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Power graph analysis
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<p>In <a href="/facts/Computational_biology/GxgNfwcX">computational biology</a>, power graph analysis is a method for the analysis and representation of <a href="/facts/Complex_network/6zTa9z4e">complex networks</a>. Power graph analysis is the computation, analysis and visual representation of a power graph from a <a href="/facts/Graph_(discrete_mathematics)/kw3eIBUe">graph</a> (<a href="/facts/Network_(mathematics)/kw3eIBUe">networks</a>). </p><p>Power graph analysis can be thought of as a <a href="/facts/Lossless_data_compression/LS702KsN">lossless compression algorithm</a> for graphs. It extends graph syntax with representations of <a href="/facts/Clique_(graph_theory)/XK9ppLz5">cliques</a>, <a href="/facts/Complete_bipartite_graph/C1RYIdpX">bicliques</a> and <a href="/facts/Star_graph/PNW0H6eK">stars</a>. Compression levels of up to 95% have been obtained for complex <a href="/facts/Biological_network/HonxLhWT">biological networks</a>. </p><p><a href="/facts/Hypergraph/qIR2o2I3">Hypergraphs</a> are a generalization of graphs in which <a href="/facts/Edge_(graph_theory)/W0MqKkyE">edges</a> are not just couples of <a href="/facts/Node_(graph_theory)/WhlWmL3o">nodes</a> but arbitrary <a href="/facts/N-tuple/BI1HIxL8">n-tuples</a>. Power graphs are not another generalization of graphs, but instead a novel representation of graphs that proposes a shift from the "node and edge" language to one using cliques, bicliques and stars as primitives. </p>