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Courses

  1. Applied Social Network Analysis in Python by Daniel Romero, University of Michigan
  2. CS224W: Machine Learning with Graphs. Video: 2021 by Jurij Leskovec, Stanford University

Books

  1. Network Science by Albert-László Barabási and Barabasi Lab
  2. Graph Representation Learning Book by William L. Hamilton
  3. Networks, Crowds, and Markets: Reasoning About a Highly Connected World by David Easley and Jon Kleinberg
  4. Graph Algorithms: Practical Examples in Apache Spark and Neo4j by Mark Needham & Amy Hodler Published by O'Reilly Media

Blogs

  1. Research Blog of OCTAVIAN that create new approaches to machine reasoning and graph-based learning.
  2. Thomas Kipf.
  3. Maël Fabien. Introduction to Graphs.

Knowledge Graphs. Tutorials

  1. Tutorial: Build a Knowledge Graph using NLP and Ontologies by Neo4j. Video: 2020
  2. Mining Knowledge Graphs from Text (WSDM, 2018)
  3. Knowledge Graphs: In Theory and Practice (FIRE, 2018)
  4. Knowledge Graphs by Evangelos Kanoulas.
  5. Knowledge Graphs in Natural Language Processing @ ACL 2020
  6. Knowledge Extraction from Unstructured Data is an article about some techniques.

Software

  • Graph

  1. NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
  2. Deep Graph Library is a Python package built for easy implementation of graph neural network model family, on top of existing DL frameworks (e.g. PyTorch, MXNet, Gluon etc.).
  3. PyTorch Geometric GitHub, Documentation, Paper is a geometric deep learning extension library for PyTorch.
  4. OpenKE is an open-source framework for knowledge embedding based on the TensorFlow toolkit. Paper.
  5. Graph Nets is DeepMind's library for building graph networks in Tensorflow and Sonnet. Paper.
  6. Neo4j is a graph database management system. Console.

  • Information Extraction

  1. Stanford OpenIE refers to the extraction of relation tuples, typically binary relations, from plain text. Online Demo of Stanford NLP. Python Wrapper.
  2. Open IE 5.0.
  3. Graphene is an information extraction pipeline which extracts Knowledge Graphs from texts.

Materials

  1. Knowledge Graphs is a list of papers with code related to Knowledge Graphs area.
  2. Geometric Deep Learning is website built for collecting workshops, tutorials, publications and code, that several different researchers has produced in the last years.

Papers

  1. Graphs and neural networks: Reading node properties is a blog paper about a basic Graph-Question-Answer (GQA) tasks. Code.
  2. GCN is a blog article about the main ideas of Graph Convolutional Networks. PyTorch and Tensorflow.
  3. SimplE Embedding for Link Prediction in Knowledge Graphs is paper about SimplE algorithm. PyTorch and Tensorflow.
  4. TuckER: Tensor Factorization for Knowledge Graph Completion. PyTorch

Videos

  1. Small Knowledge Graph by Google.