This is a PyTorch implementation of the GraphATA algorithm, which tries to address the multi-source domain adaptation problem without accessing the labelled source graph. Unlike previous multi-source ...

Abstract: In recent years, reconstructing features and learning node representations by graph autoencoders (GAE) have attracted much attention in deep graph node clustering. However, existing works ...

Freepik CEO Joaquin Cuenca Abela wants creative teams to share their processes. Not just the final outputs, but also the steps. Cuenca argues that progress in AI design will come from exposing ...

Justin Stebbing does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond ...

What if you could unlock the full power of automation without drowning in complexity? Imagine building workflows that feel intuitive yet handle everything from data processing to AI integration, all ...

Guest author Jonathan Goldberg is the founder of D2D Advisory, a multi-functional consulting firm. Jonathan has developed growth strategies and alliances for companies in the mobile, networking, ...

Classic Graph Convolutional Networks (GCNs) often learn node representation holistically, which would ignore the distinct impacts from different neighbors when aggregating their features to update a ...

Abstract: We consider regenerating codes in distributed storage systems where connections between the nodes are constrained by a graph. In this problem, the failed node downloads the information ...

A Bitcoin node is a computer that runs Bitcoin software to validate and relay transactions across the network. Like servers in a traditional financial system, nodes store a complete copy of the ...