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Graph Representation Learning

this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings;

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Heterogeneous Graph Representation Learning and Applications

Representation learning in heterogeneous graphs (HG) is intended to provide a meaningful vector representation for each node so as to;

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Graph-Based Representations in Pattern Recognition

This book constitutes the refereed proceedings of the 12th IAPR-TC-15 International Workshop on Graph-Based Representation in Pattern;

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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

networks (RNNs). Nodes in graphs usually contain useful feature information that cannot be well addressed in most unsupervised representation;

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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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Graph Structures for Knowledge Representation and Reasoning

contributions address various issues for knowledge representation and reasoning and the common graph-theoretic background allows to bridge the gap;

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Graph Structures for Knowledge Representation and Reasoning

papers feature current research involved in the development and application of graph-based knowledge representation formalisms and reasoning;

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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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Graph-Based Representation and Reasoning

, respectively. The topics covered are: conceptual structures, knowledge representation, reasoning, conceptual graphs, formal concept analysis, semantic;

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Graph Structures for Knowledge Representation and Reasoning

papers feature current research involved in the development and application of graph-based knowledge representation formalisms and reasoning;

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Graph-Based Representations in Pattern Recognition

This book constitutes the refereed proceedings of the 10th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern;

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Deep Learning

, graph representation, and forecasting.;

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Deep Learning: Concepts and Architectures

, graph representation, and forecasting.;

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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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Graph-based Knowledge Representation

This book provides a de?nition and study of a knowledge representation and r- soning formalism stemming from conceptual graphs, while;

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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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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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Knowledge Graph and Semantic Computing: Knowledge Computing and Language Understanding

wide research fields including the knowledge graph, the semantic Web, linked data, NLP, information extraction, knowledge representation and;

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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-Powered Machine Learning

naturally expressed as graphs. Graph-Powered Machine Learning introduces you to graph technology concepts, highlighting the role of graphs;

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Graph Learning in Medical Imaging

This book constitutes the refereed proceedings of the First International Workshop on Graph Learning in Medical Imaging, GLMI 2019, held;

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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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Graph Kernels

Among the data structures commonly used in machine learning, graphs are arguably one of the most general. Graphs allow the modelling;

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