Static versus dynamic topology of complex communications network during organizational crisis

Uddin, Shahadat; Hossain, Liaquat; Murshed, Shahriar Tanvir Hasan; Crawford, John W.

doi:10.1002/cplx.20349

Cited by 18 publications

(13 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various network measures such as centrality, tie strength and density have gained significant interest in recent years [29], [30] and in many disciplines they play an important role to quantify and identify informal network which functions at level beyond the formal and traditional structure of relationships [31]–[33]. In this study, we use four network measures.…”

Section: Methodsmentioning

confidence: 99%

Network Effects on Scientific Collaborations

2013

Self Cite

View full text Add to dashboard Cite

BackgroundThe 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 FindingsMeasures of social network analysis, for example network centrality and tie strength, have been utilized extensively in current co-authorship literature to explore different behavioural patterns of co-authorship networks. Using three SNA measures (i.e., degree centrality, closeness centrality and betweenness centrality), we explore scientific collaboration networks to understand factors influencing performance (i.e., citation count) and formation (tie strength between authors) of such networks. A citation count is the number of times an article is cited by other articles. We use co-authorship dataset of the research field of ‘steel structure’ for the year 2005 to 2009. To measure the strength of scientific collaboration between two authors, we consider the number of articles co-authored by them. In this study, we examine how citation count of a scientific publication is influenced by different centrality measures of its co-author(s) in a co-authorship network. We further analyze the impact of the network positions of authors on the strength of their scientific collaborations. We use both correlation and regression methods for data analysis leading to statistical validation. We identify that citation count of a research article is positively correlated with the degree centrality and betweenness centrality values of its co-author(s). Also, we reveal that degree centrality and betweenness centrality values of authors in a co-authorship network are positively correlated with the strength of their scientific collaborations.Conclusions/SignificanceAuthors’ network positions in co-authorship networks influence the performance (i.e., citation count) and formation (i.e., tie strength) of scientific collaborations.

show abstract

Section: Methodsmentioning

confidence: 99%

Network Effects on Scientific Collaborations

2013

Self Cite

View full text Add to dashboard Cite

show abstract

“…According to social network analysis topology, a given longitudinal network must be analysed with respect to the temporal aggregation of links among its actors 26 , 27 . The quantification of different aspects of dynamicity as demonstrated by longitudinal networks is dependent on static and dynamic topologies of social network analysis 28 . Due to the temporal nature of SINs, dynamic topologies are exercised on temporal network snapshots, whereas the static topology is applied to an aggregated network.…”

Section: Positional Dynamicitymentioning

confidence: 99%

The optimal window size for analysing longitudinal networks

Uddin

Choudhury

Farhad

et al. 2017

Sci Rep

Self Cite

View full text Add to dashboard Cite

The time interval between two snapshots is referred to as the window size. A given longitudinal network can be analysed from various actor-level perspectives, such as exploring how actors change their degree centrality values or participation statistics over time. Determining the optimal window size for the analysis of a given longitudinal network from different actor-level perspectives is a well-researched network science problem. Many researchers have attempted to develop a solution to this problem by considering different approaches; however, to date, no comprehensive and well-acknowledged solution that can be applied to various longitudinal networks has been found. We propose a novel approach to this problem that involves determining the correct window size when a given longitudinal network is analysed from different actor-level perspectives. The approach is based on the concept of actor-level dynamicity, which captures variability in the structural behaviours of actors in a given longitudinal network. The approach is applied to four real-world, variable-sized longitudinal networks to determine their optimal window sizes. The optimal window length for each network, determined using the approach proposed in this paper, is further evaluated via time series and data mining methods to validate its optimality. Implications of this approach are discussed in this article.

show abstract

“…introductory emails sent to all by most of the students in order to introduce themselves to the whole class) were excluded. Those emails that had a single recipient were considered for research analysis purposes as this type of email reflects more intensive and directed communications (Uddin, Hossain, Murshed, & Crawford, 2011;. After these refinements, the dataset contained 617 emails that were included in the analysis.…”

Section: The Datasetmentioning

confidence: 99%

The impact of study load on the dynamics of longitudinal email communications among students

Uddin

Thompson

Schwendimann

et al. 2014

Computers & Education

Self Cite

View full text Add to dashboard Cite

a b s t r a c tWith the advent of information technology, emails have gained wide acceptability among students as an asynchronous communication tool. According to the current pedagogy literature the overall trend of the use of email communication by university students has been increasing significantly since its inception, despite the rapid growth of the popularity and acceptability of other social mediums (e.g. Mobile phone and Facebook). In this study, we explore a longitudinal email communication network, which evolved under an increasing study load among 38 students throughout a university semester, using measures of social network analysis (SNA) and exponential random graph (ERG) models. This longitudinal network was divided into three waves, where each wave represents the portion of the complete longitudinal network that evolves between two consecutive observations. An increased study load was imposed through the assessment components of the course. SNA measures of degree centrality (i.e. the activity of an actor or actor popularity), betweenness centrality (i.e. the capacity to control the flow of information in a network), closeness centrality (i.e. reachable to other nodes) and reciprocity (i.e. tendency to make reciprocal links) are considered to explore this longitudinal network. ERG models are probabilistic models that are presented by locally determined explanatory variables and can effectively identify structural properties of networks. From the analysis of this email communication network, we notice that students' network positions and behaviours change with the changes in their study load. In particular, we find that (i) students make an increased number of email communications with the increase of study load; (ii) the email communication network become sparse with the increase of study load; and (iii) the 2-star parameter (a subset of three nodes in which one node is connected to each of the other two nodes) and the triangle parameter (a subset of three nodes in which each node is connected to the other two nodes) can effectively explain the formation of network in wave3; whereas, the 3-star parameter (a subset of four nodes in which one node is connected to each of other three nodes) can effectively explain the formation of network in wave1 and wave2. Interpretations of these findings for the monitoring of student behaviour in online learning environments, as well as the implications for the design of assessment and the use of asynchronous tools are discussed in this paper.

show abstract

Static versus dynamic topology of complex communications network during organizational crisis

Cited by 18 publications

References 25 publications

Network Effects on Scientific Collaborations

Network Effects on Scientific Collaborations

The optimal window size for analysing longitudinal networks

The impact of study load on the dynamics of longitudinal email communications among students

Contact Info

Product

Resources

About