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Managing and Mining Graph Data

Managing and Mining Graph Data is a comprehensive survey book in graph management and mining. It contains extensive surveys on a variety;

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Managing and Mining Graph Data

Managing and Mining Graph Data is a comprehensive survey book in graph management and mining. It contains extensive surveys on a variety;

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Graph Data Mining

applications in fields such as bioinformatics, traffic network, scientific collaboration, world wide web and social networks. Graph data mining is used;

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Practical Graph Mining with R

Discover Novel and Insightful Knowledge from Data Represented as a GraphPractical Graph Mining with R presents a do-it-yourself approach;

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Advances in Knowledge Discovery and Data Mining

from 458 submissions. They are organized in topical sections named: classification and deep learning; social network and graph mining; privacy;

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Advances in Knowledge Discovery and Data Mining

from 458 submissions. They are organized in topical sections named: classification and deep learning; social network and graph mining; privacy;

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Web Information Systems Engineering WISE 2020

organized in the following topical sections: Part I: network embedding; graph neural network; social network; graph query; knowledge graph;

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Web Information Systems Engineering WISE 2020

organized in the following topical sections: Part I: network embedding; graph neural network; social network; graph query; knowledge graph;

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Mining Graph Data

This text takes a focused and comprehensive look at mining data represented as a graph, with the latest findings and applications in both;

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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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Web Information Systems Engineering - WISE 2019

linkage and disambiguation; graph learning; knowledge graphs; graph mining; and text mining.;

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Advanced Data Mining and Applications

algorithms, mining on data streams, graph mining, spatial data mining, multimedia data mining, Web mining, the Internet of Things, health informatics;

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Graph Theory

; the algebraic structure of graphs; the combination of graph theory and unsupervised learning applied to social data mining; organising and;

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Advanced Data Mining and Applications

submissions. The papers were organized in topical sections named: Machine Learning; Text Mining; Graph Mining; Predictive Analytics; Recommender;

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Web and Big Data

were carefully reviewed and selected from 184 submissions. The papers are organized around the following topics: Graph Mining; Data Mining;

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Graph-theoretic Techniques For Web Content Mining

This book describes exciting new opportunities for utilizing robust graph representations of data with common machine learning algorithms;

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Web and Big Data

Analytics; Data and Information Quality; Data Mining and Application; Graph Data and Social Networks; Information Extraction and Retrieval;

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Web and Big Data

Analytics; Data and Information Quality; Data Mining and Application; Graph Data and Social Networks; Information Extraction and Retrieval;

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Data Mining

, spatiotemporal data, and graph/networks), data mining applications (such as sentiment analysis, truth discovery, and information propagattion), data;

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Matrix Methods in Data Mining and Pattern Recognition

mining and pattern recognition. Adopting an application-oriented approach, the author introduces matrix theory and decompositions, describes how;

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Web and Big Data

submissions. The papers are organized around the following topics: Big Data Analytics; Graph Data and Social Networks; Knowledge Graph; Recommender;

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Web and Big Data

submissions. The papers are organized around the following topics: Big Data Analytics; Graph Data and Social Networks; Knowledge Graph; Recommender;

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Advances in Knowledge Discovery and Data Mining

methods and algorithms; opinion mining and sentiment analysis; clustering; feature extraction and pattern mining; graph and network data;

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Advances in Knowledge Discovery and Data Mining

methods and algorithms; opinion mining and sentiment analysis; clustering; feature extraction and pattern mining; graph and network data;

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Graph-based Natural Language Processing and Information Retrieval

Graph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these;

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