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Found 6 papers, 2 papers with code
Community Detection with Known, Unknown, or Partially Known Auxiliary Latent Variables
We analyze the conditions for exact recovery when these auxiliary latent variables are unknown, representing unknown nuisance parameters or model mismatch.
Semidefinite Programming for Community Detection with Side Information
SDP is an efficient solution for standard community detection on graphs.
Community Detection: Exact Recovery in Weighted Graphs
In community detection, the exact recovery of communities (clusters) has been mainly investigated under the general stochastic block model with edges drawn from Bernoulli distributions.
Semi-Supervised Node Classification by Graph Convolutional Networks and Extracted Side Information
Another contribution of this paper is relevant to non-graph observations (independent side information) that exists beside a graph realization in many applications.
EXIT Analysis for Community Detection
For two symmetric communities, the asymptotic residual error for belief propagation is calculated under finite-alphabet side information, generalizing a previous result with noisy labels.
Recovering a Single Community with Side Information
Under belief propagation, tight necessary and sufficient conditions for weak recovery are calculated when the LLRs are constant, and sufficient conditions when the LLRs vary with $n$.
