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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -11,7 +11,7 @@ GFlowNet-related training and environment code on graphs.

**Primer**

GFlowNet [[1]](https://yoshuabengio.org/2022/03/05/generative-flow-networks/), [[2]](https://www.gflownet.org/), [[3]](https://github.com/zdhNarsil/Awesome-GFlowNets), short for Generative Flow Network, is a novel generative modeling framework, particularly suited for discrete, combinatorial objects. Here in particular it is implemented for graph generation.
GFlowNet [[1]](https://yoshuabengio.org/en/blog/generative-flow-networks), [[2]](https://www.gflownet.org/), [[3]](https://github.com/zdhNarsil/Awesome-GFlowNets), short for Generative Flow Network, is a novel generative modeling framework, particularly suited for discrete, combinatorial objects. Here in particular it is implemented for graph generation.

The idea behind GFN is to estimate flows in a (graph-theoretic) directed acyclic network*. The network represents all possible ways of constructing objects, and so knowing the flow gives us a policy which we can follow to sequentially construct objects. Such a sequence of partially constructed objects is a _trajectory_. *Perhaps confusingly, the _network_ in GFN refers to the state space, not a neural network architecture.

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