Identifying the most influential nodes in networked systems is of vital importance to optimize their function and control. Several scalar metrics have been proposed that effect, but recent shift focus towards network structures which go beyond a simple collection dyadic interactions has rendered them void performance guarantees. We here introduce new measure node's centrality, no longer value, ...

We introduce a new measure of centrality, the information centrality C , based on the concept of efficient propagation of information over the network. C is defined for both valued and non-valued graphs, and applies to groups and classes as well as individuals. The new measure is illustrated and compared to the standard centrality measures by using a classic network data set.

In this paper, we present and compare various centrality measures for graphbased keyphrase extraction. Through experiments carried out on three standard datasets of different languages and domains, we show that simple degree centrality achieve results comparable to the widely used TextRank algorithm, and that closeness centrality obtains the best results on short documents.

Results of correlation study (using Pearson's correlation coefficient, PCC) between decay centrality (DEC) vs. degree centrality (DEG) and closeness centrality (CLC) for a suite of 48 real-world networks indicate an interesting trend: PCC(DEC, DEG) decreases with increase in the decay parameter δ (0 < δ < 1) and PCC(DEC, CLC) decreases with decrease in δ. We make use of this trend of monotonic ...

background and objectives: while social network analysis has left a remarkable practical impact in the healthcare field, the potential implication of this methodology in the primary health domain is poorly researched. hence, this study aimed to explore the use and usefulness social network analysis in the context of primary health care. methods: the health volunteers of imam ali health center i...

Measuring the importance of a node in a network is a major goal in the analysis of social networks, biological systems, transportation networks etc. Different centrality measures have been proposed to capture the notion of node importance. For example, the center of a graph is a node that minimizes the maximum distance to any other node (the latter distance is the radius of the graph). The medi...

In recent decades, a number of centrality metrics describing network properties of nodes have been proposed to rank the importance of nodes. In order to understand the correlations between centrality metrics and to approximate a high-complexity centrality metric by a strongly correlated low-complexity metric, we first study the correlation between centrality metrics in terms of their Pearson co...

BACKGROUND The analysis of co-authorship network aims at exploring the impact of network structure on the outcome of scientific collaborations and research publications. However, little is known about what network properties are associated with authors who have increased number of joint publications and are being cited highly. METHODOLOGY/PRINCIPAL FINDINGS Measures of social network analysis...

We consider a broad class of walk-based, parameterized node centrality measures based on functions of the adjacency matrix. These measures generalize various well-known centrality indices, including Katz and subgraph centrality. We show that the parameter can be “tuned” to interpolate between degree and eigenvector centrality, which appear as limiting cases. We also highlight the roles played b...

Closeness centrality is an important concept in social network analysis. In a graph representing a social network, closeness centrality measures how close a vertex is to all other vertices in the graph. In this paper, we combine existing methods on calculating exact values and approximate values of closeness centrality and present new algorithms to rank the top-k vertices with the highest close...