Degree Distributions The Power Law and Scale-Free Networks Properties of a Scale-Free Network Further Reading Exercises Models of Network Evolution and Growth 8.1 8.2 8.3 8.4 8.5 8.6 8.7 136 136 138 144 149 158 158 161 The Topology of Networks 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 8 Introduction Erdös-Rényi Random Graphs Technical Tools Graph Properties Giant Component Further Reading Exercises Percolation on Z4 6.1 6.2 6.3 6.4 6.5 6.6 6.7 7 76 Random Graph Models Introduction The Configuration Model Expected-Degree Random Graph Preferential Attachment Random Copying Further Reading Exercises viii 161 161 164 169 171 173 180 180 182 182 183 199 201 212 215 216
CONTENTS 9 218 Network Sampling 9.1 9.2 9.3 9.4 9.5 9.6 9.7 Introduction Objectives for Network Sampling Sampling of Nodes Sampling of Edges Sampling by Network Exploration Further Reading Exercises 239 10 Parametric Network Models 10.1 10.2 10.3 10.4 10.5 10.6 10.7 10.8 10.9 218 220 221 225 226 237 238 Introduction Exponential Families The p Model The pz Model Markov Random Graphs Exponential Random Graph Models Latent Space Models Further Reading Exercises 239 239 241 248 254 257 265 269 270 11 Graph Partitioning: I. Graph Cuts 272 Introduction Binary Cuts Multiway Cuts Further Reading Exercises 272 274 290 293 293 11.1 11.2 11.3 11.4 11.5 12 Graph Partitioning: II. Community Detection 12.1 12.2 12.3 12.4 12.5 12.6 12.7 12.8 12.9 12.10 Introduction Equivalence Concepts Deterministic Blockmodels Stochastic Blockmodels Modularity Optimizing Modularity in Large Networks Consistency Sampling for Network Structure Further Reading Exercises 13 Graph Partitioning: III. Spectral Clustering 294 294 295 297 300 317 332 338 340 344 344 346 Introduction Graph Laplacians Spectral Clustering of Networks Regularized Spectral Clustering Further Reading Exercises 346 346 350 356 359 359 14 Graph Partitioning: IV. Overlapping Communities 362 13.1 13.2 13.3 13.4 13.5 13.6 14.1 362 Introduction ix
CONTENTS 14.2 14.3 14.4 14.5 14.6 Mixed-Membership SBMs Alternative Algorithms Latent Cluster Random-Effects Model Further Reading Exercises 15 Examining Network Properties 15.1 15.2 15.3 15.4 15.5 15.6 15.7 Introduction Similarity Measures for Large Networks Exchangeable Random Structures Homomorphisms and Isomorphisms Property Testing in Networks Further Reading Exercises 16 Graphons as Limits of Networks 16.1 16.2 16.3 16.4 16.5 16.6 16.7 Introduction Kernels and Graphons Szemerédi’s Regularity Lemma Sampling of Graphons Graphon Estimation Further Reading Exercises 17 Dynamic Networks 17.1 17.2 17.3 17.4 17.5 17.6 17.7 17.8 Introduction Networks with a Time Component Dynamic Community Discovery Longitudinal Social Networks Graph Distances for Comparing Networks Dynamic Biological Networks Further Reading Exercises References Index of Examples Subject Index 363 366 371 372 372 374 374 375 378 385 388 391 391 393 393 395 399 401 403 409 409 411 411 412 415 420 422 425 438 439 441 475 477
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| adam_txt |
Contents page xi Preface Introduction and Preview 1.1 1.2 1.3 1.4 1.5 1.6 1.7 What is a Network? Why Study Networks? A Little Bit of History Building Network Models Discovering Network Structure Preliminaries Further Reading 16 16 16 20 25 32 42 49 51 53 54 Examples of Networks 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 Introduction Technological Networks Information Networks Financial Networks Social Networks Biological Networks Ecological Networks Terrorist Networks Further Reading Exercises Graphs and Networks 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 3.11 1 1 2 4 8 10 11 15 Introduction Definitions The Adjacency Matrix The Incidence Matrix Paths and Connectivity Two Huge Real-World Networks Clusters and Hubs Graph-Searching Algorithms Minimum Spanning Trees Further Reading Exercises vii 56 56 56 61 62 63 65 67 70 73 73 73
CONTENTS 4 4.1 4.2 4.3 4.4 4.5 4.6 4.7 5 6 76 77 78 87 95 103 103 105 5.1 Introduction 5.2 Examples of Percolation 5.3 Discrete Percolation 5.4 Subcriticai Phase 5.5 Supercritical Phase 5.6 Critical Point 5.7 Power-Law Conjectures 5.8 The Number of Infinite Clusters 5.9 Further Reading 5.10 Exercises 105 106 112 116 120 120 121 128 134 134 Percolation Beyond Z¿ 136 Introduction Example: Polymer Gelation Percolation on Trees Percolation on Transitive Graphs Continuum Percolation Further Reading Exercises Introduction It’s a Small World The Watts-Strogatz Model Degree Distributions The Power Law and Scale-Free Networks Properties of a Scale-Free Network Further Reading Exercises Models of Network Evolution and Growth 8.1 8.2 8.3 8.4 8.5 8.6 8.7 136 136 138 144 149 158 158 161 The Topology of Networks 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 8 Introduction Erdös-Rényi Random Graphs Technical Tools Graph Properties Giant Component Further Reading Exercises Percolation on Z4 6.1 6.2 6.3 6.4 6.5 6.6 6.7 7 76 Random Graph Models Introduction The Configuration Model Expected-Degree Random Graph Preferential Attachment Random Copying Further Reading Exercises viii 161 161 164 169 171 173 180 180 182 182 183 199 201 212 215 216
CONTENTS 9 218 Network Sampling 9.1 9.2 9.3 9.4 9.5 9.6 9.7 Introduction Objectives for Network Sampling Sampling of Nodes Sampling of Edges Sampling by Network Exploration Further Reading Exercises 239 10 Parametric Network Models 10.1 10.2 10.3 10.4 10.5 10.6 10.7 10.8 10.9 218 220 221 225 226 237 238 Introduction Exponential Families The p\ Model The pz Model Markov Random Graphs Exponential Random Graph Models Latent Space Models Further Reading Exercises 239 239 241 248 254 257 265 269 270 11 Graph Partitioning: I. Graph Cuts 272 Introduction Binary Cuts Multiway Cuts Further Reading Exercises 272 274 290 293 293 11.1 11.2 11.3 11.4 11.5 12 Graph Partitioning: II. Community Detection 12.1 12.2 12.3 12.4 12.5 12.6 12.7 12.8 12.9 12.10 Introduction Equivalence Concepts Deterministic Blockmodels Stochastic Blockmodels Modularity Optimizing Modularity in Large Networks Consistency Sampling for Network Structure Further Reading Exercises 13 Graph Partitioning: III. Spectral Clustering 294 294 295 297 300 317 332 338 340 344 344 346 Introduction Graph Laplacians Spectral Clustering of Networks Regularized Spectral Clustering Further Reading Exercises 346 346 350 356 359 359 14 Graph Partitioning: IV. Overlapping Communities 362 13.1 13.2 13.3 13.4 13.5 13.6 14.1 362 Introduction ix
CONTENTS 14.2 14.3 14.4 14.5 14.6 Mixed-Membership SBMs Alternative Algorithms Latent Cluster Random-Effects Model Further Reading Exercises 15 Examining Network Properties 15.1 15.2 15.3 15.4 15.5 15.6 15.7 Introduction Similarity Measures for Large Networks Exchangeable Random Structures Homomorphisms and Isomorphisms Property Testing in Networks Further Reading Exercises 16 Graphons as Limits of Networks 16.1 16.2 16.3 16.4 16.5 16.6 16.7 Introduction Kernels and Graphons Szemerédi’s Regularity Lemma Sampling of Graphons Graphon Estimation Further Reading Exercises 17 Dynamic Networks 17.1 17.2 17.3 17.4 17.5 17.6 17.7 17.8 Introduction Networks with a Time Component Dynamic Community Discovery Longitudinal Social Networks Graph Distances for Comparing Networks Dynamic Biological Networks Further Reading Exercises References Index of Examples Subject Index 363 366 371 372 372 374 374 375 378 385 388 391 391 393 393 395 399 401 403 409 409 411 411 412 415 420 422 425 438 439 441 475 477 |
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| index_date | 2024-07-03T22:07:36Z |
| indexdate | 2024-07-10T09:52:13Z |
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| isbn | 9781108835763 |
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| spelling | Izenman, Alan Julian Verfasser (DE-588)1243686030 aut Network models for data science theory, algorithms, and applications Alan Julian Izenman, Temple University Philadelphia First published Cambridge, United Kingdom ; New York, NY, USA ; Port Melbourne, VIC, Australia ; New Delhi, India ; Singapore Cambridge University Press 2023 xv, 484 Seiten Illustrationen, Diagramme, Karten txt rdacontent n rdamedia nc rdacarrier Literaturverzeichnis: Seite 441-473 "This text on the theory and applications of network science is aimed at beginning graduate students in statistics, data science, computer science, machine learning, and mathematics, as well as advanced students in business, computational biology, physics, social science, and engineering working with large, complex relational data sets. It provides an exciting array of analysis tools, including probability models, graph theory, and computational algorithms, exposing students to ways of thinking about types of data that are different from typical statistical data. Concepts are demonstrated in the context of real applications, such as relationships between financial institutions, between genes or proteins, between neurons in the brain, and between terrorist groups. Methods and models described in detail include random graph models, percolation processes, methods for sampling from huge networks, network partitioning, and community detection. In addition to static networks the book introduces dynamic networks such as epidemics, where time is an important component"-- Mathematisches Modell (DE-588)4114528-8 gnd rswk-swf Systemanalyse (DE-588)4116673-5 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Netzwerk Graphentheorie (DE-588)4705155-3 gnd rswk-swf System analysis Mathematical models MATHEMATICS / Probability & Statistics / General Netzwerk Graphentheorie (DE-588)4705155-3 s Systemanalyse (DE-588)4116673-5 s Mathematisches Modell (DE-588)4114528-8 s Datenanalyse (DE-588)4123037-1 s DE-604 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034255541&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Izenman, Alan Julian Network models for data science theory, algorithms, and applications Mathematisches Modell (DE-588)4114528-8 gnd Systemanalyse (DE-588)4116673-5 gnd Datenanalyse (DE-588)4123037-1 gnd Netzwerk Graphentheorie (DE-588)4705155-3 gnd |
| subject_GND | (DE-588)4114528-8 (DE-588)4116673-5 (DE-588)4123037-1 (DE-588)4705155-3 |
| title | Network models for data science theory, algorithms, and applications |
| title_auth | Network models for data science theory, algorithms, and applications |
| title_exact_search | Network models for data science theory, algorithms, and applications |
| title_exact_search_txtP | Network models for data science theory, algorithms, and applications |
| title_full | Network models for data science theory, algorithms, and applications Alan Julian Izenman, Temple University Philadelphia |
| title_fullStr | Network models for data science theory, algorithms, and applications Alan Julian Izenman, Temple University Philadelphia |
| title_full_unstemmed | Network models for data science theory, algorithms, and applications Alan Julian Izenman, Temple University Philadelphia |
| title_short | Network models for data science |
| title_sort | network models for data science theory algorithms and applications |
| title_sub | theory, algorithms, and applications |
| topic | Mathematisches Modell (DE-588)4114528-8 gnd Systemanalyse (DE-588)4116673-5 gnd Datenanalyse (DE-588)4123037-1 gnd Netzwerk Graphentheorie (DE-588)4705155-3 gnd |
| topic_facet | Mathematisches Modell Systemanalyse Datenanalyse Netzwerk Graphentheorie |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034255541&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT izenmanalanjulian networkmodelsfordatasciencetheoryalgorithmsandapplications |