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Found 4 papers, 2 papers with code
Semantic Word Clusters Using Signed Spectral Clustering
For spectral clustering using such word embeddings, words are points in a vector space where synonyms are linked with positive weights, while antonyms are linked with negative weights.
Semantic Word Clusters Using Signed Normalized Graph Cuts
Vector space representations of words capture many aspects of word similarity, but such methods tend to make vector spaces in which antonyms (as well as synonyms) are close to each other.
Spectral Theory of Unsigned and Signed Graphs. Applications to Graph Clustering: a Survey
This is a survey of the method of graph cuts and its applications to graph clustering of weighted unsigned and signed graphs.
Notes on Elementary Spectral Graph Theory. Applications to Graph Clustering Using Normalized Cuts
The main thrust of this paper is the method of normalized cuts.
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