arXiv:2301.05749
공식 코드 · Papers with Code
Artificial Benchmark for Community Detection with Outliers (ABCD+o)
원문 초록
The Artificial Benchmark for Community Detection graph (ABCD) is a random graph model with community structure and power-law distribution for both degrees and community sizes. The model generates graphs with similar properties as the well-known LFR one, and its main parameter $\xi$ can be tuned to mimic its counterpart in the LFR model, the mixing parameter $\mu$. In this paper, we extend the ABCD model to include potential outliers. We perform some exploratory experiments on both the new ABCD+o model as well as a real-world network to show that outliers possess some desired, distinguishable properties.
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Community and hyperedge inference in multiple hypergraphs
Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation
Robust Markov stability for community detection at a scale learned based on the structure
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