Hierarchical closeness

Last updated

Hierarchical closeness (HC) is a structural centrality measure used in network theory or graph theory. It is extended from closeness centrality to rank how centrally located a node is in a directed network. While the original closeness centrality of a directed network considers the most important node to be that with the least total distance from all other nodes, hierarchical closeness evaluates the most important node as the one which reaches the most nodes by the shortest paths. The hierarchical closeness explicitly includes information about the range of other nodes that can be affected by the given node. In a directed network where is the set of nodes and is the set of interactions, hierarchical closeness of a node called was proposed by Tran and Kwon [1] as follows:

where:

In the formula, represents the number of nodes in that can be reachable from . It can also represent the hierarchical position of a node in a directed network. It notes that if , then because is . In cases where , the reachability is a dominant factor because but . In other words, the first term indicates the level of the global hierarchy and the second term presents the level of the local centrality.

Application

Hierarchical closeness can be used in biological networks to rank the risk of genes to carry diseases.

Related Research Articles

In computer science, the method of contraction hierarchies is a speed-up technique for finding the shortest-path in a graph. The most intuitive applications are car-navigation systems: a user wants to drive from to using the quickest possible route. The metric optimized here is the travel time. Intersections are represented by vertices, the road sections connecting them by edges. The edge weights represent the time it takes to drive along this segment of the road. A path from to is a sequence of edges ; the shortest path is the one with the minimal sum of edge weights among all possible paths. The shortest path in a graph can be computed using Dijkstra's algorithm but, given that road networks consist of tens of millions of vertices, this is impractical. Contraction hierarchies is a speed-up method optimized to exploit properties of graphs representing road networks. The speed-up is achieved by creating shortcuts in a preprocessing phase which are then used during a shortest-path query to skip over "unimportant" vertices. This is based on the observation that road networks are highly hierarchical. Some intersections, for example highway junctions, are "more important" and higher up in the hierarchy than for example a junction leading into a dead end. Shortcuts can be used to save the precomputed distance between two important junctions such that the algorithm doesn't have to consider the full path between these junctions at query time. Contraction hierarchies do not know about which roads humans consider "important", but they are provided with the graph as input and are able to assign importance to vertices using heuristics.

References

  1. Tran, T.-D. and Kwon, Y.-K. Hierarchical closeness efficiently predicts disease genes in a directed signaling network, Computational biology and chemistry.
  2. Sabidussi, G. (1966) The centrality index of a graph, Psychometrika, 31, 581-603 %G English
  3. Opsahl, T., Agneessens, F. and Skvoretz, J. (2010) Node centrality in weighted networks: Generalizing degree and shortest paths, Social networks, 32, 245-251.