Overlapping Community Detection In Massive Social Networks
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Overlapping Community Detection In Massive Social Networks
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Author : Joyce Jiyoung Whang
language : en
Publisher:
Release Date : 2015
Overlapping Community Detection In Massive Social Networks written by Joyce Jiyoung Whang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Massive social networks have become increasingly popular in recent years. Community detection is one of the most important techniques for the analysis of such complex networks. A community is a set of cohesive vertices that has more connections inside the set than outside. In many social and information networks, these communities naturally overlap. For instance, in a social network, each vertex in a graph corresponds to an individual who usually participates in multiple communities. In this thesis, we propose scalable overlapping community detection algorithms that effectively identify high quality overlapping communities in various real-world networks. We first develop an efficient overlapping community detection algorithm using a seed set expansion approach. The key idea of this algorithm is to find good seeds and then greedily expand these seeds using a personalized PageRank clustering scheme. Experimental results show that our algorithm significantly outperforms other state-of-the-art overlapping community detection methods in terms of run time, cohesiveness of communities, and ground-truth accuracy. To develop more principled methods, we formulate the overlapping community detection problem as a non-exhaustive, overlapping graph clustering problem where clusters are allowed to overlap with each other, and some nodes are allowed to be outside of any cluster. To tackle this non-exhaustive, overlapping clustering problem, we propose a simple and intuitive objective function that captures the issues of overlap and non-exhaustiveness in a unified manner. To optimize the objective, we develop not only fast iterative algorithms but also more sophisticated algorithms using a low-rank semidefinite programming technique. Our experimental results show that the new objective and the algorithms are effective in finding ground-truth clusterings that have varied overlap and non-exhaustiveness. We extend our non-exhaustive, overlapping clustering techniques to co-clustering where the goal is to simultaneously identify a clustering of the rows as well as the columns of a data matrix. As an example application, consider recommender systems where users have ratings on items. This can be represented by a bipartite graph where users and items are denoted by two different types of nodes, and the ratings are denoted by weighted edges between the users and the items. In this case, co-clustering would be a simultaneous clustering of users and items. We propose a new co-clustering objective function and an efficient co-clustering algorithm that is able to identify overlapping clusters as well as outliers on both types of the nodes in the bipartite graph. We show that our co-clustering algorithm is able to effectively capture the underlying co-clustering structure of the data, which results in boosting the performance of a standard one-dimensional clustering. Finally, we study the design of parallel data-driven algorithms, which enables us to further increase the scalability of our overlapping community detection algorithms. Using PageRank as a model problem, we look at three algorithm design axes: work activation, data access pattern, and scheduling. We investigate the impact of different algorithm design choices. Using these design axes, we design and test a variety of PageRank implementations finding that data-driven, push-based algorithms are able to achieve a significantly superior scalability than standard PageRank implementations. The design choices affect both single-threaded performance as well as parallel scalability. The lessons learned from this study not only guide efficient implementations of many graph mining algorithms but also provide a framework for designing new scalable algorithms, especially for large-scale community detection.
Graph Theoretic Approaches For Analyzing Large Scale Social Networks
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Author : Meghanathan, Natarajan
language : en
Publisher: IGI Global
Release Date : 2017-07-13
Graph Theoretic Approaches For Analyzing Large Scale Social Networks written by Meghanathan, Natarajan and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-13 with Computers categories.
Social network analysis has created novel opportunities within the field of data science. The complexity of these networks requires new techniques to optimize the extraction of useful information. Graph Theoretic Approaches for Analyzing Large-Scale Social Networks is a pivotal reference source for the latest academic research on emerging algorithms and methods for the analysis of social networks. Highlighting a range of pertinent topics such as influence maximization, probabilistic exploration, and distributed memory, this book is ideally designed for academics, graduate students, professionals, and practitioners actively involved in the field of data science.
Community Structure Analysis From Social Networks
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Author : Sajid Yousuf Bhat
language : en
Publisher: CRC Press
Release Date : 2025-07-23
Community Structure Analysis From Social Networks written by Sajid Yousuf Bhat and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-23 with Computers categories.
This book addresses social and complex network analysis challenges, exploring social network structures, dynamic networks, and hierarchical communities. Emphasizing network structure heterogeneity, including directionality and dynamics, it covers community structure concepts like distinctness, overlap, and hierarchy. The book aims to present challenges and innovative solutions in community structure detection, incorporating diversity into problem-solving. Furthermore, it explores the applications of identified community structures within network analysis, offering insights into social network dynamics. Investigates the practical applications and uses of community structures identified from network analysis across various domains of real-world networks Highlights the challenges encountered in analyzing community structures and presents state-of-the-art approaches designed to address these challenges Spans into various domains like business intelligence, marketing, and epidemics, examining influential node detection and crime within social networks Explores methodologies for evaluating the quality and accuracy of community detection models Examines a diverse range of challenges and offers innovative solutions in the field of detecting community structures from social networks The book is a ready reference for researchers and scholars of Computer Science and Computational Social Systems working in the area of Community Structure Analysis from Social Network Data.
Transactions On Large Scale Data And Knowledge Centered Systems Xviii
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Author : Abdelkader Hameurlain
language : en
Publisher: Springer
Release Date : 2015-02-21
Transactions On Large Scale Data And Knowledge Centered Systems Xviii written by Abdelkader Hameurlain and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-02-21 with Computers categories.
The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. Current decentralized systems still focus on data and knowledge as their main resource. Feasibility of these systems relies basically on P2P (peer-to-peer) techniques and the support of agent systems with scaling and decentralized control. Synergy between grids, P2P systems, and agent technologies is the key to data- and knowledge-centered systems in large-scale environments. This, the 18th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains extended and revised versions of seven papers presented at the 24th International Conference on Database and Expert Systems Applications, DEXA 2013, held in Prague, in the Czech Republic, in August 2013. Following the conference, and two further rounds of reviewing and selection, five extended papers and two invited keynote papers were chosen for inclusion in this special issue. The subject areas covered include argumentation, e-government, business processes, predictive traffic estimation, semantic model integration, top-k query processing, uncertainty handling, graph comparison, community detection, genetic programming, and web services.
Multi Disciplinary Trends In Artificial Intelligence
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Author : Antonis Bikakis
language : en
Publisher: Springer
Release Date : 2015-11-28
Multi Disciplinary Trends In Artificial Intelligence written by Antonis Bikakis and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-28 with Computers categories.
This book constitutes the refereed conference proceedings of the 9th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2015, held in Fuzhou, China, in November 2015. The 30 revised full papers presented together with 12 short papers were carefully reviewed and selected from 83 submissions. The papers feature a wide range of topics covering knowledge representation, reasoning, and management; multi-agent systems; data mining and machine learning; computer vision; robotics; AI in bioinformatics; AI in security and networks; and other AI applications.
Intelligent Data Analysis And Its Applications Volume I
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Author : Jeng-Shyang Pan
language : en
Publisher: Springer
Release Date : 2014-05-26
Intelligent Data Analysis And Its Applications Volume I written by Jeng-Shyang Pan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-26 with Technology & Engineering categories.
This volume presents the proceedings of the First Euro-China Conference on Intelligent Data Analysis and Applications (ECC 2014), which was hosted by Shenzhen Graduate School of Harbin Institute of Technology and was held in Shenzhen City on June 13-15, 2014. ECC 2014 was technically co-sponsored by Shenzhen Municipal People’s Government, IEEE Signal Processing Society, Machine Intelligence Research Labs, VSB-Technical University of Ostrava (Czech Republic), National Kaohsiung University of Applied Sciences (Taiwan), and Secure E-commerce Transactions (Shenzhen) Engineering Laboratory of Shenzhen Institute of Standards and Technology.
Community Search Over Big Graphs
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Author : Xin Huang
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Community Search Over Big Graphs written by Xin Huang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
Communities serve as basic structural building blocks for understanding the organization of many real-world networks, including social, biological, collaboration, and communication networks. Recently, community search over graphs has attracted significantly increasing attention, from small, simple, and static graphs to big, evolving, attributed, and location-based graphs. In this book, we first review the basic concepts of networks, communities, and various kinds of dense subgraph models. We then survey the state of the art in community search techniques on various kinds of networks across different application areas. Specifically, we discuss cohesive community search, attributed community search, social circle discovery, and geo-social group search. We highlight the challenges posed by different community search problems. We present their motivations, principles, methodologies, algorithms, and applications, and provide a comprehensive comparison of the existing techniques. This book finally concludes by listing publicly available real-world datasets and useful tools for facilitating further research, and by offering further readings and future directions of research in this important and growing area.
Recent Advances In Hybrid Metaheuristics For Data Clustering
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Author : Sourav De
language : en
Publisher: John Wiley & Sons
Release Date : 2020-06-02
Recent Advances In Hybrid Metaheuristics For Data Clustering written by Sourav De and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-02 with Computers categories.
An authoritative guide to an in-depth analysis of various state-of-the-art data clustering approaches using a range of computational intelligence techniques Recent Advances in Hybrid Metaheuristics for Data Clustering offers a guide to the fundamentals of various metaheuristics and their application to data clustering. Metaheuristics are designed to tackle complex clustering problems where classical clustering algorithms have failed to be either effective or efficient. The authors noted experts on the topic provide a text that can aid in the design and development of hybrid metaheuristics to be applied to data clustering. The book includes performance analysis of the hybrid metaheuristics in relationship to their conventional counterparts. In addition to providing a review of data clustering, the authors include in-depth analysis of different optimization algorithms. The text offers a step-by-step guide in the build-up of hybrid metaheuristics and to enhance comprehension. In addition, the book contains a range of real-life case studies and their applications. This important text: Includes performance analysis of the hybrid metaheuristics as related to their conventional counterparts Offers an in-depth analysis of a range of optimization algorithms Highlights a review of data clustering Contains a detailed overview of different standard metaheuristics in current use Presents a step-by-step guide to the build-up of hybrid metaheuristics Offers real-life case studies and applications Written for researchers, students and academics in computer science, mathematics, and engineering, Recent Advances in Hybrid Metaheuristics for Data Clustering provides a text that explores the current data clustering approaches using a range of computational intelligence techniques.
Principles Of Big Graph In Depth Insight
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Author :
language : en
Publisher: Elsevier
Release Date : 2023-01-24
Principles Of Big Graph In Depth Insight written by and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-24 with Computers categories.
Principles of Big Graph: In-depth Insight, Volume 128 in the Advances in Computer series, highlights new advances in the field with this new volume presenting interesting chapters on a variety of topics, including CESDAM: Centered subgraph data matrix for large graph representation, Bivariate, cluster and suitability analysis of NoSQL Solutions for big graph applications, An empirical investigation on Big Graph using deep learning, Analyzing correlation between quality and accuracy of graph clustering, geneBF: Filtering protein-coded gene graph data using bloom filter, Processing large graphs with an alternative representation, MapReduce based convolutional graph neural networks: A comprehensive review. Fast exact triangle counting in large graphs using SIMD acceleration, A comprehensive investigation on attack graphs, Qubit representation of a binary tree and its operations in quantum computation, Modified ML-KNN: Role of similarity measures and nearest neighbor configuration in multi label text classification on big social network graph data, Big graph based online learning through social networks, Community detection in large-scale real-world networks, Power rank: An interactive web page ranking algorithm, GA based energy efficient modelling of a wireless sensor network, The major challenges of big graph and their solutions: A review, and An investigation on socio-cyber crime graph. - Provides an update on the issues and challenges faced by current researchers - Updates on future research agendas - Includes advanced topics for intensive research for researchers
Proceedings Of The International Conference On Computing And Communication Systems
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Author : Arnab Kumar Maji
language : en
Publisher: Springer Nature
Release Date : 2021-04-11
Proceedings Of The International Conference On Computing And Communication Systems written by Arnab Kumar Maji and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-11 with Technology & Engineering categories.
This book contains the latest research work presented at the International Conference on Computing and Communication Systems (I3CS 2020) held at North-Eastern Hill University (NEHU), Shillong, India. The book presents original research results, new ideas and practical development experiences which concentrate on both theory and practices. It includes papers from all areas of information technology, computer science, electronics and communication engineering written by researchers, scientists, engineers and scholar students and experts from India and abroad.