Download or read book Cohesive Subgraph Search Over Large Heterogeneous Information Networks written by Yixiang Fang and published by Springer Nature. This book was released on 2022-05-06 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: This SpringerBrief provides the first systematic review of the existing works of cohesive subgraph search (CSS) over large heterogeneous information networks (HINs). It also covers the research breakthroughs of this area, including models, algorithms and comparison studies in recent years. This SpringerBrief offers a list of promising future research directions of performing CSS over large HINs. The authors first classify the existing works of CSS over HINs according to the classic cohesiveness metrics such as core, truss, clique, connectivity, density, etc., and then extensively review the specific models and their corresponding search solutions in each group. Note that since the bipartite network is a special case of HINs, all the models developed for general HINs can be directly applied to bipartite networks, but the models customized for bipartite networks may not be easily extended for other general HINs due to their restricted settings. The authors also analyze and compare these cohesive subgraph models (CSMs) and solutions systematically. Specifically, the authors compare different groups of CSMs and analyze both their similarities and differences, from multiple perspectives such as cohesiveness constraints, shared properties, and computational efficiency. Then, for the CSMs in each group, the authors further analyze and compare their model properties and high-level algorithm ideas. This SpringerBrief targets researchers, professors, engineers and graduate students, who are working in the areas of graph data management and graph mining. Undergraduate students who are majoring in computer science, databases, data and knowledge engineering, and data science will also want to read this SpringerBrief.

Download or read book Cohesive Subgraph Computation over Large Sparse Graphs written by Lijun Chang and published by Springer. This book was released on 2018-12-24 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is considered the first extended survey on algorithms and techniques for efficient cohesive subgraph computation. With rapid development of information technology, huge volumes of graph data are accumulated. An availability of rich graph data not only brings great opportunities for realizing big values of data to serve key applications, but also brings great challenges in computation. Using a consistent terminology, the book gives an excellent introduction to the models and algorithms for the problem of cohesive subgraph computation. The materials of this book are well organized from introductory content to more advanced topics while also providing well-designed source codes for most algorithms described in the book. This is a timely book for researchers who are interested in this topic and efficient data structure design for large sparse graph processing. It is also a guideline book for new researchers to get to know the area of cohesive subgraph computation.

Download or read book Mining Large Heterogeneous Graphs Using Cray S Urika written by and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Pattern discovery and predictive modeling from seemingly related Big Data represented as massive, ad-hoc, heterogeneous networks (e.g., extremely large graphs with complex, possibly unknown structure) is an outstanding problem in many application domains. To address this problem, we are designing graph-mining algorithms capable of discovering relationship-patterns from such data and using those discovered patterns as features for classification and predictive modeling. Specifically, we are: (i) exploring statistical properties, mechanics and generative models of behavior patterns in heterogeneous information networks, (ii) developing novel, automated and scalable graph-pattern discovery algorithms and (iii) applying our relationship-analytics (data science + network science) expertise to domains spanning healthcare to homeland security.

Download or read book Dense Subgraph Mining in Probabilistic Graphs written by Fatemeh Esfahani and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation we consider the problem of mining cohesive (dense) subgraphs in probabilistic graphs, where each edge has a probability of existence. Mining probabilistic graphs has become the focus of interest in analyzing many real-world datasets, such as social, trust, communication, and biological networks due to the intrinsic uncertainty present in them. Studying cohesive subgraphs can reveal important information about connectivity, centrality, and robustness of the network, with applications in areas such as bioinformatics and social networks. In deterministic graphs, there exists various definitions of cohesive substructures, including cliques, quasi-cliques, k-cores and k-trusses. In this regard, k-core and k-truss decompositions are popular tools for finding cohesive subgraphs. In deterministic graphs, a k-core is the largest subgraph in which each vertex has at least k neighbors, and a k-truss is the largest subgraph whose edges are contained in at least k triangles (or k-2 triangles depending on the definition). The k-core and k-truss decomposition in deterministic graphs have been thoroughly studied in the literature. However, in the probabilistic context, the computation is challenging and state-of-art approaches are not scalable to large graphs. The main challenge is efficient computation of the tail probabilities of vertex degrees and triangle count of edges in probabilistic graphs. We employ a special version of central limit theorem (CLT) to obtain the tail probabilities efficiently. Based on our CLT approach we propose peeling algorithms for core and truss decomposition of a probabilistic graph that scales to very large graphs and offers significant improvement over state-of-the-art approaches. Moreover, we propose a second algorithm for probabilistic core decomposition that can handle graphs not fitting in memory by processing them sequentially one vertex at a time. In terms of truss decomposition, we design a second method which is based on progressive tightening of the estimate of the truss value of each edge based on h-index computation and novel use of dynamic programming. We provide extensive experimental results to show the efficiency of the proposed algorithms. Another contribution of this thesis is mining cohesive subgraphs using the recent notion of nucleus decomposition introduced by Sariyuce et al. Nucleus decomposition is based on higher order structures such as cliques nested in other cliques. Nucleus decomposition can reveal interesting subgraphs that can be missed by core and truss decompositions. In this dissertation, we present nucleus decomposition for probabilistic graphs. The major questions we address are: How to define meaningfully nucleus decomposition in probabilistic graphs? How hard is computing nucleus decomposition in probabilistic graphs? Can we devise efficient algorithms for exact or approximate nucleus decomposition in large graphs? We present three natural definitions of nucleus decomposition in probabilistic graphs: local, global, and weakly-global. We show that the local version is in PTIME, whereas global and weakly-global are #P-hard and NP-hard, respectively. We present an efficient and exact dynamic programming approach for the local case. Further, we present statistical approximations that can scale to bigger datasets without much loss of accuracy. For global and weakly-global decompositions we complement our intractability results by proposing efficient algorithms that give approximate solutions based on search space pruning and Monte-Carlo sampling. Extensive experiments show the scalability and efficiency of our algorithms. Compared to probabilistic core and truss decompositions, nucleus decomposition significantly outperforms in terms of density and clustering metrics.

Download or read book Database Systems for Advanced Applications written by Arnab Bhattacharya and published by Springer Nature. This book was released on 2022-04-26 with total page 788 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three-volume set LNCS 13245, 13246 and 13247 constitutes the proceedings of the 26th International Conference on Database Systems for Advanced Applications, DASFAA 2022, held online, in April 2021. The total of 72 full papers, along with 76 short papers, are presented in this three-volume set was carefully reviewed and selected from 543 submissions. Additionally, 13 industrial papers, 9 demo papers and 2 PhD consortium papers are included. The conference was planned to take place in Hyderabad, India, but it was held virtually due to the COVID-19 pandemic.

Download or read book Web Information Systems Engineering WISE 2022 written by Richard Chbeir and published by Springer Nature. This book was released on 2022-11-07 with total page 658 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 23nd International Conference on Web Information Systems Engineering, WISE 2021, held in Biarritz, France, in November 2022. The 31 full, 13 short and 3 demo papers were carefully reviewed and selected from 94 submissions. The papers are organized in the following topical sections: Social Media, Spatial & Temporal Issues, Query Processing & Information Extraction, Architecture and Performance, Graph Data Management, Security & Privacy, Information Retrieval & Text Processing, Reinforcement Learning, Learning & Optimization, Spatial Data Processing, Recommendation, Neural Networks, and Demo Papers.

Download or read book Large scale Graph Analysis System Algorithm and Optimization written by Yingxia Shao and published by Springer Nature. This book was released on 2020-07-01 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces readers to a workload-aware methodology for large-scale graph algorithm optimization in graph-computing systems, and proposes several optimization techniques that can enable these systems to handle advanced graph algorithms efficiently. More concretely, it proposes a workload-aware cost model to guide the development of high-performance algorithms. On the basis of the cost model, the book subsequently presents a system-level optimization resulting in a partition-aware graph-computing engine, PAGE. In addition, it presents three efficient and scalable advanced graph algorithms – the subgraph enumeration, cohesive subgraph detection, and graph extraction algorithms. This book offers a valuable reference guide for junior researchers, covering the latest advances in large-scale graph analysis; and for senior researchers, sharing state-of-the-art solutions based on advanced graph algorithms. In addition, all readers will find a workload-aware methodology for designing efficient large-scale graph algorithms.

Download or read book Spatial Data and Intelligence written by Xiaofeng Meng and published by Springer Nature. This book was released on 2023-05-10 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 4th International Conference on Spatial Data and Intelligence, SpatialDI 2023, held in Nanchang, China, in April 13–15, 2023. The 18 full papers included in this book were carefully reviewed and selected from 68 submissions. They were organized in topical sections as follows: traffic management; visualization analysis; spatial big data analysis; spatiotemporal data mining; spatiotemporal data storage; and metaverse.

Download or read book The Spatial Grasp Model written by Peter Simon Sapaty and published by Emerald Group Publishing. This book was released on 2023-01-30 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Spatial Grasp Model suggests uses beyond the theoretical, including the examination of hurricanes and forest fires. Investigating group behaviour of ocean animals, discovery of unknown terrain features, and path-findings in large transport networks truly demonstrates the real-world application of SGL.

Download or read book Intelligence and Security Informatics written by Michael Chau and published by Springer. This book was released on 2016-03-28 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 11th Pacific Asia Workshop on Intelligence and Security Informatics, PAISI 2016, held in Auckland, New Zealand, in April 2016 in conjunction with PAKDD 2016, the 20th Pacific-Asia Conference on Knowledge Discovery and Data Mining. The 7 revised full papers presented together with 7 short papers were carefully reviewed and selected from 23 submissions. The papers cover network-based data analytics, data and text mining, and cyber security and infrastructure protection.

Download or read book On Uncertain Graphs written by Arijit Khan and published by Springer Nature. This book was released on 2022-05-31 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt: Large-scale, highly interconnected networks, which are often modeled as graphs, pervade both our society and the natural world around us. Uncertainty, on the other hand, is inherent in the underlying data due to a variety of reasons, such as noisy measurements, lack of precise information needs, inference and prediction models, or explicit manipulation, e.g., for privacy purposes. Therefore, uncertain, or probabilistic, graphs are increasingly used to represent noisy linked data in many emerging application scenarios, and they have recently become a hot topic in the database and data mining communities. Many classical algorithms such as reachability and shortest path queries become #P-complete and, thus, more expensive over uncertain graphs. Moreover, various complex queries and analytics are also emerging over uncertain networks, such as pattern matching, information diffusion, and influence maximization queries. In this book, we discuss the sources of uncertain graphs and their applications, uncertainty modeling, as well as the complexities and algorithmic advances on uncertain graphs processing in the context of both classical and emerging graph queries and analytics. We emphasize the current challenges and highlight some future research directions.

Download or read book Mining Heterogeneous Information Networks written by Yizhou Sun and published by Morgan & Claypool Publishers. This book was released on 2012 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: Investigates 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.

Download or read book Managing and Mining Graph Data written by Charu C. Aggarwal and published by Springer Science & Business Media. This book was released on 2010-02-02 with total page 623 pages. Available in PDF, EPUB and Kindle. Book excerpt: Managing and Mining Graph Data is a comprehensive survey book in graph management and mining. It contains extensive surveys on a variety of important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by well known researchers in the field, and provide a broad perspective of the area. This is the first comprehensive survey book in the emerging topic of graph data processing. Managing and Mining Graph Data is designed for a varied audience composed of professors, researchers and practitioners in industry. This volume is also suitable as a reference book for advanced-level database students in computer science and engineering.

Download or read book ECAI 2023 written by K. Gal and published by IOS Press. This book was released on 2023-10-18 with total page 3328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence, or AI, now affects the day-to-day life of almost everyone on the planet, and continues to be a perennial hot topic in the news. This book presents the proceedings of ECAI 2023, the 26th European Conference on Artificial Intelligence, and of PAIS 2023, the 12th Conference on Prestigious Applications of Intelligent Systems, held from 30 September to 4 October 2023 and on 3 October 2023 respectively in Kraków, Poland. Since 1974, ECAI has been the premier venue for presenting AI research in Europe, and this annual conference has become the place for researchers and practitioners of AI to discuss the latest trends and challenges in all subfields of AI, and to demonstrate innovative applications and uses of advanced AI technology. ECAI 2023 received 1896 submissions – a record number – of which 1691 were retained for review, ultimately resulting in an acceptance rate of 23%. The 390 papers included here, cover topics including machine learning, natural language processing, multi agent systems, and vision and knowledge representation and reasoning. PAIS 2023 received 17 submissions, of which 10 were accepted after a rigorous review process. Those 10 papers cover topics ranging from fostering better working environments, behavior modeling and citizen science to large language models and neuro-symbolic applications, and are also included here. Presenting a comprehensive overview of current research and developments in AI, the book will be of interest to all those working in the field.

Download or read book Social Networks Analysis and Case Studies written by Şule Gündüz-Öğüdücü and published by Springer. This book was released on 2014-07-11 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: The present volume provides a comprehensive resource for practitioners and researchers alike-both those new to the field as well as those who already have some experience. The work covers Social Network Analysis theory and methods with a focus on current applications and case studies applied in various domains such as mobile networks, security, machine learning and health. With the increasing popularity of Web 2.0, social media has become a widely used communication platform. Parallel to this development, Social Network Analysis gained in importance as a research field, while opening up many opportunities in different application domains. Forming a bridge between theory and applications makes this work appealing to both academics and practitioners as well as graduate students.

Download or read book Knowledge Graphs written by Aidan Hogan and published by Morgan & Claypool Publishers. This book was released on 2021-11-08 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale. The book defines knowledge graphs and provides a high-level overview of how they are used. It presents and contrasts popular graph models that are commonly used to represent data as graphs, and the languages by which they can be queried before describing how the resulting data graph can be enhanced with notions of schema, identity, and context. The book discusses how ontologies and rules can be used to encode knowledge as well as how inductive techniques—based on statistics, graph analytics, machine learning, etc.—can be used to encode and extract knowledge. It covers techniques for the creation, enrichment, assessment, and refinement of knowledge graphs and surveys recent open and enterprise knowledge graphs and the industries or applications within which they have been most widely adopted. The book closes by discussing the current limitations and future directions along which knowledge graphs are likely to evolve. This book is aimed at students, researchers, and practitioners who wish to learn more about knowledge graphs and how they facilitate extracting value from diverse data at large scale. To make the book accessible for newcomers, running examples and graphical notation are used throughout. Formal definitions and extensive references are also provided for those who opt to delve more deeply into specific topics.

Download or read book Representation Learning for Natural Language Processing written by Zhiyuan Liu and published by Springer Nature. This book was released on 2020-07-03 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.