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Heterogeneous Information Network Analysis and Applications

2017-05-25 Computers
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Release : 2017-05-25
Genre : Computers
Kind : eBook
Book Rating : 126/5 ( reviews)

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Book Synopsis Heterogeneous Information Network Analysis and Applications by : Chuan Shi

Download or read book Heterogeneous Information Network Analysis and Applications written by Chuan Shi. This book was released on 2017-05-25. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Mining Heterogeneous Information Networks

2012-08-15 Computers
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Release : 2012-08-15
Genre : Computers
Kind : eBook
Book Rating : 814/5 ( reviews)

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Book Synopsis Mining Heterogeneous Information Networks by : Yizhou Sun

Download or read book Mining Heterogeneous Information Networks written by Yizhou Sun. This book was released on 2012-08-15. Available in PDF, EPUB and Kindle. Book excerpt: 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 Embedding

2021-03-25 Computers
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Release : 2021-03-25
Genre : Computers
Kind : eBook
Book Rating : 455/5 ( reviews)

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Book Synopsis Network Embedding by : Cheng Yang

Download or read book Network Embedding written by Cheng Yang. This book was released on 2021-03-25. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Graph Neural Networks: Foundations, Frontiers, and Applications

2022-01-03 Computers
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Release : 2022-01-03
Genre : Computers
Kind : eBook
Book Rating : 549/5 ( reviews)

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Book Synopsis Graph Neural Networks: Foundations, Frontiers, and Applications by : Lingfei Wu

Download or read book Graph Neural Networks: Foundations, Frontiers, and Applications written by Lingfei Wu. This book was released on 2022-01-03. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. The new neural network architectures on graph-structured data (graph neural networks, GNNs in short) have performed remarkably on these tasks, demonstrated by applications in social networks, bioinformatics, and medical informatics. Despite these successes, GNNs still face many challenges ranging from the foundational methodologies to the theoretical understandings of the power of the graph representation learning. This book provides a comprehensive introduction of GNNs. It first discusses the goals of graph representation learning and then reviews the history, current developments, and future directions of GNNs. The second part presents and reviews fundamental methods and theories concerning GNNs while the third part describes various frontiers that are built on the GNNs. The book concludes with an overview of recent developments in a number of applications using GNNs. This book is suitable for a wide audience including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications.

Mining Heterogeneous Information Networks

2012
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Author :
Release : 2012
Genre :
Kind : eBook
Book Rating : /5 ( reviews)

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Book Synopsis Mining Heterogeneous Information Networks by : Yizhou Sun

Download or read book Mining Heterogeneous Information Networks written by Yizhou Sun. This book was released on 2012. Available in PDF, EPUB and Kindle. Book excerpt:

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