I have conducted research in the area of social informatics, data analysis and data mining, and network dynamics. My research addresses interdisciplinary issues relating to understand the impact of network structure and network dynamics on group performance and coordination outcomes in complex, dynamic and distributed environments, and to model network itself. For understanding the impact of network structure and network dynamics, I use concepts and methods from coordination theory, organizational theory, network theory, and social network analysis. To model network and understand network dynamics, I apply features of exponential random graph, scale-free network topology, power-law theory, complexity theory, statistical dyadic and triadic interaction methods and models, actor-level and network-level network modelling approach, and the concept of static and dynamic network topology. Phone: +61 2 9351 2118 (office) Address: Room 402
PNR Building
Redffern
NSW 2006 Australia
In this paper, we introduce a measure to analyse the structural robustness of complex networks, w... more In this paper, we introduce a measure to analyse the structural robustness of complex networks, which is specifically applicable in scenarios of targeted, sustained attacks. The measure is based on the changing size of the largest component as the network goes through disintegration. We argue that the measure can be used to quantify and compare the effectiveness of various attack strategies. Applying this measure, we confirm the result that scale-free networks are comparatively less vulnerable to random attacks and more vulnerable to targeted attacks. Then we analyse the robustness of a range of real world networks, and show that most real world networks are least robust to attacks based on betweenness of nodes. We also show that the robustness values of some networks are more sensitive to the attack strategy as compared to others. Furthermore, robustness coefficient computed using two centrality measures may be similar, even when the computational complexities of calculating these centrality measures may be different. Given this disparity, the robustness coefficient introduced potentially plays a key role in choosing attack and defence strategies for real world networks. While the measure is applicable to all types of complex networks, we clearly demonstrate its relevance to social network analysis.
Game theory has long been used to model cognitive decision making in societies. While traditional... more Game theory has long been used to model cognitive decision making in societies. While traditional game theoretic modelling has focussed on well-mixed populations, recent research has suggested that the topological structure of social networks play an important part in the dynamic behaviour of social systems. Any agent or person playing a game employs a strategy (pure or mixed) to optimise pay-off. Previous studies have analysed how selfish agents can optimise their payoffs by choosing particular strategies within a social network model. In this paper we ask the question that, if agents were to work towards the common goal of increasing the public good (that is, the total network utility), what strategies they should adapt within the context of a heterogeneous network. We consider a number of classical and recently demonstrated game theoretic strategies, including cooperation , defection, general cooperation, Pavlov, and zero-determinant strategies, and compare them pairwise. We use the Iterative Prisoners Dilemma game simulated on scale-free networks, and use a genetic-algorithmic approach to investigate what optimal placement patterns evolve in terms of strategy. In particular, we ask the question that, given a pair of strategies are present in a network, which strategy should be adopted by the hubs (highly connected people),
In networked systems research, game theory is increasingly used to model a number of scenarios wh... more In networked systems research, game theory is increasingly used to model a number of scenarios where distributed decision making takes place in a competitive environment. These scenarios include peer-to-peer network formation and routing, computer security level allocation, and TCP congestion control. It has been shown, however, that such modeling has met with limited success in capturing the real-world behavior of computing systems. One of the main reasons for this drawback is that, whereas classical game theory assumes perfect rationality of players, real world entities in such settings have limited information, and cognitive ability which hinders their decision making. Meanwhile , new bounded rationality models have been proposed in networked game theory which take into account the topology of the network. In this article, we demonstrate that game-theoretic modeling of computing systems would be much more accurate if a topologically distributed bounded rationality model is used. In particular, we consider (a) link formation on peer-to-peer overlay networks (b) assigning security levels to computers in computer networks (c) routing in peer-to-peer overlay networks, and show that in each of these scenarios, the accuracy of the modeling improves very significantly when topological models of bounded rationality are applied in the modeling process. Our results indicate that it is possible to use game theory to model competitive scenarios in networked systems in a way that closely reflects real world behavior, topology, and dynamics of such systems. V
In this paper, we introduce a measure to analyse the structural robustness of complex networks, w... more In this paper, we introduce a measure to analyse the structural robustness of complex networks, which is specifically applicable in scenarios of targeted, sustained attacks. The measure is based on the changing size of the largest component as the network goes through disintegration. We argue that the measure can be used to quantify and compare the effectiveness of various attack strategies. Applying this measure, we confirm the result that scale-free networks are comparatively less vulnerable to random attacks and more vulnerable to targeted attacks. Then we analyse the robustness of a range of real world networks, and show that most real world networks are least robust to attacks based on betweenness of nodes. We also show that the robustness values of some networks are more sensitive to the attack strategy as compared to others. Furthermore, robustness coefficient computed using two centrality measures may be similar, even when the computational complexities of calculating these centrality measures may be different. Given this disparity, the robustness coefficient introduced potentially plays a key role in choosing attack and defence strategies for real world networks. While the measure is applicable to all types of complex networks, we clearly demonstrate its relevance to social network analysis.
Assortativity is the tendency in networks where nodes connect with other nodes similar to themsel... more Assortativity is the tendency in networks where nodes connect with other nodes similar to themselves. Degree assortativity can be quantified as a Pear-son correlation. However, it is insufficient to explain assortative or disassortative tendencies of individual links, which may be contrary to the overall tendency in the network. In this paper we define and analyse link assortativity in the context of directed networks. Using synthesised and real world networks, we show that the overall assortativity of a network may be due to the number of assortative or disassortative links it has, the strength of such links, or a combination of both factors, which may be reinforcing or opposing each other. We also show that in some directed networks, link assortativity can be used to highlight subnetworks which have vastly different topological structures. The quantity we propose is limited to directed networks and complements the earlier proposed metric of node assortativity.
This study proposes a new set of measures for longitudinal social networks (LSNs). A LSN evolves ... more This study proposes a new set of measures for longitudinal social networks (LSNs). A LSN evolves over time through the creation and/or deletion of links among a set of actors (e.g., individuals or organizations). The current literature does feature some methods, such as multiagent simulation models, for studying the dynamics of LSNs. These methods have mainly been utilized to explore evolutionary changes in LSNs from one state to another and to explain the underlying mechanisms for these changes. However, they cannot quantify different aspects of a LSN. For example, these methods are unable to quantify the level of dynamicity shown by an actor in a LSN and its contribution to the overall dynamicity shown by that LSN. This article develops a set of measures for LSNs to overcome this limitation. We illustrate the benefits of these measures by applying them to an exploration of the Enron crisis. These measures successfully identify a significant but previously unobserved change in network structures (both at individual and group levels) during Enron's crisis period. V
Dyad and triad census summarize much of the network-level structural information of a given direc... more Dyad and triad census summarize much of the network-level structural information of a given directed network. They have been found very useful in analyzing structural properties of social networks. This study aims to explore crisis communication network by following dyad and triad census analysis approach to investigate the association of mi-cro-level communication patterns with organizational crisis. This study further tests hypothesis related to the process of data generation and tendency of the structural pattern of transitivity using dyad and triad census output. The changing communication network at Enron Corporation during the period of its crisis is analyzed in this study. Significant differences in the presence of different isomorphism classes or micro-level patterns of both dyad and triad census are noticed in crisis and non-crisis period network of Enron email corpus. It is also noticed that crisis communication network shows more transitivity compared to the non-crisis communication network.
The goal of this study was to understand research trends and collaboration patterns together with... more The goal of this study was to understand research trends and collaboration patterns together with scholarly impact within the domain of global obesity research. We developed and analysed bibliographic affiliation data collected from 117,340 research articles indexed in Scopus database on the topic of obesity and published from 1993–2012. We found steady growth and an exponential increase of publication numbers. Research output in global obesity research roughly doubled each 5 years, with almost 80% of the publications and authors from the second decade (2003–2012). The highest publication output was from the USA – 42% of publications had at least one author from the USA. Many US institutions also ranked highly in terms of research output and collaboration. Fifteen of the top-20 institutions in terms of publication output were from the USA; however, several European and Japanese research institutions ranked more highly in terms of average citations per paper. The majority of obesity research and collaboration has been confined to developed countries although developing countries have showed higher growth in recent times, e.g. the publication ratio between 2003–2012 and 1993–2002 for developing regions was much higher than that of developed regions (9:1 vs. 4:1). We also identified around 42 broad disciplines from authors' affiliation data, and these showed strong collaboration between them. Overall, this study provides one of the most comprehensive longitudinal bibliometric analyses of obesity research. This should help in understanding research trends, spatial density, collaboration patterns and the complex multidisciplinary nature of research in the obesity domain.
Understanding emerging areas of a multidisciplinary research field is crucial for researchers , p... more Understanding emerging areas of a multidisciplinary research field is crucial for researchers , policymakers and other stakeholders. For them a knowledge structure based on longitudinal bibliographic data can be an effective instrument. But with the vast amount of available online information it is often hard to understand the knowledge structure for data. In this paper, we present a novel approach for retrieving online bibliographic data and propose a framework for exploring knowledge structure. We also present several longitudinal analyses to interpret and visualize the last 20 years of published obesity research data.
A patient-centric care network can be defined as a network among a group of healthcare profession... more A patient-centric care network can be defined as a network among a group of healthcare professionals who provide treatments to common patients. Various multi-level attributes of the members of this network have substantial influence to its perceived level of performance. In order to assess the impact different multi-level attributes of patient-centric care networks on healthcare outcomes, this study first captured patient-centric care networks for 85 hospitals using health insurance claim dataset. From these networks, this study then constructed physician collaboration networks based on the concept of patient-sharing network among physicians. A multi-level regression model was then developed to explore the impact of different attributes that are organised at two levels on hospitalisation cost and hospital length of stay. For Level-1 model, the average visit per physician significantly predicted both hospitalisation cost and hospital length of stay. The number of different physicians significantly predicted only the hospitalisation cost, which has significantly been moderated by age, gender and Comorbidity score of patients. All Level-1 findings showed significance variance across physician collaboration networks having different community structure and density. These findings could be utilised as a reflective measure by healthcare decision makers. Moreover, healthcare managers could consider them in developing effective healthcare environments. A patient-centric approach to healthcare calls for increased collaboration among healthcare professionals who look after patients 1,2. This leads to the development of an informal social network among healthcare professionals who collaborate while looking after patients. This informal network is known as patient-centric care network in the healthcare literature 3. A patient-centric care network can therefore be thought as a group of healthcare professionals between whom collaborative connections or links emerge during the course of providing treatments to a common patient or a group of common patients. This study uses the multi-level regression approach to analyse and explore patient-centric care networks. A multi-level regression model concerns the data that is structured in more than one hierarchical level. A sample from this data can be described as multistage data. First, a sample of higher level units is drawn (e.g. hospitals or organisations), and next a sample of available sub-units (e.g. patients or healthcare professionals in hospitals) is considered. In such data, individual observations at the lowest level are in general dependent on the all other available hierarchical levels, often called as explanatory variables, in the data. Separate linear regression models for each level are used to model the impact of the residuals from different hierarchical levels on the outcome variable in a multi-level regression model 4. In this respect, multi-level regression models can be viewed as hierarchical systems of linear regression equations. A multi-level regression model is therefore considered as generalisations of linear regression models and is particularly appropriate for research designs where data for participants (e.g. patients) are organised at more than one level 4. There are numerous studies in the current literature exploring collaborations among healthcare professionals in a patient-centric care network. Mostly they examined hospital performance and patient outcomes
Previous studies have documented the application of electronic health insurance claim data for he... more Previous studies have documented the application of electronic health insurance claim data for health services research purposes. In addition to administrative and billing details of healthcare services, insurance data reveal important information regarding professional interactions and/or links that emerge among healthcare service providers through, for example, informal knowledge sharing. By using details of such professional interactions and social network analysis methods, the aim of the present study was to develop a research framework to explore health care coordination and collaboration. The proposed framework was used to analyse a patient-centric care coordination network and a physician collaboration network. The usefulness of this framework and its applications in exploring collaborative efforts of different healthcare professionals and service providers is discussed. What is known about the topic? Application of methods and measures of social network analytics in exploring different health care collaboration and coordination networks is a comparatively new research direction. It is apparent that no other study in the present healthcare literature proposes a generic framework for examining health care collaboration and coordination using an administrative claim dataset. What does this paper add? Using methods and measures of social network analytics, this paper proposes a generic framework for analysing various health care collaboration and coordination networks extracted from an administrative claim dataset. What are the implications for the practitioners? Healthcare managers or administrators can use the framework proposed in the present study to evaluate organisational functioning in terms of effective collaboration and coordination of care in their respective healthcare organisations. Additional keywords: exponential random graph models, health insurance claim data, patient-centric care coordination network, physician collaboration network, social network measures.
Crisis informetrics is considered to be a relatively new and emerging area of research, which dea... more Crisis informetrics is considered to be a relatively new and emerging area of research, which deals with the application of analytical approaches of network and information science combined with experimental learning approaches of statistical mechanics to explore communication and information flow, robustness as well as tolerance of complex crisis networks under threats. In this paper, we discuss the scale free network property of an organizational communication network and test both traditional (static) and dynamic topology of social networks during organizational crises Both types of topologies exhibit similar characteristics of prominent actors reinforcing the power law distribution nature of scale free networks. There are no significant fluctuations among the actor prominence in daily and aggregated networks. We found that email communication network display a high degree of scale free behavior described by power law.
Studies in health technology and informatics, 2015
There is a substantial variation in healthcare spending and readmission rate for individuals havi... more There is a substantial variation in healthcare spending and readmission rate for individuals having admissions to different hospitals. This study assessed how the community structure of physician collaboration networks that evolve during the period of providing healthcare services to hospitalised patients contribute to this variation. A physician collaboration network is said to have a community structure if the nodes (i.e. physicians) of that network can be easily grouped into sets of nodes such that each set of nodes is densely connected internally but sparsely connected between groups. This study constructed physician collaboration networks based on patient-sharing ties among physicians who provided healthcare services to hospitalised patients. An administrative health insurance claim dataset was utilised to extract patient-sharing ties among physicians. Simple linear regression models were estimated to assess the impact of the community structure of physician collaboration netwo...
In this paper, we introduce a measure to analyse the structural robustness of complex networks, w... more In this paper, we introduce a measure to analyse the structural robustness of complex networks, which is specifically applicable in scenarios of targeted, sustained attacks. The measure is based on the changing size of the largest component as the network goes through disintegration. We argue that the measure can be used to quantify and compare the effectiveness of various attack strategies. Applying this measure, we confirm the result that scale-free networks are comparatively less vulnerable to random attacks and more vulnerable to targeted attacks. Then we analyse the robustness of a range of real world networks, and show that most real world networks are least robust to attacks based on betweenness of nodes. We also show that the robustness values of some networks are more sensitive to the attack strategy as compared to others. Furthermore, robustness coefficient computed using two centrality measures may be similar, even when the computational complexities of calculating these centrality measures may be different. Given this disparity, the robustness coefficient introduced potentially plays a key role in choosing attack and defence strategies for real world networks. While the measure is applicable to all types of complex networks, we clearly demonstrate its relevance to social network analysis.
Game theory has long been used to model cognitive decision making in societies. While traditional... more Game theory has long been used to model cognitive decision making in societies. While traditional game theoretic modelling has focussed on well-mixed populations, recent research has suggested that the topological structure of social networks play an important part in the dynamic behaviour of social systems. Any agent or person playing a game employs a strategy (pure or mixed) to optimise pay-off. Previous studies have analysed how selfish agents can optimise their payoffs by choosing particular strategies within a social network model. In this paper we ask the question that, if agents were to work towards the common goal of increasing the public good (that is, the total network utility), what strategies they should adapt within the context of a heterogeneous network. We consider a number of classical and recently demonstrated game theoretic strategies, including cooperation , defection, general cooperation, Pavlov, and zero-determinant strategies, and compare them pairwise. We use the Iterative Prisoners Dilemma game simulated on scale-free networks, and use a genetic-algorithmic approach to investigate what optimal placement patterns evolve in terms of strategy. In particular, we ask the question that, given a pair of strategies are present in a network, which strategy should be adopted by the hubs (highly connected people),
In networked systems research, game theory is increasingly used to model a number of scenarios wh... more In networked systems research, game theory is increasingly used to model a number of scenarios where distributed decision making takes place in a competitive environment. These scenarios include peer-to-peer network formation and routing, computer security level allocation, and TCP congestion control. It has been shown, however, that such modeling has met with limited success in capturing the real-world behavior of computing systems. One of the main reasons for this drawback is that, whereas classical game theory assumes perfect rationality of players, real world entities in such settings have limited information, and cognitive ability which hinders their decision making. Meanwhile , new bounded rationality models have been proposed in networked game theory which take into account the topology of the network. In this article, we demonstrate that game-theoretic modeling of computing systems would be much more accurate if a topologically distributed bounded rationality model is used. In particular, we consider (a) link formation on peer-to-peer overlay networks (b) assigning security levels to computers in computer networks (c) routing in peer-to-peer overlay networks, and show that in each of these scenarios, the accuracy of the modeling improves very significantly when topological models of bounded rationality are applied in the modeling process. Our results indicate that it is possible to use game theory to model competitive scenarios in networked systems in a way that closely reflects real world behavior, topology, and dynamics of such systems. V
In this paper, we introduce a measure to analyse the structural robustness of complex networks, w... more In this paper, we introduce a measure to analyse the structural robustness of complex networks, which is specifically applicable in scenarios of targeted, sustained attacks. The measure is based on the changing size of the largest component as the network goes through disintegration. We argue that the measure can be used to quantify and compare the effectiveness of various attack strategies. Applying this measure, we confirm the result that scale-free networks are comparatively less vulnerable to random attacks and more vulnerable to targeted attacks. Then we analyse the robustness of a range of real world networks, and show that most real world networks are least robust to attacks based on betweenness of nodes. We also show that the robustness values of some networks are more sensitive to the attack strategy as compared to others. Furthermore, robustness coefficient computed using two centrality measures may be similar, even when the computational complexities of calculating these centrality measures may be different. Given this disparity, the robustness coefficient introduced potentially plays a key role in choosing attack and defence strategies for real world networks. While the measure is applicable to all types of complex networks, we clearly demonstrate its relevance to social network analysis.
Assortativity is the tendency in networks where nodes connect with other nodes similar to themsel... more Assortativity is the tendency in networks where nodes connect with other nodes similar to themselves. Degree assortativity can be quantified as a Pear-son correlation. However, it is insufficient to explain assortative or disassortative tendencies of individual links, which may be contrary to the overall tendency in the network. In this paper we define and analyse link assortativity in the context of directed networks. Using synthesised and real world networks, we show that the overall assortativity of a network may be due to the number of assortative or disassortative links it has, the strength of such links, or a combination of both factors, which may be reinforcing or opposing each other. We also show that in some directed networks, link assortativity can be used to highlight subnetworks which have vastly different topological structures. The quantity we propose is limited to directed networks and complements the earlier proposed metric of node assortativity.
This study proposes a new set of measures for longitudinal social networks (LSNs). A LSN evolves ... more This study proposes a new set of measures for longitudinal social networks (LSNs). A LSN evolves over time through the creation and/or deletion of links among a set of actors (e.g., individuals or organizations). The current literature does feature some methods, such as multiagent simulation models, for studying the dynamics of LSNs. These methods have mainly been utilized to explore evolutionary changes in LSNs from one state to another and to explain the underlying mechanisms for these changes. However, they cannot quantify different aspects of a LSN. For example, these methods are unable to quantify the level of dynamicity shown by an actor in a LSN and its contribution to the overall dynamicity shown by that LSN. This article develops a set of measures for LSNs to overcome this limitation. We illustrate the benefits of these measures by applying them to an exploration of the Enron crisis. These measures successfully identify a significant but previously unobserved change in network structures (both at individual and group levels) during Enron's crisis period. V
Dyad and triad census summarize much of the network-level structural information of a given direc... more Dyad and triad census summarize much of the network-level structural information of a given directed network. They have been found very useful in analyzing structural properties of social networks. This study aims to explore crisis communication network by following dyad and triad census analysis approach to investigate the association of mi-cro-level communication patterns with organizational crisis. This study further tests hypothesis related to the process of data generation and tendency of the structural pattern of transitivity using dyad and triad census output. The changing communication network at Enron Corporation during the period of its crisis is analyzed in this study. Significant differences in the presence of different isomorphism classes or micro-level patterns of both dyad and triad census are noticed in crisis and non-crisis period network of Enron email corpus. It is also noticed that crisis communication network shows more transitivity compared to the non-crisis communication network.
The goal of this study was to understand research trends and collaboration patterns together with... more The goal of this study was to understand research trends and collaboration patterns together with scholarly impact within the domain of global obesity research. We developed and analysed bibliographic affiliation data collected from 117,340 research articles indexed in Scopus database on the topic of obesity and published from 1993–2012. We found steady growth and an exponential increase of publication numbers. Research output in global obesity research roughly doubled each 5 years, with almost 80% of the publications and authors from the second decade (2003–2012). The highest publication output was from the USA – 42% of publications had at least one author from the USA. Many US institutions also ranked highly in terms of research output and collaboration. Fifteen of the top-20 institutions in terms of publication output were from the USA; however, several European and Japanese research institutions ranked more highly in terms of average citations per paper. The majority of obesity research and collaboration has been confined to developed countries although developing countries have showed higher growth in recent times, e.g. the publication ratio between 2003–2012 and 1993–2002 for developing regions was much higher than that of developed regions (9:1 vs. 4:1). We also identified around 42 broad disciplines from authors' affiliation data, and these showed strong collaboration between them. Overall, this study provides one of the most comprehensive longitudinal bibliometric analyses of obesity research. This should help in understanding research trends, spatial density, collaboration patterns and the complex multidisciplinary nature of research in the obesity domain.
Understanding emerging areas of a multidisciplinary research field is crucial for researchers , p... more Understanding emerging areas of a multidisciplinary research field is crucial for researchers , policymakers and other stakeholders. For them a knowledge structure based on longitudinal bibliographic data can be an effective instrument. But with the vast amount of available online information it is often hard to understand the knowledge structure for data. In this paper, we present a novel approach for retrieving online bibliographic data and propose a framework for exploring knowledge structure. We also present several longitudinal analyses to interpret and visualize the last 20 years of published obesity research data.
A patient-centric care network can be defined as a network among a group of healthcare profession... more A patient-centric care network can be defined as a network among a group of healthcare professionals who provide treatments to common patients. Various multi-level attributes of the members of this network have substantial influence to its perceived level of performance. In order to assess the impact different multi-level attributes of patient-centric care networks on healthcare outcomes, this study first captured patient-centric care networks for 85 hospitals using health insurance claim dataset. From these networks, this study then constructed physician collaboration networks based on the concept of patient-sharing network among physicians. A multi-level regression model was then developed to explore the impact of different attributes that are organised at two levels on hospitalisation cost and hospital length of stay. For Level-1 model, the average visit per physician significantly predicted both hospitalisation cost and hospital length of stay. The number of different physicians significantly predicted only the hospitalisation cost, which has significantly been moderated by age, gender and Comorbidity score of patients. All Level-1 findings showed significance variance across physician collaboration networks having different community structure and density. These findings could be utilised as a reflective measure by healthcare decision makers. Moreover, healthcare managers could consider them in developing effective healthcare environments. A patient-centric approach to healthcare calls for increased collaboration among healthcare professionals who look after patients 1,2. This leads to the development of an informal social network among healthcare professionals who collaborate while looking after patients. This informal network is known as patient-centric care network in the healthcare literature 3. A patient-centric care network can therefore be thought as a group of healthcare professionals between whom collaborative connections or links emerge during the course of providing treatments to a common patient or a group of common patients. This study uses the multi-level regression approach to analyse and explore patient-centric care networks. A multi-level regression model concerns the data that is structured in more than one hierarchical level. A sample from this data can be described as multistage data. First, a sample of higher level units is drawn (e.g. hospitals or organisations), and next a sample of available sub-units (e.g. patients or healthcare professionals in hospitals) is considered. In such data, individual observations at the lowest level are in general dependent on the all other available hierarchical levels, often called as explanatory variables, in the data. Separate linear regression models for each level are used to model the impact of the residuals from different hierarchical levels on the outcome variable in a multi-level regression model 4. In this respect, multi-level regression models can be viewed as hierarchical systems of linear regression equations. A multi-level regression model is therefore considered as generalisations of linear regression models and is particularly appropriate for research designs where data for participants (e.g. patients) are organised at more than one level 4. There are numerous studies in the current literature exploring collaborations among healthcare professionals in a patient-centric care network. Mostly they examined hospital performance and patient outcomes
Previous studies have documented the application of electronic health insurance claim data for he... more Previous studies have documented the application of electronic health insurance claim data for health services research purposes. In addition to administrative and billing details of healthcare services, insurance data reveal important information regarding professional interactions and/or links that emerge among healthcare service providers through, for example, informal knowledge sharing. By using details of such professional interactions and social network analysis methods, the aim of the present study was to develop a research framework to explore health care coordination and collaboration. The proposed framework was used to analyse a patient-centric care coordination network and a physician collaboration network. The usefulness of this framework and its applications in exploring collaborative efforts of different healthcare professionals and service providers is discussed. What is known about the topic? Application of methods and measures of social network analytics in exploring different health care collaboration and coordination networks is a comparatively new research direction. It is apparent that no other study in the present healthcare literature proposes a generic framework for examining health care collaboration and coordination using an administrative claim dataset. What does this paper add? Using methods and measures of social network analytics, this paper proposes a generic framework for analysing various health care collaboration and coordination networks extracted from an administrative claim dataset. What are the implications for the practitioners? Healthcare managers or administrators can use the framework proposed in the present study to evaluate organisational functioning in terms of effective collaboration and coordination of care in their respective healthcare organisations. Additional keywords: exponential random graph models, health insurance claim data, patient-centric care coordination network, physician collaboration network, social network measures.
Crisis informetrics is considered to be a relatively new and emerging area of research, which dea... more Crisis informetrics is considered to be a relatively new and emerging area of research, which deals with the application of analytical approaches of network and information science combined with experimental learning approaches of statistical mechanics to explore communication and information flow, robustness as well as tolerance of complex crisis networks under threats. In this paper, we discuss the scale free network property of an organizational communication network and test both traditional (static) and dynamic topology of social networks during organizational crises Both types of topologies exhibit similar characteristics of prominent actors reinforcing the power law distribution nature of scale free networks. There are no significant fluctuations among the actor prominence in daily and aggregated networks. We found that email communication network display a high degree of scale free behavior described by power law.
Studies in health technology and informatics, 2015
There is a substantial variation in healthcare spending and readmission rate for individuals havi... more There is a substantial variation in healthcare spending and readmission rate for individuals having admissions to different hospitals. This study assessed how the community structure of physician collaboration networks that evolve during the period of providing healthcare services to hospitalised patients contribute to this variation. A physician collaboration network is said to have a community structure if the nodes (i.e. physicians) of that network can be easily grouped into sets of nodes such that each set of nodes is densely connected internally but sparsely connected between groups. This study constructed physician collaboration networks based on patient-sharing ties among physicians who provided healthcare services to hospitalised patients. An administrative health insurance claim dataset was utilised to extract patient-sharing ties among physicians. Simple linear regression models were estimated to assess the impact of the community structure of physician collaboration netwo...
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Papers by Shahadat Uddin