Liaquat Hossain

Identifying the Effects of Co-Authorship Networks on the Performance of Scholars: A Correlation and Regression Analysis of Performance Measures and Social Network Analysis Measures

In this study, we develop a theoretical model based on social network theories and analytical methods for exploring collaboration (co-authorship) networks of scholars. We use measures from social network analysis (SNA) (i.e., normalized degree centrality, normalized closeness centrality, normalized betweenness centrality, normalized eigenvector centrality, average ties strength, and efficiency) for examining the effect of social networks on the (citation-based) performance of scholars in a given discipline (i.e., information systems). Results from our statistical analysis using a Poisson regression model suggest that research performance of scholars (g-index) is positively correlated with four SNA measures except for the normalized betweenness centrality and the normalized closeness centrality measures. Furthermore, it reveals that only normalized degree centrality, efficiency, and average ties strength have a positive significant influence on the g-index (as a performance measure). The normalized eigenvector centrality has a negative significant influence on the g-index. Based on these results, we can imply that scholars, who are connected to many distinct scholars, have a better citation-based performance (g-index) than scholars with fewer connections. Additionally, scholars with large average ties strengths (i.e., repeated co-authorships) show a better research performance than those with low tie strengths (e.g., single co-authorships with many different scholars). The results related to efficiency show that scholars, who maintain a strong co-authorship relationship to only one co-author of a group of linked co-authors, perform better than those researchers with many relationships to the same group of linked co-authors. The negative effect of the normalized eigenvector suggests that scholars should work with many students instead of other well-performing scholars. Consequently, we can state that the professional social network of researchers can be used to predict the future performance of researchers.

Betweenness centrality as a driver of preferential attachment in the evolution of research collaboration networks

We analyze whether preferential attachment in scientific coauthorship networks is different for authors with different forms of centrality. Using a complete database for the scientific specialty of research about “steel structures,” we show that betweenness centrality of an existing node is a significantly better predictor of preferential attachment by new entrants than degree or closeness centrality. During the growth of a network, preferential attachment shifts from (local) degree centrality to betweenness centrality as a global measure. An interpretation is that supervisors of PhD projects and postdocs broker between new entrants and the already existing network, and thus become focal to preferential attachment. Because of this mediation, scholarly networks can be expected to develop differently from networks which are predicated on preferential attachment to nodes with high degree centrality.

Community Detection in Complex Networks: Multi-objective Enhanced Firefly Algorithm

Studying the evolutionary community structure in complex networks is crucial for uncovering the links between structures and functions of a given community. Most contemporary community detection algorithms employs single optimization criteria (i.e.., modularity), which may not be adequate to represent the structures in complex networks. We suggest community detection process as a Multi-objective Optimization Problem (MOP) for investigating the community structures in complex networks. To overcome the limitations of the community detection problem, we propose a new multi-objective optimization algorithm based on enhanced firefly algorithm so that a set of non-dominated (Pareto-optimal) solutions can be achieved. In our proposed algorithm, a new tuning parameter based on a chaotic mechanism and novel self-adaptive probabilistic mutation strategies are used to improve the overall performance of the algorithm. The experimental results on synthetic and real world complex networks suggest that the multi-objective community detection algorithm provides useful paradigm for discovering overlapping community structures robustly.

ICT Enabled Virtual Collaboration through Trust

The advent of information and communication technology (ICT) provides opportunities for employees with offices in geographically dispersed locations to communicate, share and collaborate on projects to achieve common business goals. Previous studies on computer-mediated communication and computer-supported cooperative work suggest that the higher utilization of ICT for supporting collaborative work is largely dependent on the business strategy, which promotes trust among parties. Our focus is on understanding the effect of virtual organizing for achieving higher collaboration in virtual settings. We identify the challenges for developing trust in a virtual collaborative environment. We describe how the process for virtual organizing helps promote higher levels of collaboration among parties in geographically dispersed locations. We posit that virtual organizing helps support creating, sustaining and deploying key intellectual and knowledge assets while sourcing tangible, physical assets in a complex network of relationships. Our analysis demonstrates that the real challenge for the management of virtual collaboration is trust and has to be guided by a shared business principle or shared vision. Eight propositions are offered based on this analysis. We conclude that virtual organizing as presented here suggests a set of rules and norms enabling and constraining actions that promote a desired and required higher level of trust. This, in turn, is critical (a) to the development and sustainability of virtual collaboration and (b) to ensure the optimal use of ICT.

Actor centrality correlates to project based coordination

In this study, we draw on network centrality concepts and coordination theory to understand how project team members interact when working towards a common goal. A text-mining application based on the constructs of coordination theory was developed to measure the coordinative activity of each employee. Results show that high network centrality is correlated with the ability of an actor to coordinate actions of others in a project group. Furthermore, highly centralised actors coordinate better than others. In conclusion, we suggest implications of appropriate network structure for supporting organisational coordination more effectively and efficiently.

Network Effects on Scientific Collaborations

Background The 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 Findings Measures 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/Significance Authors’ network positions in co-authorship networks influence the performance (i.e., citation count) and formation (i.e., tie strength) of scientific collaborations.

Percolation Centrality: Quantifying Graph-Theoretic Impact of Nodes during Percolation in Networks

A number of centrality measures are available to determine the relative importance of a node in a complex network, and betweenness is prominent among them. However, the existing centrality measures are not adequate in network percolation scenarios (such as during infection transmission in a social network of individuals, spreading of computer viruses on computer networks, or transmission of disease over a network of towns) because they do not account for the changing percolation states of individual nodes. We propose a new measure, percolation centrality, that quantifies relative impact of nodes based on their topological connectivity, as well as their percolation states. The measure can be extended to include random walk based definitions, and its computational complexity is shown to be of the same order as that of betweenness centrality. We demonstrate the usage of percolation centrality by applying it to a canonical network as well as simulated and real world scale-free and random networks.

Communications and coordination in construction projects

Coordination can be seen as a process of managing resources in an organized manner so that a higher degree of operational efficiency can be achieved for a given project. Social network matrices are constructed using different centrality measures. These measurements are used to explore the association between network centrality and coordination for a construction project. Network centrality affects the ability of an individual to coordinate the actions of others. The following questions guide this study: What is the effect of network centrality on coordination? How is the actors ability to coordinate projects related to his or her structural position in the communications network? Multi‐layered test designs were developed to explore this relationship in a project‐based coordination of Dabhol Power Company Construction company and Azurix Corporation. There are three major findings from this analysis. First, centrally positioned actors show more coordinative activity. Second, the betweenness index of centrality is the most potent predicate for coordination. Last, the influence of an actor is associated with coordination more than the actors prominence.

Disaster response preparedness coordination through social networks.

Studies of coordination in human networks have typically presented models that require stable working relationships. These models cannot be applied to emergency response management, which demands distributed coordination in volatile situations. This paper argues that changes to interconnectedness of nodes in a network may have implications for the potential to coordinate. A social network-based coordination model is proposed to explore an organizational actors state of readiness in extreme conditions. To test this hypothesis, the study investigates survey data from state law enforcement, state emergency services and local law enforcement, presenting agency-based (macro) and cross-agency (micro) analysis on 224 completed questionnaires. The main findings are: (i) there is a positive correlation between network connectedness and the potential to coordinate; (ii) the concept of tiers within an emergency response network may exist and be characterized by the sub-network with which an organization associates; (iii) a range or threshold characterizes how interconnected an organization at a given tier should be.

Measuring performance of knowledge-intensive workgroups through social networks

In this article, we examine the effect of social network position, structure, and ties on the performance of knowledge-intensive workers in dispersed occupational communities. Using structural holes and strength-of-tie theory, we develop a theoretical framework and a valid and reliable survey instrument. Second, we apply network and structural holes measures for understanding its association with performance. Empirical results suggest that degree centrality in a knowledge workers’ professional network positively influences performance use, whereas a highly constrained professional network is detrimental to performance. The findings show that social network structure and position are important factors to consider for individual performance.