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SSSNET: Semi-Supervised Signed Network Clustering. (arXiv:2110.06623v2 [cs.SI] UPDATED)
Jan. 21, 2022, 2:10 a.m. | Yixuan He, Gesine Reinert, Songchao Wang, Mihai Cucuringu
stat.ML updates on arXiv.org arxiv.org
Node embeddings are a powerful tool in the analysis of networks; yet, their
full potential for the important task of node clustering has not been fully
exploited. In particular, most state-of-the-art methods generating node
embeddings of signed networks focus on link sign prediction, and those that
pertain to node clustering are usually not graph neural network (GNN) methods.
Here, we introduce a novel probabilistic balanced normalized cut loss for
training nodes in a GNN framework for semi-supervised signed network
clustering, …
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