TensorFlow Graph Neural Networks
blog.tensorflow.org
TensorFlow Graph Neural Networks
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Re: TensorFlow Graph Neural Networks
#2[deleted]
Re: TensorFlow Graph Neural Networks
#3I’m glad to see support for GNNs with tensorflow. Working with gnns for the past few years, personally for me it gets tiring to roll my own framework.
Re: TensorFlow Graph Neural Networks
#4I’m glad to see support for GNNs with tensorflow. Working with gnns for the past few years, personally for me it gets tiring to roll my own framework.
what's the state of GNN support elsewhere? does everyone else also roll their own, or are folks using Pytorch or something else?
Re: TensorFlow Graph Neural Networks
#5I’m glad to see support for GNNs with tensorflow. Working with gnns for the past few years, personally for me it gets tiring to roll my own framework.
what's the state of GNN support elsewhere? does everyone else also roll their own, or are folks using Pytorch or something else?
This is a big one: https://github.com/pyg-team/pytorch_geometric
Re: TensorFlow Graph Neural Networks
#6I’m glad to see support for GNNs with tensorflow. Working with gnns for the past few years, personally for me it gets tiring to roll my own framework.
what's the state of GNN support elsewhere? does everyone else also roll their own, or are folks using Pytorch or something else?
DGL is the other big one, it supports several frameworks (at least PyTorch and MXNet).
Re: TensorFlow Graph Neural Networks
#7I’m glad to see support for GNNs with tensorflow. Working with gnns for the past few years, personally for me it gets tiring to roll my own framework.
What's an example problem for which such networks work well?
Re: TensorFlow Graph Neural Networks
#8I’m glad to see support for GNNs with tensorflow. Working with gnns for the past few years, personally for me it gets tiring to roll my own framework.
What's an example problem for which such networks work well?
Think of it as an ensemble for blending your normal NN features (ex: RNN for time/clickstreams) with a model that can also leverage useful graph features (document citations, app logins, chemicals connecting, social graphs).
We think a lot about security/fraud and digital journeys, where NN + xgboost are popular in general, and graph is used seperately (or upstream) for looking at broader structure. GNNs help blend these models. For example, in analyzing malicious user accounts (ex: misinfo on twitter), we already get many time/nlp/etc scores for whatever events/entities we look at, and use the social network structure to ensure better propagation/blending, similar to why boosting and ensemble methods became popular to beginwith. Feel free to DM if interested, we are quite excited by this space and working on some things here.
Re: TensorFlow Graph Neural Networks
#9Earlier quoted context omitted.
What's an example problem for which such networks work well?
Think of it as an ensemble for blending your normal NN features (ex: RNN for time/clickstreams) with a model that can also leverage useful graph features (document citations, app logins, chemicals connecting, social graphs). We think a lot about security/fraud and digital journeys, where NN + xgboost are popular in general, and graph is used seperately (or upstream) for looking at broader structure. GNNs help blend t…
I remember reading a bit about GNNs circa 2019. At that time it seemed to have mostly to do with point clouds (for LIDAR data and for 3-D modelling mostly) but I imagine things have changed lots on this front. Are there any interesting papers/resources you could recommend for one to get back up to speed?
Re: TensorFlow Graph Neural Networks
#10I’m glad to see support for GNNs with tensorflow. Working with gnns for the past few years, personally for me it gets tiring to roll my own framework.
what's the state of GNN support elsewhere? does everyone else also roll their own, or are folks using Pytorch or something else?
GeometricFlux for GNNs in Julia: https://github.com/FluxML/GeometricFlux.jl