Tiago Simas1 and Luis M. Rocha1,2,3

1Cognitive Science Program, Indiana University, Bloomington IN, USA
2School of Informatics, Indiana University, Bloomington IN, USA
3Instituto Gulbenkian de Ciencia, Portugal

Citation: T. Simas and L.M. Rocha [2015]."Distance Closures on Complex Networks" Network Science, 3(2):227-268. doi:10.1017/nws.2015.11

The full text is available. The pre-print is also available: arXiv:1312.2459.


To expand the toolbox available to network science, we study the isomorphism between distance and Fuzzy (proximity or strength) graphs. Distinct transitive closures in Fuzzy graphs lead to closures of their isomorphic distance graphs with widely different structural properties. For instance, the All Pairs Shortest Paths (APSP) problem, based on the Dijkstra algorithm, is equivalent to a metric closure, which is only one of the possible ways to calculate shortest paths in weighted graphs. We show that different closures lead to different distortions of the original topology of weighted graphs. Therefore, complex network analyses that depend on the calculation of shortest paths on weighted graphs should take into account the closure choice and associated topological distortion. We characterise the isomorphism using the max-min and Dombi disjunction/conjunction pairs. This allows us to: (1) study alternative distance closures, such as those based on diffusion, metric, and ultra-metric distances; (2) identify the operators closest to the metric closure of distance graphs (the APSP), but which are logically consistent; and (3) propose a simple method to compute alternative distance closures using existing algorithms for the APSP. In particular, we show that a specific diffusion distance is promising for community detection in complex networks, and is based on desirable axioms for logical inference or approximate reasoning on networks; it also provides a simple algebraic means to compute diffusion processes on networks. Based on these results, we argue that choosing different distance closures can lead to different conclusions about indirect associations on network data, as well as the structure of complex networks, and are thus important to consider.

Keywords: complex networks; network theory; fuzzy graphs; weighted graphs; transitive closure; diffusion distance; network science; modularity; community structure.