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Social networks play a fundamental role in the diffusion of innovation through peers’ influence on adoption. Thus, network position including a wide range of network centrality measures has been used to describe individuals’ affinity to adopt an innovation and their ability to propagate diffusion. Yet, social networks are assortative in terms of susceptibility and influence and in terms of network centralities as well. This makes the identification of influencers difficult especially since susceptibility and centrality do not always go hand in hand. Here, we propose the Top Candidate algorithm, an expert recommendation method, to rank individuals based on their perceived expertise, which resonates well with the assortative mixing of innovators and early adopters in networks. Leveraging adoption data from two online social networks that are assortative in terms of adoption but represent different levels of assortativity of network centralities, we demonstrate that the Top Candidate ranking is more efficient in capturing innovators and early adopters than other widely used indices. Top Candidate nodes adopt earlier and have higher reach among innovators, early adopters and early majority than nodes highlighted by other methods. These results suggest that the Top Candidate method can identify good seeds for influence maximization campaigns on social networks.

The social network centrality of Chief Executive Officers (CEOs) has received tremendous attention in recent research. This study examines how CEO network centrality relates to corporate cash holdings. We find a significant negative relation between CEO network centrality and the level of corporate cash holdings, suggesting that firms with higher-centrality CEOs hold less cash. Our results still hold after a battery of robustness checks.

Purpose This study aims to examine the impact of a chief executive officer (CEO) social network centrality on corporate social responsibility (CSR) performance. Design/methodology/approach This study carries out a multivariate linear regression analysis on a panel data sample of 11,507 firm-year observations (representing 1,386 unique US firms) from 2004 to 2014. Findings This paper finds a significant negative relation between CEO network centrality and irresponsible CSR performance (measured as CSR concerns). The findings suggest that better-connected CEOs can better mitigate CSR concerns or weaknesses, leading to improved overall CSR performance of a firm. Originality/value This is the first study that directly examines the empirical link between CEO centrality and CSR performance.

We compiled a social network of directors who were serving concurrently on the boards of several listed companies in China and analyzed the effect of the directors' social network centrality on corporate charitable donation. The results revealed that the directors' social network centrality had differing effects according to whether or not the enterprise was state-owned. Charitable donation of state-owned enterprises was not sensitive to directors' social network centrality, whereas the directors' social network centrality exerted a positive effect on charitable donation among enterprises that were not state-owned. These findings support the application of a political cost perspective to gain a better understanding of the mechanism of charitable donation.

Network analyses on psychopathological data focus on the network structure and its derivatives such as node centrality. One conclusion one can draw from centrality measures is that the node with the highest centrality is likely to be the node that is determined most by its neighboring nodes. However, centrality is a relative measure: knowing that a node is highly central gives no information about the extent to which it is determined by its neighbors. Here we provide an absolute measure of determination (or controllability) of a node - its predictability. We introduce predictability, estimate the predictability of all nodes in 18 prior empirical network papers on psychopathology, and statistically relate it to centrality. We carried out a literature review and collected 25 datasets from 18 published papers in the field (several mood and anxiety disorders, substance abuse, psychosis, autism, and transdiagnostic data). We fit state-of-the-art network models to all datasets, and computed the predictability of all nodes. Predictability was unrelated to sample size, moderately high in most symptom networks, and differed considerable both within and between datasets. Predictability was higher in community than clinical samples, highest for mood and anxiety disorders, and lowest for psychosis. Predictability is an important additional characterization of symptom networks because it gives an absolute measure of the controllability of each node. It allows conclusions about how self-determined a symptom network is, and may help to inform intervention strategies. Limitations of predictability along with future directions are discussed.

A tourist destination most often represents a complex and fragmented system of various stakeholders with interrelated interests that operate on a more or less network principle. Managing and synchronizing to all important destination stakeholders is a very important prerequisite for delivering a quality and competitive tourist product. It is also a very important balance between stakeholder participation in tourism activities / processes and involvement in key destination management decision making. An additional complexity in system synchronization is contributed by sub-network groups grouped around a specific and dominant tourist concept, in this case, nautical activities and processes in the destination. In this respect, it was important to investigate their topological position in relation to other destination stakeholders in order to assess the potential of their common influence on the central actors of destination management, i.e., key destination decision making. For this purpose, a total destination network analysis was performed and a separate analysis of the nautical stakeholder sub-network based on the mathematical graph of the social network and a correlation analysis of the obtained results / parameters of each of them with the level of their participation in key destination management decisions. The results of the analysis have shown that, unlike the general destination network, in the case of nautical sub-networks there is no statistically significant correlation between the topological position (potential of influence) and the level of participation in the most important destination management decisions. Specifically, their topological position is considerably more salient in relation to their participation in destination management decision-making, indicating their specific passivity in this regard and requiring new institutional and organizational solutions by central management structures.

Using data from 138 independent samples, we meta-analytically examined three research questions concerning the roles of personality and network position in organizations. First, how do different personality characteristics—self-monitoring and the Big Five personality traits—relate to indegree centrality and brokerage, the two most studied structurally advantageous positions in organizational networks? Second, how do indegree centrality and brokerage compare in explaining job performance and career success? Third, how do these personality variables and network positions relate to work outcomes? Our results show that self-monitoring predicted indegree centrality (across expressive and instrumental networks) and brokerage (in expressive networks) after controlling for the Big Five traits. Self-monitoring, therefore, was especially relevant for understanding why people differ in their acquisition of advantageous positions in social networks. But the total variance explained by personality ranged between 3% and 5%. Surprisingly, we found that indegree centrality was more strongly related to job performance and career success than brokerage. We also found that personality predicted job performance and career success above and beyond network position and that network position partially mediated the effects of certain personality variables on work outcomes. This paper provides an integrated view of how an individual’s personality and network position combine to influence job performance and career success.

This paper studies the evolution of self-appraisal, social power, and interpersonal influences for a group of individuals who discuss and form opinions about a sequence of issues. Our empirical model combines the averaging rule of DeGroot to describe opinion formation processes and the reflected appraisal mechanism of Friedkin to describe the dynamics of individuals' self-appraisal and social power. Given a set of relative interpersonal weights, the DeGroot–Friedkin model predicts the evolution of the influence network governing the opinion formation process. We provide a rigorous mathematical formulation of the influence network dynamics, characterize its equilibria, and establish its convergence properties for all possible structures of the relative interpersonal weights and corresponding eigenvector centrality scores. The model predicts that the social power ranking among individuals is asymptotically equal to their centrality ranking, that social power tends to accumulate at the top of the hierarchy, and that an autocratic (resp., democratic) power structure arises when the centrality scores are maximally nonuniform (resp., uniform).

Social networks are absolutely a useful and important place for connecting people within the world. A basic issue in a social network is to identify the key persons within it. This is why different centrality measures have been found over the years. In this survey paper, we present past and present research works on measures of centrality in social network. For this plan, we discuss mathematical definitions and different developed centrality measures. We also present some applications of centrality measures in biology, research, security, traffic, transportation, drug, class room. At last, our future research work on centrality measure is given.

Scholars have used network analysis to explore the structural properties of mass ideology. This article incorporates two important, though ignored features in past research by investigating how time shapes the properties of belief network for different populations of people—those who exhibit high and low levels of political knowledge. We find that (1) belief network density increases asymmetrically among people with high relative to low knowledge; (2) symbolic preferences are more central to belief networks irrespective of survey timing or population; in contrast, policy beliefs exhibit some increase in centrality over time among the politically knowledgeable; and (3) a belief's centrality is unrelated to the amount of change it explains in other beliefs. Troublingly, this latter finding presents problems for describing belief networks using the vernacular of Conversian belief systems—a disconnect that seems grounded in the mismatch between Converse's individual-level theory and network analysis' population-based properties.