Alireza Abbasi

h-Type hybrid centrality measures for weighted networks

Alireza Abbasi — Mon, 21 Jan 2013 19:40:45 PST

In this study, new centrality (collaborative) measures are proposed for a node in weighted networks in three different categories. The bibliometric indicators’ concepts (e.g., h-index and g-index) are applied to the network analysis measures in order to introduce the new centrality measures. First category of measures (i.e., l-index, al-index and gl-index) only considers a node’s neighbors’ degree. Second category of measures (i.e., h-Degree, a-Degree and g-Degree) takes into account the links’ weights of a node in a weighted network. Third category of measures (i.e., Hw-Degree, Aw-Degree and Gw-Degree) combines both neighbors’ degree and their links’ weight. Using a co-authorship network, the association between these new measures and the existing measures with scholars’ performance is examined to show the applicability of the new centrality measures. The analysis shows that the scholars’ citation-based performances measures are significantly associated with all the proposed centrality measures but the correlation coefficient for the ones based on average indicators (i.e, a-Degree and Aw-Degree) is the highest.

Investigating Preferential Attachment Behavior over the Evolution of Disaster Response Networks

Alireza Abbasi et al. — Sun, 16 Sep 2012 00:05:32 PDT

This study aims to understand the mechanisms of disaster response network evolution by quantitatively examining the actors’ attachment behaviors in a real disaster collaboration networks. We aim to do this by identifying the characteristic of existing actors and its impact in forming new connection over time. To quantify actors’ attachment logics (i.e., preferential attachment), different options of attachments (between and among new and existing actors) are considered. The result indicates the existence of cumulative advantage for actors involved in a response network to a disaster. We argue that by understanding the mechanisms of network evolution, we can predict more precisely how the behavior of actors and network structure evolve over time. This can assist researchers, decision makers or practitioners to manage and support collaboration of actors in their systems for reaching their organizational goals. The overall findings of this study can contribute further to the development of network organizational theory, organizational learning theory and self-organizations in different contexts especially disaster and emergency response management.

Structural Dynamics of Organizations during the Evolution of Interorganizational Networks in Disaster Response

Alireza Abbasi et al. — Sat, 14 Apr 2012 06:14:42 PDT

This study analyzes the evolution of interorganizational response networks over time. The study aims to investigate an organization‟s position and role as a result of the dynamic changes that occur through the evolution of interorganizational response networks. Social network analysis measures are applied in order to evaluate not only network structure and specifications but also an organizations focal structure and role changes over time. Using Hurricane Charley‟s coordination data of the activities of several organizations involved in the response process over time, the study evaluates networks and the organizations‟ network structural changes over a period of time. The results show that analyzing static networks does not reflect how the network evolves and how different organizations change their role as the incident emerges. This study takes these criticisms into consideration and adds time dimension to the analysis of response networks. It is the first step in investigating the emerging structure of interorganizational response dynamics during emerging disasters and its effect on improving coordination output. In addition, the study proposes indicators for identifying real, lead or coordinating organizations in interorganizational networks.

Effects of Networks on Learning during Emergency Events

Jafar Hamra et al. — Wed, 21 Mar 2012 03:46:46 PDT

This paper aims to explore the inherent relationship between social networks and learning within the context of emergency management. We hypothesize using social network theory as a framework for analysis, that changes to interconnectedness of actors in their social network are implicated in the potential for those actors to learn and improvise in dynamically changing and emergent conditions. To test our hypotheses, we investigate survey data which was collected as part of a research study with the support of the Australian Bushfire Co-operative Research Centre (CRC). Results show that increases in actors’ involvement within the social emergency management network influences the ability of those actors to engage in learning-related work activity. This means that these actors are better able to adapt and improvise in complex emergency events. As an area of further research, it would be useful to apply the existing theoretical model to the context of another domain, preferably one that shares characteristics of uncertainty and unstable environments. Most existing studies of learning theory in human networks have focused on learning in situations requiring stable working relationships with no environmental uncertainties. In this paper, we argue that the designs of existing models are useful as a building block, yet flawed for application within the context of disaster management. By presenting a model of learning-related work activity, based on network connectedness, personnel within emergency services organizations can strengthen their capacity to be flexible and adaptable.

Measuring Social Capital and its influence on Individual Performance

Alireza Abbasi et al. — Sun, 11 Dec 2011 12:20:28 PST

Studies on social networks highlight the importance of network structure or structural properties of a given network and its impact on performance outcome. The empirical validation of the association between network structures and performance has been well documented in a number of recent studies. One of the important properties of this network structure is referred as “social capital” which is the “network of contacts” and the associated values attached to these networks of contacts. There are very few systematic empirical studies suggesting a role of co-authors, as social capital in their scientific collaboration network and their effect on performance. In this study, our aim is to provide empirical evidence of the influence of social capital and performance within the context of academic collaboration. We suggest that the collaborative process involves social capital embedded within relationships and network structures among direct co-authors. Thus, we examine whether scholars’ social capital is associated with their citation-based performance, using co-authorship and citation data. In order to test and validate our proposed hypotheses, we extract publication records from Scopus having “information science” in their title or keywords or abstracts during 2001 and 2010. To overcome the limitations of traditional social network metrics for measuring the influence of scholars’ social capital within their co-authorship network, we extend the traditional social network metrics by proposing a new measure (Power-Diversity Index). We then use Spearman’s correlation rank test to examine the association between scholars’ social capital measures and their citation-based performance. Results suggest that research performance of authors is positively correlated with their social capital measures. This study highlights the importance of scholars’ social capital characteristics on their performance suggesting stronger links to more powerful contacts will lead to better performance and, therefore, their respective professional social network shows indicative outcomes to evaluate and predict the performance of scholars. It further highlights that the Power-diversity Index, which is introduced as a new hybrid centrality measure, serves as an indicator of power and influence of an individual’s ability to control communication and information.

Exploring the relationship between research impact and collaborations for Information Science

Alireza Abbasi — Mon, 10 Oct 2011 04:28:22 PDT

In this study, we examine whether internal and external collaboration (considering institutional and national levels) among scientific associates with research impact relates to research performance. We extract data from Scopus, a bibliometric database, searching for journal publications having ‘information science’ phrase in their title or keywords or abstracts and retrieve totally 4,579 publications. Spearman correlation rank test is used to examine the hypotheses. Analysis of collected data shows that publication’s impact is significantly and positively associated with collaboration indicators based on authors’ affiliations. However, internal collaborations at both institutional and national levels show stronger associations with publications’ impact than external collaborations do.

Analyzing Academic Communities’ Collaboration and Performance

Alireza Abbasi et al. — Thu, 28 Jul 2011 22:23:43 PDT

Egocentric Analysis of Co-authorship Network Structure, Position and Performance

Alireza Abbasi — Fri, 20 May 2011 04:03:38 PDT

Investigating Attachment Behavior of Nodes during Evolution of a Complex Social Network: a case of a scientific collaboration

Alireza Abbasi — Fri, 20 May 2011 03:59:34 PDT

Evaluating Academic Communities’ Collaboration Networks and Performance

Alireza Abbasi — Fri, 20 May 2011 03:57:57 PDT

Evolutionary Dynamics of Scientific Collaboration Networks: Multi-Levels and Cross-time Analysis

Alireza Abbasi — Fri, 20 May 2011 03:57:14 PDT

Several studies exist which use scientific literature for comparing scientific activities (e.g., productivity, and collaboration). In this study, using co-authorship data over the last 40 years, we present the evolutionary dynamics of multi level (i.e., individual, institutional and national) collaboration networks for exploring the emergence of collaborations in the research field of “steel structures”. The collaboration network of scientists in the field has been analyzed using author affiliations extracted from Scopus between 1970 and 2009. We have studied collaboration distribution networks at the micro-, meso- and macro-levels for the 40 years. We compared and analyzed a number of properties of these networks (i.e., density, centrality measures, the giant component and clustering coefficient) for presenting a longitudinal analysis and statistical validation of the evolutionary dynamics of “steel structures” collaboration networks. At all levels, the scientific collaborations network structures were central considering the closeness centralization while betweenness and degree centralization were much lower. In general networks density, connectedness, centralization and clustering coefficient were highest in marco-level and decreasing as the network size grow to the lowest in micro-level. We also find that the average distance between countries about two and institutes five and for authors eight meaning that only about eight steps are necessary to get from one randomly chosen author to another.

Betweenness Centrality as a Driver of Preferential Attachment in the Evolution of Research Collaboration Networks

Alireza Abbasi et al. — Fri, 20 May 2011 03:55:48 PDT

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.

Social Connectedness and Adaptive Team Coordination during Fire Events

Alireza Abbasi et al. — Fri, 20 May 2011 03:41:49 PDT

Preparing for fire-related emergencies and consequence management is considered to be dynamic and challenging in managing crises, preventing losses, and in the allocation of resources. In this study, we argue that improving plans and operations of personnel involved in managing fire-related emergencies is an important area of investigation. Here, we investigate the effects of social connectedness among different team members to manage bushfires. We further analyze response coordination by exploring variables such as participants’ preparedness quality, quality of incident action planning, and quality of accessibility of resources. In doing so, we also test the effects of these variables on improved adaptive behavior. Our results show high positive correlation between social connectedness for team members and coordination quality and also adaptive behavior. We also found significant relationship between coordination and adaptive behavior. Applying regression analyses, the results indicate positive significant effect of social connectedness on coordination and adaptive behavior and also the effect of coordination on adaptive behavior. By exploring the proposed model, we are able to develop a better understanding of the factors that support adaptive behavior in incident management teams responding to Bushfire events.

Research Impact and Scholars’ Geographical Diversity

Alireza Abbasi — Fri, 20 May 2011 03:39:11 PDT

In recent years there has been a sharp increase in collaborations among scholars and there are studies on the effects of scientific collaboration on scholars’ performance. This study examines the hypothesis that geographically diverse scientific collaboration is associated with research impact. Here, the approach is differentiated from other studies by: (a) focusing on publications rather than researchers or institutes; (b) considering the geographical diversity of authors of each publication; (c) considering the average number of citations a publication receives per year (time-based normalization of citations) as a surrogate for its impact; and (d) not focusing on a specific country (developed or developing) or region. Analysis of the collected bibliometric data shows that a publication impact is significantly and positively associated with all related geographical collaboration indicators. But publication impact has a stronger association with the numbers of external collaborations at department and institution levels (inter-departmental and inter-institutional collaborations) compared to internal collaborations. Conversely, national collaboration correlates better with impact than international collaboration.

Hybrid Centrality Measures for Binary and Weighted Networks

Alireza Abbasi — Tue, 10 May 2011 18:23:37 PDT

Existing centrality measures for social network analysis suggest the im-portance of an actor and give consideration to actor's given structural position in a network. These existing measures suggest specific attribute of an actor (i.e., popularity, accessibility, and brokerage behavior). In this study, we propose new hybrid centrality measures (i.e., Degree-Degree, Degree-Closeness and Degree-Betweenness), by combining existing measures (i.e., degree, closeness and betweenness) with a proposition to better understand the importance of actors in a given network. Generalized set of measures are also proposed for weighted networks. Our analysis of co-authorship networks dataset suggests significant correlation of our proposed new centrality measures (especially weighted networks) than traditional centrality measures with performance of the scholars. Thus, they are useful measures which can be used instead of traditional measures to show prominence of the actors in a network.

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

Alireza Abbasi et al. — Tue, 10 May 2011 18:18:33 PDT

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.

A social network system for analyzing publication activities of researchers

Alireza Abbasi — Tue, 10 May 2011 18:01:29 PDT

Social Networks Perspective of Firefighters' Adaptive Behaviour and Coordination among Them

Alireza Abbasi — Tue, 10 May 2011 17:48:04 PDT

On the Correlation between Research Performance and Social Network Analysis Measures Applied to Research Collaboration Networks

Alireza Abbasi et al. — Tue, 10 May 2011 17:43:02 PDT

E-Commerce Development in Iran

Alireza Abbasi — Tue, 10 May 2011 06:37:32 PDT