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Found 5 papers, 1 papers with code
Semi-supervised Learning on Large Graphs: is Poisson Learning a Game-Changer?
We explain Poisson learning on graph-based semi-supervised learning to see if it could avoid the problem of global information loss problem as Laplace-based learning methods on large graphs.
Central-Smoothing Hypergraph Neural Networks for Predicting Drug-Drug Interactions
This problem can be formulated as predicting labels (i. e. side effects) for each pair of nodes in a DDI graph, of which nodes are drugs and edges are interacting drugs with known labels.
Learning subtree pattern importance for Weisfeiler-Lehmanbased graph kernels
Graph is an usual representation of relational data, which are ubiquitous in manydomains such as molecules, biological and social networks.
Learning on Hypergraphs with Sparsity
On a hypergraph, as a generalization of graph, one wishes to learn a smooth function with respect to its topology.
