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Found 3 papers, 1 papers with code
Correlation Clustering Algorithm for Dynamic Complete Signed Graphs: An Index-based Approach
In this paper, we reduce the complexity of approximating the correlation clustering problem from $O(m\times\left( 2+ \alpha (G) \right)+n)$ to $O(m+n)$ for any given value of $\varepsilon$ for a complete signed graph with $n$ vertices and $m$ positive edges where $\alpha(G)$ is the arboricity of the graph.
Online Correlation Clustering for Dynamic Complete Signed Graphs
Our results show that the dynamic operations have local effects on the neighboring vertices and we employ this locality to reduce the dependency of the running time in the Baseline to the summation of the degree of all vertices in $G_t$, the graph after applying the graph edit operation at time step $t$, to the summation of the degree of the changing vertices (e. g. two endpoints of an edge) and the number of clusters in the previous time step.
A note on belief structures and S-approximation spaces
We study relations between evidence theory and S-approximation spaces.
