In complex networks characterized by broad degree distribution, node significance is often associated with its degree or with centrality metrics which relate to its reachability and shortest paths passing through it. Such measures do not consider availability of efficient backup of the node and thus often fail to capture its contribution to the functionality and resilience of the network operat...

Blogging is a popular activity with high impact on marketing, shaping public opinions, and informing the world about major events from a grassroots point of view. Influential bloggers are recognized by businesses as significant forces for product promotion or demotion, and by oppressive political regimes as serious threats to their power. This paper studies the problem of identifying influentia...

This paper proposes a new node centrality measurement index (c-index) and its derivative indexes (iterative c-index and cg-index) to measure the collaboration competence of a node in a weighted network. We prove that c-index observe the power law distribution in the weighted scale-free network. A case study of a very large scientific collaboration network indicates that the indexes proposed in ...

The goal of this paper is to estimate the top k central nodes in a network through parsimonious sampling, in an online fashion. We consider three centrality metrics: degree, betweenness, and closeness centrality. We identify and investigate through simulations the contributions of two sources of error in finding central nodes: (1) sampling (collection) error and (2) identification error. Sampli...

Centrality of a node measures its relative importance within a network. There are a number of applications of centrality, including inferring the influence or success of an individual in a social network, and the resulting social network dynamics. While we can compute the centrality of any node in a given network snapshot, a number of applications are also interested in knowing the potential im...

Traumatic brain injury affects brain connectivity by producing traumatic axonal injury. This disrupts the function of large-scale networks that support cognition. The best way to describe this relationship is unclear, but one elegant approach is to view networks as graphs. Brain regions become nodes in the graph, and white matter tracts the connections. The overall effect of an injury can then ...

The centrality of vertices has been a key issue in network analysis. For unweighted networks where edges are just present or absent and have no weight attached, many centrality measures have been presented, such as degree, betweenness, closeness, eigenvector and subgraph centrality. There has been a growing need to design centrality measures for weighted networks, because weighted networks wher...

Ignet (Integrative Gene Network) is a web-based system for dynamically updating and analyzing gene interaction networks mined using all PubMed abstracts. Four centrality metrics, namely degree, eigenvector, betweenness, and closeness are used to determine the importance of genes in the networks. Different gene interaction types between genes are classified using the Interaction Network Ontology...