A STUDY ON SIMILARITY JOIN IN HETEROGENEOUS INFORMATION NETWORK USING PATH BASED ALGORITHM
Author: S. Sandhya
Publisher: Archers & Elevators Publishing House
Published:
Total Pages: 75
ISBN-13: 8119385004
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Web Information Systems and Applications
Author: Weiwei Ni
Publisher: Springer Nature
Published: 2019-09-17
Total Pages: 725
ISBN-13: 3030309525
DOWNLOAD EBOOKThis book constitutes the proceedings of the 16th International Conference on Web Information Systems and Applications, WISA 2019, held in Qingdao, China, in September 2019. The 39 revised full papers and 33 short papers presented were carefully reviewed and selected from 154 submissions. The papers are grouped in topical sections on machine learning and data mining, cloud computing and big data, information retrieval, natural language processing, data privacy and security, knowledge graphs and social networks, blockchain, query processing, and recommendations.
Mining Heterogeneous Information Networks
Author: Yizhou Sun
Publisher: Morgan & Claypool Publishers
Published: 2012
Total Pages: 162
ISBN-13: 1608458806
DOWNLOAD EBOOKInvestigates the principles and methodologies of mining heterogeneous information networks. Departing from many existing network models that view interconnected data as homogeneous graphs or networks, the semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and uncovers surprisingly rich knowledge from the network.
Modern Principles, Practices, and Algorithms for Cloud Security
Author: Gupta, Brij B.
Publisher: IGI Global
Published: 2019-09-27
Total Pages: 361
ISBN-13: 1799810844
DOWNLOAD EBOOKIn today’s modern age of information, new technologies are quickly emerging and being deployed into the field of information technology. Cloud computing is a tool that has proven to be a versatile piece of software within IT. Unfortunately, the high usage of Cloud has raised many concerns related to privacy, security, and data protection that have prevented cloud computing solutions from becoming the prevalent alternative for mission critical systems. Up-to-date research and current techniques are needed to help solve these vulnerabilities in cloud computing. Modern Principles, Practices, and Algorithms for Cloud Security is a pivotal reference source that provides vital research on the application of privacy and security in cloud computing. While highlighting topics such as chaos theory, soft computing, and cloud forensics, this publication explores present techniques and methodologies, as well as current trends in cloud protection. This book is ideally designed for IT specialists, scientists, software developers, security analysts, computer engineers, academicians, researchers, and students seeking current research on the defense of cloud services.
Network Embedding
Author: Cheng Yang
Publisher: Morgan & Claypool Publishers
Published: 2021-03-25
Total Pages: 244
ISBN-13: 1636390455
DOWNLOAD EBOOKThis is a comprehensive introduction to the basic concepts, models, and applications of network representation learning (NRL) and the background and rise of network embeddings (NE). It introduces the development of NE techniques by presenting several representative methods on general graphs, as well as a unified NE framework based on matrix factorization. Afterward, it presents the variants of NE with additional information: NE for graphs with node attributes/contents/labels; and the variants with different characteristics: NE for community-structured/large-scale/heterogeneous graphs. Further, the book introduces different applications of NE such as recommendation and information diffusion prediction. Finally, the book concludes the methods and applications and looks forward to the future directions. Many machine learning algorithms require real-valued feature vectors of data instances as inputs. By projecting data into vector spaces, representation learning techniques have achieved promising performance in many areas such as computer vision and natural language processing. There is also a need to learn representations for discrete relational data, namely networks or graphs. Network Embedding (NE) aims at learning vector representations for each node or vertex in a network to encode the topologic structure. Due to its convincing performance and efficiency, NE has been widely applied in many network applications such as node classification and link prediction.
Web and Big Data
Author: Xin Wang
Publisher: Springer Nature
Published: 2020-10-15
Total Pages: 829
ISBN-13: 3030602591
DOWNLOAD EBOOKThis two-volume set, LNCS 11317 and 12318, constitutes the thoroughly refereed proceedings of the 4th International Joint Conference, APWeb-WAIM 2020, held in Tianjin, China, in September 2020. Due to the COVID-19 pandemic the conference was organizedas a fully online conference. The 42 full papers presented together with 17 short papers, and 6 demonstration papers were carefully reviewed and selected from 180 submissions. The papers are organized around the following topics: Big Data Analytics; Graph Data and Social Networks; Knowledge Graph; Recommender Systems; Information Extraction and Retrieval; Machine Learning; Blockchain; Data Mining; Text Analysis and Mining; Spatial, Temporal and Multimedia Databases; Database Systems; and Demo.
Expertise Retrieval
Author: Krisztian Balog
Publisher: Foundations and Trends(r) in I
Published: 2012-07
Total Pages: 146
ISBN-13: 9781601985668
DOWNLOAD EBOOKPeople have looked for experts since before the advent of computers. With advances in information retrieval technology, coupled with the large-scale availability of traces of knowledge-related activities, computer systems that can fully automate the process of locating expertise have become a reality. The past decade has witnessed tremendous interest and a wealth of results in expertise retrieval as an emerging subdiscipline in information retrieval. This survey highlights advances in models and algorithms relevant to this field. We draw connections among methods proposed in the literature and summarize them in five groups of basic approaches. These serve as the building blocks for more advanced models that arise when we consider a range of content-based factors that may impact the strength of association between a topic and a person. We also discuss practical aspects of building an expert search system and present applications of the technology in other domains such as blog distillation and entity retrieval. The limitations of current approaches are also pointed out. We end our survey with a set of conjectures on what the future may hold for expertise retrieval research.
Network Science
Author: Ted G. Lewis
Publisher: John Wiley & Sons
Published: 2011-09-20
Total Pages: 440
ISBN-13: 1118211014
DOWNLOAD EBOOKA comprehensive look at the emerging science of networks Network science helps you design faster, more resilient communication networks; revise infrastructure systems such as electrical power grids, telecommunications networks, and airline routes; model market dynamics; understand synchronization in biological systems; and analyze social interactions among people. This is the first book to take a comprehensive look at this emerging science. It examines the various kinds of networks (regular, random, small-world, influence, scale-free, and social) and applies network processes and behaviors to emergence, epidemics, synchrony, and risk. The book's uniqueness lies in its integration of concepts across computer science, biology, physics, social network analysis, economics, and marketing. The book is divided into easy-to-understand topical chapters and the presentation is augmented with clear illustrations, problems and answers, examples, applications, tutorials, and a discussion of related Java software. Chapters cover: Origins Graphs Regular Networks Random Networks Small-World Networks Scale-Free Networks Emergence Epidemics Synchrony Influence Networks Vulnerability Net Gain Biology This book offers a new understanding and interpretation of the field of network science. It is an indispensable resource for researchers, professionals, and technicians in engineering, computing, and biology. It also serves as a valuable textbook for advanced undergraduate and graduate courses in related fields of study.
Mining the Web
Author: Soumen Chakrabarti
Publisher: Morgan Kaufmann
Published: 2002-10-09
Total Pages: 366
ISBN-13: 1558607544
DOWNLOAD EBOOKThe definitive book on mining the Web from the preeminent authority.
Foundations of Data Science
Author: Avrim Blum
Publisher: Cambridge University Press
Published: 2020-01-23
Total Pages: 433
ISBN-13: 1108617360
DOWNLOAD EBOOKThis book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.
