The general consensus for an Graph-AE is to train against the dense adjacency matrix. However, you only need a dense output. In contrast, the input graph can be sparse. We have an example of this, see ...

Is there a way to convert an adjacency tensor produced by a MLP into a Data object while allowing backprop for a generative adversarial network? The generative adversarial network has an MLP generator ...

In this paper, we propose a progressive two-step algorithm called GIFTS to accelerate GCN inference on CPUs by making use of the dynamic sparsity in the feature matrix and the static sparsity in the ...

In this paper, we describe three graph theoretic heuristics that attempt to determine an optimal planar adjacency graph from a REL chart. Our computational experience suggests that these methods can ...

This is a survey paper on the second largest eigenvalue λ₂ of the adjacency matrix of a graph. Among the topics presented are the graphs with small λ₂, bounds for λ₂, algebraic connectivity, graphs ...

Graph Convolutional Networks (GCNs) are gaining attraction in AI research due to their ability to learn from graph data effectively. However, deploying GCNs on CPUs presents substantial challenges, as ...