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A search space of graph motifs for graph compression : from Powergraphs to triplet concepts

Lucas Bourneuf 1, 2
Abstract : Power Graph Analysis is a lossless graph compression method aiming at reducing the visual complexity of a graph. The process is to detect motifs, cliques and bicliques, which enables the hierarchical clustering of nodes, the grouping of edges, and ultimately a graph reduced to these groups. This thesis exposes first the formalization of the Power Graph Analysis search space, using Formal Concept Analysis as a theoretical ground to express the compression process. Because the independent treatment of two motifs presents some caveats, we propose a unification framework, triplet concepts, which encode a more general motif for compression. Both Power Graph Analysis and the new approach have been implemented in Answer Set Programming (ASP), a logical formalism, and we present some applications in bioinformatics of these two approaches. This thesis ends on the presentation of an high-level specification and visualization environment for graph theory.
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Lucas Bourneuf. A search space of graph motifs for graph compression : from Powergraphs to triplet concepts. Bioinformatics [q-bio.QM]. Université Rennes 1, 2019. English. ⟨NNT : 2019REN1S060⟩. ⟨tel-02509459⟩

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