Identifying influential nodes that lead to faster and wider spreading in complex networks is of theoretical and practical significance. The degree centrality method is very simple but of little relevance. Global metrics such as betweenness centrality and closeness centrality can better identify influential nodes, but are incapable to be applied in large-scale networks due to the computational c...

We introduce a novel centrality metric, the neighbor vector centrality. It is a measurement of node importance with respect to the degree distribution of the node neighbors. This centrality is explored in the context of several networks. We use attack vulnerability simulation to compared our approach with three standard centrality approaches. While for real-world network our method outperforms ...

the aim of this paper is to determine if the centrality degree and density of latin-american and caribbean countries (la-c) predicts their citation based performance in academic research in the management discipline. for our analysis, we use publication data of the social science citation index in the field of ‘‘management’’ between 1988 and 2013, extracted from isi web of science. the resultin...

Recent developments in network theory have allowed for the study of the structure and function of the human brain in terms of a network of interconnected components. Among the many nodes that form a network, some play a crucial role and are said to be central within the network structure. Central nodes may be identified via centrality metrics, with degree, betweenness, and eigenvector centralit...

In this paper, we seek to find a computationally light centrality metric that could serve as an alternate for the computationally heavy betweenness centrality (BWC) metric. In this pursuit, in the first half of the paper, we evaluate the correlation coefficient between BWC and the other commonly used centrality metrics such as Degree Centrality (DEG), Closeness Centrality (CLC), Farness Central...

In many networks with distributed operation and self-organization features, acquiring their global topological information is impractical, if feasible at all. Internet protocols drawing on node centrality indices may instead approximate them with their egocentric counterparts, computed out over the nodes’ ego-networks. Surprisingly, however, in router-level topologies the approximative power of...

Properties of graph representation of genome scale metabolic networks have been extensively studied. However, the relationship between these structural properties and functional properties of the networks are still very unclear. In this paper, we focus on nutritional requirements of organisms as a functional property and study the relationship with structural properties of a graph representatio...

The identification of the most influential spreaders in networks is important to control and understand the spreading capabilities of the system as well as to ensure an efficient information diffusion such as in rumor-like dynamics. Recent works have suggested that the identification of influential spreaders is not independent of the dynamics being studied. For instance, the key disease spreade...

this research examines the association between co-authorship network centrality (degree, closeness, betweenness, eigenvector, bonacich flow betweenness) and productivity of information science researchers. the research population includes all those researchers who have published at least one record in one of the twenty journals of information science which has an impact factor of 0.635 as a min...

Real technological, social and biological networks evolve over time. Predicting their future topology has applications to epidemiology, targetedmarketing, network reliability and routing in ad hoc and peer-to-peer networks. The key problem for such applications is usually to identify thenodes thatwill be inmore important positions in the future. Previous researchers had used ad hoc prediction f...