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Heterogeneous Graph Representation Learning and Applications

to summarize the latest developments and present cutting-edge research on heterogeneous graph representation learning. To gain the most from;

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Graph Representation Learning

learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and;

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Graph-Based Representations in Pattern Recognition

analysis, and graph theory. They cover topics such as graph edit distance, graph matching, machine learning for graph problems, network and graph;

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Introduction to Graph Neural Networks

in most unsupervised representation learning methods (e.g., network embedding methods). Graph neural networks (GNNs) are proposed to combine the;

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Introduction to Graph Neural Networks

learning methods (e.g., network embedding methods). Graph neural networks (GNNs) are proposed to combine the feature information and the graph;

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Graph-Based Representations in Pattern Recognition

submissions. The accepted papers cover diverse issues of graph-based methods and applications, with 7 in graph representation, 15 in graph matching;

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Graph Algorithms And Applications 3

, constraint systems, databases, graph drawing, graph embedding and layout, knowledge representation, multimedia, software engineering;

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Graph Algorithms And Applications 5

, constraint systems, databases, graph drawing, graph embedding and layout, knowledge representation, multimedia, software engineering;

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Graph Algorithms And Applications 4

, constraint systems, databases, graph drawing, graph embedding and layout, knowledge representation, multimedia, software engineering;

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Robust Representation for Data Analytics

This book introduces the concepts and models of robust representation learning, and provides a set of solutions to deal with real-world;

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Knowledge Graph and Semantic Computing: Knowledge Graph Empowers New Infrastructure Construction

submissions. The papers are organized in topical sections on knowledge extraction: knowledge graph representation and reasoning; knowledge;

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Advances in Knowledge Discovery and Data Mining

; recommender system; social network and graph mining; data pre-processing and feature selection; representation learning and embedding; mining;

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Advances in Knowledge Discovery and Data Mining

; recommender system; social network and graph mining; data pre-processing and featureselection; representation learning and embedding; mining;

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Advances in Knowledge Discovery and Data Mining

; recommender system; social network and graph mining; data pre-processing and featureselection; representation learning and embedding; mining;

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Creativity in Load-Balance Schemes for Multi/Many-Core Heterogeneous Graph Computing

-balance schemes for heterogeneous graph processing, and trends and challenges in large-scale HPC network analysis''--;

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A Graph Theoretic Approach to Heterogeneous Data Clustering

is threefold: homogeneous clustering of images, pairwise heterogeneous data co-clustering, and high-order star-structured heterogeneous data co;

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Graph Learning in Medical Imaging

submissions. The papers focus on major trends and challenges of graph learning in medical imaging and present original work aimed to identify new;

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Graph-Powered Machine Learning

At its core, machine learning is about efficiently identifying patterns and relationships in data. Many tasks, such as finding associations;

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Quantitative Graph Theory

Network complexity measures and other topological indices Quantitative approaches to graphs using machine learning (e.g., clustering;

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Quantitative Graph Theory

Network complexity measures and other topological indices Quantitative approaches to graphs using machine learning (e.g., clustering;

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Graph Transformation for Software Engineers

concepts, notations and techniques of graph transformation. The second part presents and discusses a range of applications to both model-based;

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Graph Transformation for Software Engineers

concepts, notations and techniques of graph transformation. The second part presents and discusses a range of applications to both model-based;

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Knowledge Seeker - Ontology Modelling for Information Search and Management

components. First, it defines the knowledge representation model !V Ontology Graph. Second, an ontology learning process that based on chi-square;

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Graph-Based Representations in Pattern Recognition

This book constitutes the refereed proceedings of the 11th IAPR-TC-15 International Workshop on Graph-Based Representation in Pattern;

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Knowledge Science, Engineering and Management

Processing and Data Mining; Image and Video Data Analysis; Deep Learning; Knowledge Graph and Knowledge Management; Machine Learning; and Knowledge;

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Knowledge Science, Engineering and Management

Processing and Data Mining; Image and Video Data Analysis; Deep Learning; Knowledge Graph and Knowledge Management; Machine Learning; and Knowledge;

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Graph Structures for Knowledge Representation and Reasoning

submissions. The contributions address various issues for knowledge representation and reasoning and the common graph-theoretic background, which;

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