2 code implementations • 23 Jan 2019 • Marcelo O. R. Prates, Pedro H. C. Avelar, Henrique Lemos, Marco Gori, Luis Lamb
To illustrate the generality of the original model, we present a Graph Neural Network formalisation, which partitions the vertices of a graph into a number of types.
no code implementations • 11 Sep 2018 • Pedro H. C. Avelar, Henrique Lemos, Marcelo O. R. Prates, Luis Lamb
We then show that a GNN can be trained to develop a \emph{lingua franca} of vertex embeddings from which all relevant information about any of the trained centrality measures can be decoded.
3 code implementations • 8 Sep 2018 • Marcelo O. R. Prates, Pedro H. C. Avelar, Henrique Lemos, Luis Lamb, Moshe Vardi
Our model is trained to function as an effective message-passing algorithm in which edges (embedded with their weights) communicate with vertices for a number of iterations after which the model is asked to decide whether a route with cost $<C$ exists.
1 code implementation • 6 Sep 2018 • Marcelo O. R. Prates, Pedro H. C. Avelar, Luis Lamb
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