Heterogeneous Information Network Analysis and Applications

Heterogeneous Information Network Analysis and Applications
Author: Chuan Shi
Publisher: Springer
Total Pages: 233
Release: 2017-05-25
Genre: Computers
ISBN: 3319562126
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This book offers researchers an understanding of the fundamental issues and a good starting point to work on this rapidly expanding field. It provides a comprehensive survey of current developments of heterogeneous information network. It also presents the newest research in applications of heterogeneous information networks to similarity search, ranking, clustering, recommendation. This information will help researchers to understand how to analyze networked data with heterogeneous information networks. Common data mining tasks are explored, including similarity search, ranking, and recommendation. The book illustrates some prototypes which analyze networked data. Professionals and academics working in data analytics, networks, machine learning, and data mining will find this content valuable. It is also suitable for advanced-level students in computer science who are interested in networking or pattern recognition.

Heterogeneous Network Mining and Analysis

Heterogeneous Network Mining and Analysis
Author: Ranran Bian
Publisher:
Total Pages: 145
Release: 2019
Genre: Computer networks
ISBN:
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Nowadays, large amounts of data are being created daily through ngertips with the emergence of abundant social media. With the exponential growth of the Internet over the past decades, there has been a surge of interest in the capability to extract useful data, trends and structures on these social platforms as they act as a gateway for online commercialization and information propagation. Heterogeneous networks model di erent types of objects and relationships among them. Compared to homogeneous networks, heterogeneous networks can fuse information from multiple data sources and social platforms. Therefore, it is natural to model complex objects and their relationships in big social media data with heterogeneous networks. Despite decades of technique development for various data mining tasks, few of them target heterogeneous networks. Heterogeneity is a key element in contemporary social networks which provides diversi ed perception of networks. Therefore, heterogeneous network analysis has become an important topic in data mining in recent years that has been attracting increasing attention from both industry and academia, as they provide more comprehensive and interesting analysis results than their projected homogeneous networks. Motivated by these considerations, this thesis presents a series of new techniques for knowledge discovery in heterogeneous networks. In particular, the methods proposed in this thesis have been applied to a wide range of applications including community discovery, ranking and information retrieval. For dynamic heterogeneous networks, our research presents a more e ective network embedding technique when compared to the existing state-of-the-art methods. Throughout this thesis, we highlight how our methodologies were able to identify more tightly coupled communities in heterogeneous networks, more accurately rank top performing social actors and having the capability to view heterogeneous networks in a dynamic construct.

Mining Heterogeneous Information Networks

Mining Heterogeneous Information Networks
Author: Yizhou Sun
Publisher: Morgan & Claypool Publishers
Total Pages: 161
Release: 2012-08-15
Genre: Computers
ISBN: 1608458814
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Real world physical and abstract data objects are interconnected, forming gigantic, interconnected networks. By structuring these data objects and interactions between these objects into multiple types, such networks become semi-structured heterogeneous information networks. Most real world applications that handle big data, including interconnected social media and social networks, scientific, engineering, or medical information systems, online e-commerce systems, and most database systems, can be structured into heterogeneous information networks. Therefore, effective analysis of large-scale heterogeneous information networks poses an interesting but critical challenge. In this monograph, we investigate the principles and methodologies of mining heterogeneous information networks. Departing from many existing network models that view data as homogeneous graphs or networks, our semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and uncovers surprisingly rich knowledge from interconnected data. This semi-structured heterogeneous network modeling leads to a series of new principles and powerful methodologies for mining interconnected data, including (1) rank-based clustering and classification, (2) meta-path-based similarity search and mining, (3) relation strength-aware mining, and many other potential developments. This monograph introduces this new research frontier and points out some promising research directions.

Network Data Mining And Analysis

Network Data Mining And Analysis
Author: Ming Gao
Publisher: World Scientific
Total Pages: 205
Release: 2018-09-28
Genre: Computers
ISBN: 9813274972
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Online social networking sites like Facebook, LinkedIn, and Twitter, offer millions of members the opportunity to befriend one another, send messages to each other, and post content on the site — actions which generate mind-boggling amounts of data every day.To make sense of the massive data from these sites, we resort to social media mining to answer questions like the following:

Network Embedding

Network Embedding
Author: Cheng Yang
Publisher: Morgan & Claypool Publishers
Total Pages: 244
Release: 2021-03-25
Genre: Computers
ISBN: 1636390455
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This 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.

Network Data Mining and Analysis

Network Data Mining and Analysis
Author: Ming Gao (Data analyst)
Publisher:
Total Pages:
Release: 2018
Genre: COMPUTERS
ISBN: 9789813274969
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"Consider an online social networking site with millions of members in which members have the opportunity to befriend one another, send messages to each other, and post content on the site. Facebook, LinkedIn, and Twitter are examples of such sites. To make sense of data from these sites, we resort to social media mining to answer the following questions: 1. What are social communities in bipartite graphs and signed graphs? 2. How robust are the networks? How can we apply the robustness of networks? 3. How can we find identical social users across heterogeneous social networks? Social media shatters the boundaries between the real world and the virtual world. We can now integrate social theories with computational methods to study how individuals interact with each other and how social communities form in bipartite and signed networks. The uniqueness of social media data calls for novel data mining techniques that can effectively handle user generated content with rich social relations. The study and development of these new techniques are under the purview of social media mining, an emerging discipline under the umbrella of data mining. Social Media Mining is the process of representing, analyzing, and extracting actionable patterns from social media data"--

Performance Modelling and Analysis of Heterogeneous Networks

Performance Modelling and Analysis of Heterogeneous Networks
Author: Demetres D. Kouvatsos
Publisher: CRC Press
Total Pages: 489
Release: 2022-09-01
Genre: Science
ISBN: 1000793745
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Over the recent years, a considerable amount of effort has been devoted, both in industry and academia, towards the performance modelling, evaluation and prediction of convergent multi-service heterogeneous networks, such as wireless and optical networks, towards the design and dimensioning of the next and future generation Internets.This book follows Heterogeneous Networks: Traffic Engineering, Performance Evaluation Studies and Tools and presents recent advances in networks of diverse technology reflecting the state-of-the-art technology and research achievements in performance modelling, analysis and applications worldwide.Technical topics discussed in the book include:• Multiservice Switching Networks;• Multiservice Switching Networks;• Wireless Ad Hoc Networks;• Wireless Sensor Networks;• Wireless Cellular Networks;• Optical Networks;Heterogeneous Networks:- Performance Modelling and Analysis contains recently extended research papers, which have their roots in the series of the HET-NETs International Working Conferences focusing on the 'Performance Modelling and Evaluation of Heterogeneous Networks' under the auspices of the EU Networks of Excellence Euro-NGI and Euro-FGI.Heterogeneous Networks: Performance Modelling and Analysis is ideal for personnel in computer/communication industries as well as academic staff and master/research students in computer science, operational research, electrical engineering and telecommunication systems and the Internet.KeywordsHeterogeneous networks, performance modelling and analysis, wired networks, wireless networks: ad hoc, sensor and cellular, optical networks, next and future generation Internets.

Community detection and mining in social media

Community detection and mining in social media
Author: Lei Tang
Publisher: Springer Nature
Total Pages: 126
Release: 2022-06-01
Genre: Computers
ISBN: 3031019008
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The past decade has witnessed the emergence of participatory Web and social media, bringing people together in many creative ways. Millions of users are playing, tagging, working, and socializing online, demonstrating new forms of collaboration, communication, and intelligence that were hardly imaginable just a short time ago. Social media also helps reshape business models, sway opinions and emotions, and opens up numerous possibilities to study human interaction and collective behavior in an unparalleled scale. This lecture, from a data mining perspective, introduces characteristics of social media, reviews representative tasks of computing with social media, and illustrates associated challenges. It introduces basic concepts, presents state-of-the-art algorithms with easy-to-understand examples, and recommends effective evaluation methods. In particular, we discuss graph-based community detection techniques and many important extensions that handle dynamic, heterogeneous networks in social media. We also demonstrate how discovered patterns of communities can be used for social media mining. The concepts, algorithms, and methods presented in this lecture can help harness the power of social media and support building socially-intelligent systems. This book is an accessible introduction to the study of \emph{community detection and mining in social media}. It is an essential reading for students, researchers, and practitioners in disciplines and applications where social media is a key source of data that piques our curiosity to understand, manage, innovate, and excel. This book is supported by additional materials, including lecture slides, the complete set of figures, key references, some toy data sets used in the book, and the source code of representative algorithms. The readers are encouraged to visit the book website for the latest information. Table of Contents: Social Media and Social Computing / Nodes, Ties, and Influence / Community Detection and Evaluation / Communities in Heterogeneous Networks / Social Media Mining