Matches in SemOpenAlex for { <https://semopenalex.org/work/W2248940257> ?p ?o ?g. }
- W2248940257 abstract "The stochastic block model for one community with parameters $n, K, p,$ and $q$ is considered: $K$ out of $n$ vertices are in the community; two vertices are connected by an edge with probability $p$ if they are both in the community and with probability $q$ otherwise, where $p > q > 0$ and $p/q$ is assumed to be bounded. An estimator based on observation of the graph $G=(V,E)$ is said to achieve weak recovery if the mean number of misclassified vertices is $o(K)$ as $n to infty$. A critical role is played by the effective signal-to-noise ratio $lambda=K^2(p-q)^2/((n-K)q).$ In the regime $K=Theta(n)$, a na{i}ve degree-thresholding algorithm achieves weak recovery in $O(|E|)$ time if $lambda to infty$, which coincides with the information theoretic possibility of weak recovery. The main focus of the paper is on weak recovery in the sublinear regime $K=o(n)$ and $np = n^{o(1)}.$ It is shown that weak recovery is provided by a belief propagation algorithm running for $log^ast(n)+O(1) $ iterations, if $lambda > 1/e,$ with the total time complexity $O(|E| log^*n)$. Conversely, no local algorithm with radius $t$ of interaction satisfying $t = o(frac{log n}{log(2+np)})$ can asymptotically outperform trivial random guessing if $lambda leq 1/e.$ By analyzing a linear message-passing algorithm that corresponds to applying power iteration to the non-backtracking matrix of the graph, we provide evidence to suggest that spectral methods fail to provide weak recovery if $lambda leq 1.$" @default.
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- W2248940257 date "2015-10-09" @default.
- W2248940257 modified "2023-09-26" @default.
- W2248940257 title "Recovering a Hidden Community Beyond the Spectral Limit in O(|E|log * |V|) Time." @default.
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