SherylHYX/SSSNET_Signed_Clustering
Official code for the SDM2022 paper -- SSSNET: Semi-Supervised Signed Network Clustering.
This tool helps researchers and data scientists analyze networks where relationships can be either positive (e.g., friendship, trust) or negative (e.g., enmity, distrust). It takes a 'signed network' as input, which is a collection of entities connected by relationships, and groups similar entities together. The output is a set of clusters, identifying communities or factions within the network, even with limited initial information about some of the entities.
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Use this if you need to find inherent groupings or communities within a network where connections have both positive and negative sentiments, and you have some, but not complete, information to guide the clustering process.
Not ideal if your network only contains positive relationships, or if you need to analyze evolving networks in real-time.
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Jupyter Notebook
License
MIT
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Last pushed
Oct 13, 2024
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