Markov Cluster Shortest Path Founded Upon the Alibi-Breaking Algorithm

Abstract

In this paper, we propose a new variant of the breadth-first shortest path search called Markov Cluster Shortest Path (MCSP). This is applied to the associative semantic network to show us the flow of association between two very different concepts, by providing the shortest path of them. MCSP is obtained from the virtual adjacency matrix of the hard clusters taken as vertices after MCL process. Since each hard cluster grouped by concepts as a result of MCL has no overlap with others, we propose a method called Alibi-breaking algorithm, which calculates the adjacency matrix of them in a way of collecting their past overlapping information by tracing back to the on-going MCL loops. The comparison is made between MCSP and the ordinary shortest paths to know the difference in quality.