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Deep Graph Library: Easy Deep Learning on Graphs

122 points by based2 6y ago

brokensegue 6y ago
Anyone know how this compares to Facebook's recentish work on PyTorch-BigGraph (https://ai.facebook.com/blog/open-sourcing-pytorch-biggraph-for-faster-embeddings-of-extremely-large-graphs/ https://ai.facebook.com/blog/open-sourcing-pytorch-biggraph-...). They claim to be able to embed billions of nodes. I presume this library isn't distributed (quick glance suggests not).
- 101101001010 6y ago
  They serve different purposes. PyTorch-BigGraph (BG) implements a set of algorithms for learning node embeddings (vector representations of each node in the graph) based on the edges (relations) present on a single graph. dgl is a library for graph neural networks (GNNs). The algorithms present in BG can be implemented in dgl, albeit much less efficiently but the reverse might not be true. More specifically GNNs are a set of methods based on what is called "Message Passing" algorithm, where the embedding of each node is a function (parameterized over the model weights) of its neighborhood and the edges that connect the node to it. Additionally GNNs target learning functions that work on multiple graphs for example graphs of molecules to predict their properties, not just a single graph. dgl can be compared to PyTorch Geometric. The former works on both TF 2.0 and Pytorch while the latter is only for PyTorch. Both are almost equivalent, although dgl has some institutions backing it. PyTorch Geometric might feel a bit more lightweight to integrate in existing codebases.
- agentofoblivion 6y ago
  Check this out: https://www.amazon.science/publications/distdgl-distributed-graph-neural-network-training-for-billion-scale-graphs https://www.amazon.science/publications/distdgl-distributed-...
xvilka 6y ago
There is also a GeometricFlux.jl[1] for Julia language and FluxML[2] framework. [1] https://github.com/yuehhua/GeometricFlux.jl https://github.com/yuehhua/GeometricFlux.jl [2] https://fluxml.ai/ https://fluxml.ai/
potatoman22 6y ago
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
Phait 6y ago
For a Tensorflow/Keras alternative check out Spektral: https://graphneural.network https://graphneural.network
pizza 6y ago
Quite handy, also check out dgl-lifesci for utilities dealing with molecule-graph-specific GNNs
steve_g 6y ago
Feeling dumb: Does this thing learn a graph (i.e. build nodes and edges) from more unstructured data like text in order to make relationships explicit? Does it take a knowledge graph and use it as partial input to help learn some other function? Or something else?
- sooheon 6y ago
  The latter. It does not infer graphs from unstructured data, it gives a set of backend agnostic APIs for constructing featurized graphs, message passing and pooling/aggregating (with gradients being handled by your framework of choice), and has some really nice helpers like performing softmax over certain node/edge types, while keeping everything performant.

122 points by based2 6y ago

Anyone know how this compares to Facebook's recentish work on PyTorch-BigGraph (https://ai.facebook.com/blog/open-sourcing-pytorch-biggraph-for-faster-embeddings-of-extremely-large-graphs/ https://ai.facebook.com/blog/open-sourcing-pytorch-biggraph-...). They claim to be able to embed billions of nodes. I presume this library isn't distributed (quick glance suggests not).

101101001010 6y ago
They serve different purposes. PyTorch-BigGraph (BG) implements a set of algorithms for learning node embeddings (vector representations of each node in the graph) based on the edges (relations) present on a single graph. dgl is a library for graph neural networks (GNNs). The algorithms present in BG can be implemented in dgl, albeit much less efficiently but the reverse might not be true. More specifically GNNs are a set of methods based on what is called "Message Passing" algorithm, where the embedding of each node is a function (parameterized over the model weights) of its neighborhood and the edges that connect the node to it. Additionally GNNs target learning functions that work on multiple graphs for example graphs of molecules to predict their properties, not just a single graph. dgl can be compared to PyTorch Geometric. The former works on both TF 2.0 and Pytorch while the latter is only for PyTorch. Both are almost equivalent, although dgl has some institutions backing it. PyTorch Geometric might feel a bit more lightweight to integrate in existing codebases.
agentofoblivion 6y ago
Check this out: https://www.amazon.science/publications/distdgl-distributed-graph-neural-network-training-for-billion-scale-graphs https://www.amazon.science/publications/distdgl-distributed-...

xvilka 6y ago

There is also a GeometricFlux.jl[1] for Julia language and FluxML[2] framework. [1] https://github.com/yuehhua/GeometricFlux.jl https://github.com/yuehhua/GeometricFlux.jl [2] https://fluxml.ai/ https://fluxml.ai/

potatoman22 6y ago

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

Phait 6y ago

For a Tensorflow/Keras alternative check out Spektral: https://graphneural.network https://graphneural.network

pizza 6y ago

Quite handy, also check out dgl-lifesci for utilities dealing with molecule-graph-specific GNNs

steve_g 6y ago

Feeling dumb: Does this thing learn a graph (i.e. build nodes and edges) from more unstructured data like text in order to make relationships explicit? Does it take a knowledge graph and use it as partial input to help learn some other function? Or something else?

sooheon 6y ago
The latter. It does not infer graphs from unstructured data, it gives a set of backend agnostic APIs for constructing featurized graphs, message passing and pooling/aggregating (with gradients being handled by your framework of choice), and has some really nice helpers like performing softmax over certain node/edge types, while keeping everything performant.