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Found 2 papers, 2 papers with code
Robust Graph Clustering via Meta Weighting for Noisy Graphs
We add a learnable weight to each node pair, and MetaGC adaptively adjusts the weights of node pairs using meta-weighting so that the weights of meaningful node pairs increase and the weights of less-meaningful ones (e. g., noise edges) decrease.
SSumM: Sparse Summarization of Massive Graphs
SSumM not only merges nodes together but also sparsifies the summary graph, and the two strategies are carefully balanced based on the minimum description length principle.
Databases Social and Information Networks H.2.8
