Titlebook: Knowledge Science, Engineering and Management; 16th International C Zhi Jin,Yuncheng Jiang,Wenjun Ma Conference proceedings 2023 The Editor

Keratin

A Sparse Matrix Optimization Method for?Graph Neural Networks Traininguperior feature representation capabilities for graph data with non-Euclidean structures. These capabilities are enabled efficiently by sparse matrix-matrix multiplication (SPMM) and sparse matrix-vector multiplication (SPMV) that operate on sparse matrix representations of graph structures. However

HEW

Dual-Dimensional Refinement of?Knowledge Graph Embedding Representation within and between triples. However, existing methods primarily focus on a single dimension of entities or relations, limiting their ability to learn knowledge facts. To address this issue, this paper proposes a dual-dimension refined representation model. At the entity level, we perform residual s

微枝末節(jié)

Congregate

Dynamic and?Static Feature-Aware Microservices Decomposition via?Graph Neural Networkstem into microservices can increase code reusability and reduce reconstruction costs. However, existing microservices decomposition approaches only utilize dynamic or static feature to represent the monolithic system, leading to low coverage of classes and inadequate information. To address these is

偽書

Functional

Low Redundancy Learning for?Unsupervised Multi-view Feature Selections on the correlation between features and data category structure, while ignoring the redundancy between features. In this paper, we propose a multi-view feature selection method based on low redundancy learning, which introduces and automatically assigns the weight of feature redundancy in each vie

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Dynamic Feed-Forward LSTMo this end, we propose the Dynamic Feed-Forward LSTM (D-LSTM). Specifically, our D-LSTM first expands the capabilities of hidden states by assigning an exclusive state vector to each word. Then, the Dynamic Additive Attention (DAA) method is utilized to adaptively compress local context words into a

小卒

Black-Box Adversarial Attack on?Graph Neural Networks Based on?Node Domain Knowledgepplication of GNNs in various graph tasks, it is particularly important to study the principles and implementation of graph adversarial attacks for understanding the robustness of GNNs. Previous studies have attempted to reduce the prediction accuracy of GNNs by adding small perturbations to the gra

corporate

Tian Wang,Zhiguang Wang,Rongliang Wang,Dawei Li,Qiang Luw?rtsspirale. Zun?chst zur Abw?rtsspirale: Stadterneuerung und Regionalentwicklung sind normalerweise eigendynamische Prozesse, bei denen sich Quartiere auf neue Gegebenheiten durch den Druck des Marktes ausrichten und eine Modernisierung ohne künstliche Steuerung oder finanzielle Anreize stattfinde

archaeology

Long Chen,Mingjian Guang,Junli Wang,Chungang Yanw?rtsspirale. Zun?chst zur Abw?rtsspirale: Stadterneuerung und Regionalentwicklung sind normalerweise eigendynamische Prozesse, bei denen sich Quartiere auf neue Gegebenheiten durch den Druck des Marktes ausrichten und eine Modernisierung ohne künstliche Steuerung oder finanzielle Anreize stattfinde