- A Tutorial on Network Embeddings, H. Chen et al.: https://arxiv.org/abs/1808.02590
- Asymmetric Transitivity Preserving Graph Embedding, M. Ou et al.: https://www.kdd.org/kdd2016/papers/files/rfp0184-ouA.pdf
- The paper behind karateclub:
An API Oriented Open Source Python Framework for Unsupervised Learning on Graphs, B. Rozemberczki et al.: https://arxiv.org/abs/2003.04819 - Paper introducing DeepWalk: Online Learning of Social Representations, B. Perozzi et al.: https://arxiv.org/abs/1403.6652
- node2vec: Scalable Feature Learning for Networks, A. Grover et al., ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 201: https://arxiv.org/abs/1607.00653
- You will find a deeper introduction to GNNs in:
- Chapter 13 of Advanced Deep Learning with Python, I. Vasilev, Packt Publishing.
- Graph Neural Networks: A Review of Methods and Applications, J. Zhou et al.: https://arxiv.org/abs/1812.08434
- A Comprehensive Survey on Graph Neural Networks, Z. Wu et al...
Hands-On Graph Analytics with Neo4j
By :
Hands-On Graph Analytics with Neo4j
By:
Overview of this book
Neo4j is a graph database that includes plugins to run complex graph algorithms.
The book starts with an introduction to the basics of graph analytics, the Cypher query language, and graph architecture components, and helps you to understand why enterprises have started to adopt graph analytics within their organizations. You’ll find out how to implement Neo4j algorithms and techniques and explore various graph analytics methods to reveal complex relationships in your data. You’ll be able to implement graph analytics catering to different domains such as fraud detection, graph-based search, recommendation systems, social networking, and data management. You’ll also learn how to store data in graph databases and extract valuable insights from it. As you become well-versed with the techniques, you’ll discover graph machine learning in order to address simple to complex challenges using Neo4j. You will also understand how to use graph data in a machine learning model in order to make predictions based on your data. Finally, you’ll get to grips with structuring a web application for production using Neo4j.
By the end of this book, you’ll not only be able to harness the power of graphs to handle a broad range of problem areas, but you’ll also have learned how to use Neo4j efficiently to identify complex relationships in your data.
Table of Contents (18 chapters)
Preface
Section 1: Graph Modeling with Neo4j
Free Chapter
Graph Databases
The Cypher Query Language
Empowering Your Business with Pure Cypher
Section 2: Graph Algorithms
The Graph Data Science Library and Path Finding
Spatial Data
Node Importance
Community Detection and Similarity Measures
Section 3: Machine Learning on Graphs
Using Graph-based Features in Machine Learning
Predicting Relationships
Graph Embedding - from Graphs to Matrices
Section 4: Neo4j for Production
Using Neo4j in Your Web Application
Neo4j at Scale
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