GitHub - pykeen/pykeen: 🤖 A Python library for learning and evaluating knowledge graph embeddingsYou signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session. Dismiss alert
Graph-Powered Machine Learning
Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data. In Graph-Powered Machine Learning, you will learn: The lifecycle of a machine learning project Graphs in big data platforms Data source modeling using graphs Graph-based natural language processing, recommendations, and fraud detection techniques Graph algorithms Working with N
Top Trends of Graph Machine Learning in 2020
SourceThe year 2020 has just started but we can already see the trends of Graph Machine Learning (GML) in the latest research papers. Below is my view on what will be important in 2020 for GML and the discussion of these papers. IntroductionThe goal of this article is not on introducing the basic concepts of GML such as graph neural networks (GNNs), but on exposing cutting-edge research that we ca
CS224W | Home
Content What is this course about? Complex data can be represented as a graph of relationships between objects. Such networks are a fundamental tool for modeling social, technological, and biological systems. This course focuses on the computational, algorithmic, and modeling challenges specific to the analysis of massive graphs. By means of studying the underlying graph structure and its features
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KR2ML
Sanja Fidler
Contextualizing Trending Entities in News Stories | Marco Ponza
Bloomberg - Are you a robot?
Graph-powered Machine Learning at Google
KR2ML
https://kr2ml.github.io/papers/
Machine Learning on Knowledge Graphs @ NeurIPS 2020
Open Graph Benchmark
Real-time Recommendations using Spark Comcast Labs