Dynamic Network Representation Based On Latent Factorization Of Tensors


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Dynamic Network Representation Based on Latent Factorization of Tensors


Dynamic Network Representation Based on Latent Factorization of Tensors

Author: Hao Wu

language: en

Publisher: Springer Nature

Release Date: 2023-03-07


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A dynamic network is frequently encountered in various real industrial applications, such as the Internet of Things. It is composed of numerous nodes and large-scale dynamic real-time interactions among them, where each node indicates a specified entity, each directed link indicates a real-time interaction, and the strength of an interaction can be quantified as the weight of a link. As the involved nodes increase drastically, it becomes impossible to observe their full interactions at each time slot, making a resultant dynamic network High Dimensional and Incomplete (HDI). An HDI dynamic network with directed and weighted links, despite its HDI nature, contains rich knowledge regarding involved nodes’ various behavior patterns. Therefore, it is essential to study how to build efficient and effective representation learning models for acquiring useful knowledge. In this book, we first model a dynamic network into an HDI tensor and present the basic latent factorization of tensors (LFT) model. Then, we propose four representative LFT-based network representation methods. The first method integrates the short-time bias, long-time bias and preprocessing bias to precisely represent the volatility of network data. The second method utilizes a proportion-al-integral-derivative controller to construct an adjusted instance error to achieve a higher convergence rate. The third method considers the non-negativity of fluctuating network data by constraining latent features to be non-negative and incorporating the extended linear bias. The fourth method adopts an alternating direction method of multipliers framework to build a learning model for implementing representation to dynamic networks with high preciseness and efficiency.

Network and Parallel Computing


Network and Parallel Computing

Author: Xu Chen

language: en

Publisher: Springer Nature

Release Date: 2025-03-28


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This two part LNCS 15227 and 15528 volumes constitutes the proceedings of the 20th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2024, which was held in Haikou, China, during December 7–8, 2024. The 76 full papers presented in this volume were carefully reviewed and selected from 200 submissions. They are organized according to the following topics: Part-I : High-performance and Parallel Computing; Novel Memory and Storage Systems; and Emerging Architectures and Systems. Part-II : Edge Computing and Intelligence; Federated Learning Algorithms and Systems; Emerging Networks; and In-network Computing and Processing.

Advanced Intelligent Computing Technology and Applications


Advanced Intelligent Computing Technology and Applications

Author: De-Shuang Huang

language: en

Publisher: Springer Nature

Release Date: 2023-07-30


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This three-volume set of LNCS 14086, LNCS 14087 and LNCS 14088 constitutes - in conjunction with the double-volume set LNAI 14089-14090- the refereed proceedings of the 19th International Conference on Intelligent Computing, ICIC 2023, held in Zhengzhou, China, in August 2023. The 337 full papers of the three proceedings volumes were carefully reviewed and selected from 828 submissions. This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was "Advanced Intelligent Computing Technology and Applications". Papers that focused on this theme were solicited, addressing theories, methodologies, and applications in science and technology.