Hierarchical closeness

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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:

Centrality degree of connectedness within a graph

In graph theory and network analysis, indicators of centrality identify the most important vertices within a graph. Applications include identifying the most influential person(s) in a social network, key infrastructure nodes in the Internet or urban networks, and super-spreaders of disease. Centrality concepts were first developed in social network analysis, and many of the terms used to measure centrality reflect their sociological origin. They should not be confused with node influence metrics, which seek to quantify the influence of every node in the network.

Network theory study of graphs as a representation of either symmetric relations or, more generally, of asymmetric relations between discrete objects

Network theory is the study of graphs as a representation of either symmetric relations or asymmetric relations between discrete objects. In computer science and network science, network theory is a part of graph theory: a network can be defined as a graph in which nodes and/or edges have attributes.

Graph theory study of graphs, which are mathematical structures used to model pairwise relations between objects

In mathematics, graph theory is the study of graphs, which are mathematical structures used to model pairwise relations between objects. A graph in this context is made up of vertices which are connected by edges. A distinction is made between undirected graphs, where edges link two vertices symmetrically, and directed graphs, where edges, then called arrows, link two vertices asymmetrically; see Graph for more detailed definitions and for other variations in the types of graph that are commonly considered. Graphs are one of the prime objects of study in discrete mathematics.

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.

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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.