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Found 4 papers, 3 papers with code
Typed Graph Networks
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.
Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network
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.
Learning to Solve NP-Complete Problems - A Graph Neural Network for Decision TSP
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.
Assessing Gender Bias in Machine Translation -- A Case Study with Google Translate
We translate these sentences into English using the Google Translate API, and collect statistics about the frequency of female, male and gender-neutral pronouns in the translated output.
