101,464 research outputs found
Limited Attention and Centrality in Social Networks
How does one find important or influential people in an online social
network? Researchers have proposed a variety of centrality measures to identify
individuals that are, for example, often visited by a random walk, infected in
an epidemic, or receive many messages from friends. Recent research suggests
that a social media users' capacity to respond to an incoming message is
constrained by their finite attention, which they divide over all incoming
information, i.e., information sent by users they follow. We propose a new
measure of centrality --- limited-attention version of Bonacich's
Alpha-centrality --- that models the effect of limited attention on epidemic
diffusion. The new measure describes a process in which nodes broadcast
messages to their out-neighbors, but the neighbors' ability to receive the
message depends on the number of in-neighbors they have. We evaluate the
proposed measure on real-world online social networks and show that it can
better reproduce an empirical influence ranking of users than other popular
centrality measures.Comment: in Proceedings of International Conference on Social Intelligence and
Technology (SOCIETY2013
Multilayer Aggregation with Statistical Validation: Application to Investor Networks
Multilayer networks are attracting growing attention in many fields,
including finance. In this paper, we develop a new tractable procedure for
multilayer aggregation based on statistical validation, which we apply to
investor networks. Moreover, we propose two other improvements to their
analysis: transaction bootstrapping and investor categorization. The
aggregation procedure can be used to integrate security-wise and time-wise
information about investor trading networks, but it is not limited to finance.
In fact, it can be used for different applications, such as gene,
transportation, and social networks, were they inferred or observable.
Additionally, in the investor network inference, we use transaction
bootstrapping for better statistical validation. Investor categorization allows
for constant size networks and having more observations for each node, which is
important in the inference especially for less liquid securities. Furthermore,
we observe that the window size used for averaging has a substantial effect on
the number of inferred relationships. We apply this procedure by analyzing a
unique data set of Finnish shareholders during the period 2004-2009. We find
that households in the capital have high centrality in investor networks,
which, under the theory of information channels in investor networks suggests
that they are well-informed investors
When does centrality matter? Scientific productivity and the moderating role of research specialization and cross-community ties
The present study addresses the ongoing debate concerning academic scientific productivity. Specifically, given the increasing number of collaborations in academia and the crucial role networks play in knowledge creation, we investigate the extent to which building social capital within the academic community represents a valuable resource for a scientist's knowledge-creation process. We measure the social capital in terms of structural position within the academic collaborative network. Furthermore, we analyse the extent to which an academic scientist's research specialization and ties that cross-community boundaries act as moderators of the aforementioned relationship. Empirical results derived from an analysis of an Italian academic community from 2001 to 2008 suggest academic scientists that build social capital by occupying central positions in the community outperform their more isolated colleagues. However, scientific productivity declines beyond a certain threshold value of centrality, hence revealing the existence of an inverted U-shaped relationship. This relationship is negatively moderated by the extent to which an academic focuses research activities in few scientific knowledge domains, whereas it is positively moderated by the number of cross-community ties established
Predicting Scientific Success Based on Coauthorship Networks
We address the question to what extent the success of scientific articles is
due to social influence. Analyzing a data set of over 100000 publications from
the field of Computer Science, we study how centrality in the coauthorship
network differs between authors who have highly cited papers and those who do
not. We further show that a machine learning classifier, based only on
coauthorship network centrality measures at time of publication, is able to
predict with high precision whether an article will be highly cited five years
after publication. By this we provide quantitative insight into the social
dimension of scientific publishing - challenging the perception of citations as
an objective, socially unbiased measure of scientific success.Comment: 21 pages, 2 figures, incl. Supplementary Materia
Interlocking directorates and different power forms: An explorative analysis in the Italian context
The purpose of the present paper is twofold. The first is to update the contribution by Drago et al. (2011) about cross-shareholdings and interlocking directorates in Italian listed companies (FTSE MIB) to 31 December 2016 and to reinforce theory of enlarged collusion. The second is to find how interlocking directorates can contribute to understanding the power structure. By using the social network analysis, we map the network structure of interlocking boards and employ centrality measures like degree, eigenvector and betweenness centrality along with the network density and average degree. We interpret eigenvector centrality as a measure of “effective power” of the connections because it can be seen as a weighted sum of not only direct connections but indirect connections, while betweenness centrality as a measure of “potential power” because it is a proxy of the volume of information that passes through the nodes. In this way, we provide a framework for selecting Italian firms with effective and potential power – around whom interactions and processes can be traced and analysed. In addition, we find that the position assumed by the controlling group of the Mediobanca Galaxy is definitely downsized
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