Kon Shing Kenneth Chung

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.

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.

Towards a social network model for understanding information and communication technology use for general practitioners in rural Australia

In this study, we develop a theoretical model based on social network theories and the social influence model to understand how knowledge professionals utilise technology for work and communication. We investigate the association between ego-centric network properties (structure, position and tie) and information and communication technology (ICT) use of individuals in knowledge-intensive and geographically dispersed settings. Analysis from data collected using a reliable and validated questionnaire show that task-level ICT use is significantly associated with degree centrality and functional tie diversity; and communication-level ICT use is negatively associated with efficiency. The implications of these associations for knowledge-intensive work mean that it is important to consider the professional social network characteristics of potential users of the technology for designing ICT-enabled organizations. The greater the number and diversity of peers individuals interact with translates into more opportunities to use ICT for context-specific tasks. Results from this study also show that individuals who tend to isolate themselves from peers tend to be slow adopters or low users of ICT. Thus, an understanding of how network structure inter-relates with technology and its adopters proves beneficial in reaping benefits required at the organizational (macro) and individual (micro) levels.

Measuring topological robustness of networks under sustained targeted attacks

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 of some networks are more sensitive to the attack strategy compared to others, and given the disparity in the computational complexities of calculating various centrality measures, the robustness coefficient introduced can play a key role in choosing the 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.

Exploring temporal communication through social networks

The dissemination of information in social networks and the relative effect of ICT (Information and Communications Technology) use has long been an interesting area of study in the field of sociology, human computer interaction and computer supported cooperative work. To date, a lot of research has been conducted regarding an actors mobile phone usage behavior while disseminating information within a mobile social network. In this study, we explore the structured network position of individuals using mobile phone and their ability to disseminate information within their social network. Our proposition is that an actors ability to disseminate information within a social group is affected by their structural network position. In this paper, we determine an actors structural network position by four different measures of centrality--(i) degree, (ii) closeness, (iii) betweenness, and (iv) eigenvector centrality. We analyse the Reality Mining dataset, which contains mobile phone usage data over a 9 month period for exploring the association between the structural positions of different actors in a temporal communication. We extract relational data to construct a social network of the mobile phone users in order to determine the association between their position in the network and their ability to disseminate information. The following questions form the basis for this study: Does information dissemination capability of an actor reflect their structural position within a social network? How do different measures of centrality associate with the information dissemination capability of an actor? Are highly central actors able to disseminate information more effectively than those who have a lower central position within a social network?

Topological analysis of longitudinal networks

Longitudinal networks evolve over time through the addition or deletion of nodes and edges. A longitudinal network can be viewed as a single static network that aggregates all edges observed over some time period (i.e., structure of network is fixed), or as a series of static networks observed in different point of time over the entire network observation period (i.e., structure of network is changing over time). By following a topological approach (i.e., static topology and dynamic topology), this paper first proposes a framework to analyze longitudinal networks. In static topology, SNA methods are applied to the aggregated network of entire observation period. Smaller segments of network data (i.e., short-interval network) that are accumulated in less time compared to the entire network observation period are used in dynamic topology for analysis purpose. Based on this framework, this study then conducts a topological analysis of email communication networks of an organization during its different operational conditions to explore changes in the behavior of actor-level dynamics.

Quantifying topological robustness of networks under sustained targeted attacks

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.

Network structure, position, ties and ICT use in distributed knowledge-intensive work

In this study, we develop a theoretical model based on social network theories and the social influence model to understand how knowledge professionals utilise technology for work and communication. We investigate the association between egocentric network properties (structure, position and tie) and information and communication technology (ICT) use of individuals in knowledge-intensive and geographically dispersed settings. Analysis from data collected using a reliable and validated questionnaire show that task-level ICT use is significantly associated with degree centrality and functional tie-diversity; and communication-level ICT use is negatively associated with efficiency. Implications of these associations for knowledge-intensive work are discussed in conclusion.

Community evolution and engagement through assortative mixing in online social networks

In this exploratory paper, we examine the evolution and engagement of an online community through a ten-year period. Data is collected from an online public discussion forum provided by a government-sponsored website specifically developed for community capacity building. We postulate that there are clear patterns of assortativity where similar actors engage in communication with each other over time. Results show that there is a clear pattern of networks losing their disassortative character in the early years followed by disassortative networks in the later years. The network-level results challenges government-level metrics of community-building success and suggests network analysis as an empirical avenue for understanding social processes involved in the very nature of community building.

Associations between personal social network properties and mental health in cancer care

In this study, we develop a theoretical model based on social network theories and the quality of life (QOL) model to understand how social support would influence Global Mental QOL in the context of cancer patients. While extant literature showing how structural, dyadic and network level perspectives influence QOL remain lacking, this study contributes towards addressing this gap. It also illustrates how social network data, which is primarily time consuming to obtain, can be extracted from current social surveys. Using the U.S. National Health Interview Survey 2010, we (i) demonstrate how relational data is extracted for (ii) investigating the association between egocentric network properties (structure, position and relations) and Global Mental QOL. Results show that there are significant differences in the network properties (density, degree, tie strength, efficiency and constraint) of those experiencing good and poor Global Mental QOL. These findings are critical to influencing interventions and policy development for enhanced Mental QOL in cancer care.