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What do people use Graph Neural Networks for?

• tomlue 6y ago
  Chemical structures can be described as graphs.
• Zelazny7 6y ago
  fraud detection
  • lmeyerov 6y ago
    expanding on that: people used to do stuff like pagerank etc. ahead of time and decorate their tabular data with the results: entities, events, whatever for social, fraud, security, customer journey, whatever. that meant a phase separation between graph analytic enrichments and learning. bringing the non-local graph reasoning to the learning phase in a way that isn't slow enables closing the loop. The early graphsage stuff was, afaict, proven for generic social recommendors, but most gnn's I see seem pretty custom (e.g., deepmind's protein folding solution), esp. when not prohibitively slow. It sounds like more generic use is becoming practical w/ these libs, and esp. interesting to me, the latest NIPS had graph transformers papers, which brings another level of practically here. Not sure if DGL & friends have those yet..
• phillypham 6y ago
  Traffic prediction https://deepmind.com/blog/article/traffic-prediction-with-advanced-graph-neural-networks https://deepmind.com/blog/article/traffic-prediction-with-ad...
• deleted 6y ago
  [deleted]
• lovelearning 6y ago
  A routine automation task is information extraction from any kind of document with a visual layout - receipts, PDFs, web pages. [1] [2] [1] : https://nanonets.com/blog/information-extraction-graph-convolutional-networks/ https://nanonets.com/blog/information-extraction-graph-convo... [2] : https://arxiv.org/pdf/1903.11279.pdf https://arxiv.org/pdf/1903.11279.pdf
• visarga 6y ago
  It's a generalization of a fully connected layer, where you give the network an adjacency matrix for data routing between nodes. This allows for designing sparse data flows and learning relations between pairs of input elements. Graph NN layer: f(X, A) = g(AXW), where g is an activation Fully connected layer: f(X) = g(XW), like GNN where the adj. matrix A=I To put it in context a few years ago GNNs became a hot field. They are very similar in a way to transformers because both do pairwise interactions between elements, the difference being that GNNs use explicit and transformers implicit graphs.
  • benibraz 6y ago
    Is this a simplified interpretation of GNNs? because I couldn't find a reference for that. EDIT: found it! https://arxiv.org/pdf/1609.02907.pdf https://arxiv.org/pdf/1609.02907.pdf
• FL33TW00D 6y ago
  They’re seeing some interest in neuroscience as the brain can be represented with a graph structure.
• sooheon 6y ago
  The transformer is a fully connected graph neural network. Read https://arxiv.org/abs/2004.08483 https://arxiv.org/abs/2004.08483 for further ideas.