Generating Random Networks and Graphs

Generating Random Networks and Graphs PDF Author: Anthony C. C. Coolen
Publisher: Oxford University Press
ISBN: 0198709897
Category : Computers
Languages : en
Pages : 325

Get Book Here

Book Description
This book describes how to correctly and efficiently generate random networks based on certain constraints. Being able to test a hypothesis against a properly specified control case is at the heart of the 'scientific method'.

Generating Random Networks and Graphs

Generating Random Networks and Graphs PDF Author: Anthony C. C. Coolen
Publisher: Oxford University Press
ISBN: 0198709897
Category : Computers
Languages : en
Pages : 325

Get Book Here

Book Description
This book describes how to correctly and efficiently generate random networks based on certain constraints. Being able to test a hypothesis against a properly specified control case is at the heart of the 'scientific method'.

Random Graph Dynamics

Random Graph Dynamics PDF Author: Rick Durrett
Publisher: Cambridge University Press
ISBN: 1139460889
Category : Mathematics
Languages : en
Pages : 203

Get Book Here

Book Description
The theory of random graphs began in the late 1950s in several papers by Erdos and Renyi. In the late twentieth century, the notion of six degrees of separation, meaning that any two people on the planet can be connected by a short chain of people who know each other, inspired Strogatz and Watts to define the small world random graph in which each site is connected to k close neighbors, but also has long-range connections. At a similar time, it was observed in human social and sexual networks and on the Internet that the number of neighbors of an individual or computer has a power law distribution. This inspired Barabasi and Albert to define the preferential attachment model, which has these properties. These two papers have led to an explosion of research. The purpose of this book is to use a wide variety of mathematical argument to obtain insights into the properties of these graphs. A unique feature is the interest in the dynamics of process taking place on the graph in addition to their geometric properties, such as connectedness and diameter.

Random Graphs and Complex Networks

Random Graphs and Complex Networks PDF Author: Remco van der Hofstad
Publisher: Cambridge University Press
ISBN: 110717287X
Category : Computers
Languages : en
Pages : 341

Get Book Here

Book Description
This classroom-tested text is the definitive introduction to the mathematics of network science, featuring examples and numerous exercises.

Graph Mining

Graph Mining PDF Author: Deepayan Chakrabarti
Publisher: Morgan & Claypool Publishers
ISBN: 160845116X
Category : Computers
Languages : en
Pages : 209

Get Book Here

Book Description
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

Graph Representation Learning

Graph Representation Learning PDF Author: William L. William L. Hamilton
Publisher: Springer Nature
ISBN: 3031015886
Category : Computers
Languages : en
Pages : 141

Get Book Here

Book Description
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

Handbook of Massive Data Sets

Handbook of Massive Data Sets PDF Author: James Abello
Publisher: Springer
ISBN: 1461500052
Category : Computers
Languages : en
Pages : 1209

Get Book Here

Book Description
The proliferation of massive data sets brings with it a series of special computational challenges. This "data avalanche" arises in a wide range of scientific and commercial applications. With advances in computer and information technologies, many of these challenges are beginning to be addressed by diverse inter-disciplinary groups, that indude computer scientists, mathematicians, statisticians and engineers, working in dose cooperation with application domain experts. High profile applications indude astrophysics, bio-technology, demographics, finance, geographi cal information systems, government, medicine, telecommunications, the environment and the internet. John R. Tucker of the Board on Mathe matical Seiences has stated: "My interest in this problern (Massive Data Sets) isthat I see it as the rnost irnportant cross-cutting problern for the rnathernatical sciences in practical problern solving for the next decade, because it is so pervasive. " The Handbook of Massive Data Sets is comprised of articles writ ten by experts on selected topics that deal with some major aspect of massive data sets. It contains chapters on information retrieval both in the internet and in the traditional sense, web crawlers, massive graphs, string processing, data compression, dustering methods, wavelets, op timization, external memory algorithms and data structures, the US national duster project, high performance computing, data warehouses, data cubes, semi-structured data, data squashing, data quality, billing in the large, fraud detection, and data processing in astrophysics, air pollution, biomolecular data, earth observation and the environment.

Generalized Blockmodeling

Generalized Blockmodeling PDF Author: Patrick Doreian
Publisher: Cambridge University Press
ISBN: 9780521840859
Category : Social Science
Languages : en
Pages : 410

Get Book Here

Book Description
This book provides an integrated treatment of generalized blockmodeling appropriate for the analysis network structures.

Computational Science - ICCS 2003

Computational Science - ICCS 2003 PDF Author: Peter M.A. Sloot
Publisher: Springer
ISBN: 3540448624
Category : Computers
Languages : en
Pages : 1164

Get Book Here

Book Description
The four-volume set LNCS 2657, LNCS 2658, LNCS 2659, and LNCS 2660 constitutes the refereed proceedings of the Third International Conference on Computational Science, ICCS 2003, held concurrently in Melbourne, Australia and in St. Petersburg, Russia in June 2003. The four volumes present more than 460 reviewed contributed and invited papers and span the whole range of computational science, from foundational issues in computer science and algorithmic mathematics to advanced applications in virtually all application fields making use of computational techniques. These proceedings give a unique account of recent results in the field.

Exploratory Social Network Analysis with Pajek

Exploratory Social Network Analysis with Pajek PDF Author: Wouter De Nooy
Publisher: Cambridge University Press
ISBN: 1108474144
Category : Language Arts & Disciplines
Languages : en
Pages : 487

Get Book Here

Book Description
Presents an analysis and visualization of social networks integrating theory, applications, and professional software for performing network analysis (Pajek).

Information Networking. Networking Technologies for Broadband and Mobile Networks

Information Networking. Networking Technologies for Broadband and Mobile Networks PDF Author: Hyun-Kook Kahng
Publisher: Springer
ISBN: 3540259783
Category : Computers
Languages : en
Pages : 1061

Get Book Here

Book Description
This book constitutes the thoroughly refereed post proceedings of the International Conference on Information Networking, ICOIN 2004, held in Busan, Korea, in February 2004. The 104 revised full papers presented were carefully selected during two rounds of reviewing and revision. The papers are organized in topical sections on mobile Internet and ubiquitous computing; QoS, measurement and performance analysis; high-speed network technologies; next generation Internet architecture; security; and Internet applications.