14,809 research outputs found
Student Network Centrality and Academic Performance: Evidence from United Nations University
In this paper we empirically studied the relationship between network centrality and academic performance among a group of 47 PhD students from UNU-MERIT institute. We conducted an independent email survey and relied on social networks theory as well as standard econometric procedures to analyse the data. We found a significant reversed U-shaped relation between network centrality and students' academic performance. We controlled our results by several node's characteristics such as age, academic background, and research area. Additional evidence shows that there is a negative impact of age on academic performance at PhD student level. Contributions of this paper can refer to the input into studies that aim to explore peereffect. Also it contributes to the methodological approach by combining elements of network analysis and econometric theories. This study demonstrates that when evaluating the impact of network centrality on performance, there is no significant difference between various network centrality measurements.Networks analysis, Network centrality, Peer-effect, Academic performance
Anisotropic Radial Layout for Visualizing Centrality and Structure in Graphs
This paper presents a novel method for layout of undirected graphs, where
nodes (vertices) are constrained to lie on a set of nested, simple, closed
curves. Such a layout is useful to simultaneously display the structural
centrality and vertex distance information for graphs in many domains,
including social networks. Closed curves are a more general constraint than the
previously proposed circles, and afford our method more flexibility to preserve
vertex relationships compared to existing radial layout methods. The proposed
approach modifies the multidimensional scaling (MDS) stress to include the
estimation of a vertex depth or centrality field as well as a term that
penalizes discord between structural centrality of vertices and their alignment
with this carefully estimated field. We also propose a visualization strategy
for the proposed layout and demonstrate its effectiveness using three social
network datasets.Comment: Appears in the Proceedings of the 25th International Symposium on
Graph Drawing and Network Visualization (GD 2017
Hierarchy measure for complex networks
Nature, technology and society are full of complexity arising from the
intricate web of the interactions among the units of the related systems (e.g.,
proteins, computers, people). Consequently, one of the most successful recent
approaches to capturing the fundamental features of the structure and dynamics
of complex systems has been the investigation of the networks associated with
the above units (nodes) together with their relations (edges). Most complex
systems have an inherently hierarchical organization and, correspondingly, the
networks behind them also exhibit hierarchical features. Indeed, several papers
have been devoted to describing this essential aspect of networks, however,
without resulting in a widely accepted, converging concept concerning the
quantitative characterization of the level of their hierarchy. Here we develop
an approach and propose a quantity (measure) which is simple enough to be
widely applicable, reveals a number of universal features of the organization
of real-world networks and, as we demonstrate, is capable of capturing the
essential features of the structure and the degree of hierarchy in a complex
network. The measure we introduce is based on a generalization of the m-reach
centrality, which we first extend to directed/partially directed graphs. Then,
we define the global reaching centrality (GRC), which is the difference between
the maximum and the average value of the generalized reach centralities over
the network. We investigate the behavior of the GRC considering both a
synthetic model with an adjustable level of hierarchy and real networks.
Results for real networks show that our hierarchy measure is related to the
controllability of the given system. We also propose a visualization procedure
for large complex networks that can be used to obtain an overall qualitative
picture about the nature of their hierarchical structure.Comment: 29 pages, 9 figures, 4 table
Latent provisions for building information modeling (BIM) contracts: a social network analysis approach
The effective adoption and use of Building Information Modeling (BIM) require appropriate contract design to fairly allocate the contracting parties’ rights and responsibilities. Several standards for BIM protocols and contracts have been developed for the industry. However, the awareness and the use of these are rather limited, leading to unclear provisions in BIM contracts. Therefore, the research aims to identify the influential legal aspects that serve as the latent contract provisions in BIM contracts. A questionnaire survey was conducted to survey experts and active BIM users in construction projects. The data were analyzed using social network analysis (SNA) by assuming interdependent relationships among various the legal aspects in BIM contacts. The key legal aspects associated with BIM contracts pertain to the roles and responsibilities of the project participants. The results also reveal that data security is the center of all latent legal aspects in the contracts. The study provides significant new insights into clarifying the required contract provisions in BIM contracts
Yeast Protein Interactome Topology Provides Framework for Coordinated-Functionality
The architecture of the network of protein-protein physical interactions in
Saccharomyces cerevisiae is exposed through the combination of two
complementary theoretical network measures, betweenness centrality and
`Q-modularity'. The yeast interactome is characterized by well-defined
topological modules connected via a small number of inter-module protein
interactions. Should such topological inter-module connections turn out to
constitute a form of functional coordination between the modules, we speculate
that this coordination is occurring typically in a pair-wise fashion, rather
than by way of high-degree hub proteins responsible for coordinating multiple
modules. The unique non-hub-centric hierarchical organization of the
interactome is not reproduced by gene duplication-and-divergence stochastic
growth models that disregard global selective pressures.Comment: Final, revised version. 13 pages. Please see Nucleic Acids open
access article for higher resolution figure
- …