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Ryan Orban

Ryan Orban

Subject
6 entries

Graph Neural Networks

Bookmarks

  1. A Generalist Neural Algorithmic Learner

  2. A Library for Representing Python Programs as Graphs for Machine Learning

    Google Research library paper introducing python_graphs — an open-source tool for constructing graph representations of Python programs via static analysis, producing control flow graphs, data flow graphs, and program dependence graphs. Standardizes the infrastructure for ML-on-code research so researchers don't each rebuild the graph extraction layer.

  3. Introduction to Graph Neural Networks with JAX/jraph

    DeepMind's interactive introduction to Graph Neural Networks using JAX and jraph — covers message passing, graph classification, and node prediction with runnable code. One of the clearest practical GNN tutorials available as a Google Colab notebook.

  4. A Survey on Graph Representation Learning Methods

    Khoshraftar and An (York University) provide a comprehensive survey of graph representation learning covering node embeddings, GNNs, and knowledge graph methods, with attention to both spectral and spatial approaches. It's the right starting point for anyone orienting to the GRL landscape — methodical coverage from DeepWalk through GAT.

  5. Spectral Graph Embedding

    Thomas Bonald's lecture notes (Institut Polytechnique de Paris, 2019-2020) introduce spectral methods for graph embedding — Laplacian eigenmaps, spectral clustering, and random walk connections — grounding graph representation learning in linear algebra. These notes are foundational for understanding why GCN works and where its limitations come from.

  6. Graph-Powered Machine Learning

    Alessandro Negro's Manning book covers the intersection of graph theory and machine learning, from knowledge graphs and GNNs to fraud detection and recommendations using Neo4j. It's a practical end-to-end treatment that bridges graph databases and ML for practitioners.

All bookmarks