We analyze directed, unweighted graphs obtained from by connecting vertex to iff . Examples of such graphs include -nearest neighbor graphs, where varies from point to point, and, arguably, many real world graphs such as co-purchasing graphs. We ask whethe…
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3 results for “Degree-Scaling”
PNA improves GNNs for graph data with multiple aggregators.
problem Capturing continuous features in graph neural networks.
method Combines multiple aggregators with degree-scalers.
result PNA outperforms existing models on graph theory and real-world tasks.
This paper studies node embeddings of networks, revealing their geometric properties.
problem Understanding the geometric properties of node embeddings in random networks.
method Characterization of ergodic limits, generalization, and convex relaxations of random walk node embedding objectives.
result The optimal node embedding Grammians have rank 1 for a nuclear norm relaxation of the non-randomized objective.