Demystifying Graph Databases: Analysis and Taxonomy of Data ...
We outline graph database queries and relationships with associated domains (NoSQL stores, graph streaming, and dynamic graph algorithms). Finally, we outline future …
Data-efficient graph learning: Problems, progress, and prospects
His research focuses on data mining, machine learning, graph analytics, and anomaly detection. To date, he has published more than 20 research papers in top-tier …
A key review on graph data science: The power of graphs in …
This comprehensive review provides an in-depth analysis of graph theory, various graph types, and the role of graph visualization in scientific studies. Graphs serve as powerful …
(PDF) A review of graph neural networks: concepts, …
The paper delves into specific GNN models like graph convolution networks (GCNs), GraphSAGE, and graph attention networks (GATs), which are widely used in various applications today.
Demystifying Graph Databases: Analysis and Taxonomy of Data ...
present the first survey and taxonomy of graph database systems. We focus on identifying and analyzing fundamental categories of these systems (e.g., triple stores, tuple stores, native …
[PDF] Graph Databases: Neo4j Analysis
In this paper, it is analyzed the fundamental points of graph databases, showing their main characteristics and advantages. We study Neo4j, the top graph database software in the market and evaluate its performance using the …
Graph Databases: Their Power and Limitations
A graph database is any storage system that uses graph structures with nodes and edges, to represent and store data. The most commonly used model of graphs in the context of graph …
The World of Graph Databases from An Industry Perspective
The purpose of this paper is to provide the research community with an industrial perspective on the graph database landscape, so that graph researcher can better …
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We outline graph database queries and relationships with associated domains (NoSQL stores, graph streaming, and dynamic graph algorithms). Finally, we outline future …
His research focuses on data mining, machine learning, graph analytics, and anomaly detection. To date, he has published more than 20 research papers in top-tier …
This comprehensive review provides an in-depth analysis of graph theory, various graph types, and the role of graph visualization in scientific studies. Graphs serve as powerful …
This paper reviews the existing graph data computational techniques and the research work, to offer the future research line up in graph database management. © Copyright 2018 Institute...
The paper delves into specific GNN models like graph convolution networks (GCNs), GraphSAGE, and graph attention networks (GATs), which are widely used in various applications today.
present the first survey and taxonomy of graph database systems. We focus on identifying and analyzing fundamental categories of these systems (e.g., triple stores, tuple stores, native …
In this paper, it is analyzed the fundamental points of graph databases, showing their main characteristics and advantages. We study Neo4j, the top graph database software in the market and evaluate its performance using the …
A graph database is any storage system that uses graph structures with nodes and edges, to represent and store data. The most commonly used model of graphs in the context of graph …
The purpose of this paper is to provide the research community with an industrial perspective on the graph database landscape, so that graph researcher can better …