Heterogeneous Information Network Analysis and Applications - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Heterogeneous Information Network Analysis and Applications write by Chuan Shi. This book was released on 2017-05-25. Heterogeneous Information Network Analysis and Applications available in PDF, EPUB and Kindle. 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
Mining Heterogeneous Information Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Mining Heterogeneous Information Networks write by Yizhou Sun. This book was released on 2012-08-15. Mining Heterogeneous Information Networks available in PDF, EPUB and Kindle. 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
Network Embedding - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Network Embedding write by Cheng Yang. This book was released on 2021-03-25. Network Embedding available in PDF, EPUB and Kindle. 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.
Mining Heterogeneous Information Networks
Mining Heterogeneous Information Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Mining Heterogeneous Information Networks write by Yizhou Sun. This book was released on 2012. Mining Heterogeneous Information Networks available in PDF, EPUB and Kindle.
Neural Computing for Advanced Applications
Neural Computing for Advanced Applications - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Neural Computing for Advanced Applications write by Haijun Zhang. This book was released on 2022-10-20. Neural Computing for Advanced Applications available in PDF, EPUB and Kindle. The two-volume Proceedings set CCIS 1637 and 1638 constitutes the refereed proceedings of the Third International Conference on Neural Computing for Advanced Applications, NCAA 2022, held in Jinan, China, during July 8–10, 2022. The 77 papers included in these proceedings were carefully reviewed and selected from 205 submissions. These papers were categorized into 10 technical tracks, i.e., neural network theory, and cognitive sciences, machine learning, data mining, data security & privacy protection, and data-driven applications, computational intelligence, nature-inspired optimizers, and their engineering applications, cloud/edge/fog computing, the Internet of Things/Vehicles (IoT/IoV), and their system optimization, control systems, network synchronization, system integration, and industrial artificial intelligence, fuzzy logic, neuro-fuzzy systems, decision making, and their applications in management sciences, computer vision, image processing, and their industrial applications, natural language processing, machine translation, knowledge graphs, and their applications, Neural computing-based fault diagnosis, fault forecasting, prognostic management, and system modeling, and Spreading dynamics, forecasting, and other intelligent techniques against coronavirus disease (COVID-19).
