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Neo4j Graph Data Modeling

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Building Web Applications with Python and Neo4j

Is ForIf you are a Python developer and want to expand your understanding of Python-based web applications over Neo4j graph data models, this;

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Graph Algorithms Practical Examples in Apache Spark and Neo4j

and Neo4j, two of the most common choices for graph analytics. Learn how graph analytics reveal more predictive elements in today's data;

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Graph-Powered Machine Learning

neighbor or page rank where it's important to quickly identify and exploit relevant relationships. Modern graph data stores, like Neo4j or Amazon;

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Neo4j - A Graph Project Story

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Learning Neo4j

This book is for developers who want an alternative way to store and process data within their applications. No previous graph database;

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Graph Databases

Discover how graph databases can help you manage and query highly connected data. With this practical book, you'll learn how to design and;

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Visual Design of GraphQL Data

as with Neo4j graph databases. Spending time on schema quality means that you will work from sharper definitions, which in turn leads to;

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Querying Graphs

Graph data modeling and querying arises in many practical application domains such as social and biological networks where the primary;

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Graph Data Modeling for NoSQL & SQL

Master a graph data modeling technique superior to traditional data modeling for both relational and NoSQL databases (graph, document, key;

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An Introduction to Exponential Random Graph Modeling

Explaining the techniques and applications of exponential random graph modeling (ERGM) for social scientists, this is a uniquely;

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Hands-On Big Data Modeling

Developer Data Modeler to create your own data models containing different file types such as CSV, XML, and JSON. You'll also learn to create graph;

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The Practitioner's Guide to Graph Data Applying Graph Thinking and Graph Technologies to Solve Complex Problems

Graph data closes the gap between the way humans and computers view the world. While computers rely on static rows and columns of data;

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Graph Database Modeling

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Database and Expert Systems Applications

; Data Mining and Machine Learning; Recommender Systems and Query Recommendation; Graph Algorithms; Semantic Clustering and Data Classific;

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Database and Expert Systems Applications

; Data Mining and Machine Learning; Recommender Systems and Query Recommendation; Graph Algorithms; Semantic Clustering and Data Classification.;

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Modeling and Management of Fuzzy Semantic RDF Data

, fuzzy RDF graph matching, query of fuzzy RDF data, and persistence of fuzzy RDF data in diverse databases. The objective of the book is to;

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Graph Data Mining

Graph data is powerful, thanks to its ability to model arbitrary relationship between objects and is encountered in a range of real-world;

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Introduction to Graph Neural Networks

Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints;

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Introduction to Graph Neural Networks

, graph recurrent networks, graph attention networks, graph residual networks, and several general frameworks. Graphs are useful data;

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Electric Power

feeders. Topics cover: (1) Data extraction from geographic information systems (GIS), (2) Graph modeling of distribution feeders, (3) Programming;

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Electric Power

feeders. Topics cover: (1) Data extraction from geographic information systems (GIS), (2) Graph modeling of distribution feeders, (3) Programming;

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System Dynamics and Control with Bond Graph Modeling

Written by a professor with extensive teaching experience, System Dynamics and Control with Bond Graph Modeling treats system dynamics from;

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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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Dynamic Systems Biology Modeling and Simulation

using structural (multicompartmental and network) models and graph theory; and analyzing structural and measurement (data) models for;

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Web and Big Data

and data quality; graph data processing; data mining, privacy and semantic analysis; text and log data management; social networks; data;

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