Correlation Clustering Algorithm for Dynamic Complete Signed Graphs: An Index-based Approach

Code

• github.com/alishakiba/correlation-clustering-algorithm-for-dynamic-complete-signed-graphs-an-index-based-approach
  OfficialIn papernone★ 1

Abstract

In this paper, we reduce the complexity of approximating the correlation clustering problem from O(m( 2+ (G) )+n) to O(m+n) for any given value of for a complete signed graph with n vertices and m positive edges where (G) is the arboricity of the graph. Our approach gives the same output as the original algorithm and makes it possible to implement the algorithm in a full dynamic setting where edge sign flipping and vertex addition/removal are allowed. Constructing this index costs O(m) memory and O(m(G)) time. We also studied the structural properties of the non-agreement measure used in the approximation algorithm. The theoretical results are accompanied by a full set of experiments concerning seven real-world graphs. These results shows superiority of our index-based algorithm to the non-index one by a decrease of %34 in time on average.

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Reproductions