Distinguished publication by Information Science Faculty

Rolf Wigand, Distinguished Professor of Information Science and Management and Jerry L. Maulden-Entergy Chair, published with Shahadat Uddin and Liaquat Hossain, both with the Center for Complex Systems Research, The University of Sydney, Sydney, Australia, an article in the International Journal of Information Technology & Decision Making, entitled, ‚ÄúNew Directions in the Degree Centrality Measure: Towards a Time-variant Approach.‚ÄĚ

The International Journal of Information Technology & Decision Making (IJITDM) provides a global forum for exchanging research findings and case studies which bridge the latest information technology and various decision-making techniques. It promotes how information technology improves decision techniques as well as how the development of decision-making tools affects the information technology era. The journal is peer-reviewed and publishes both high-quality academic (theoretical or empirical) and practical papers in the broad ranges of information technology related topics.

The International Journal of Information Technology & Decision Making has an impact factor of 3.139 and the journal enjoys the following rankings:

  • 7th out of 108 in COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
  • 5th out of 126 in COMPUTER SCIENCE, INFORMATION SYSTEMS
  • 7th out of 97 in COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
  • 3rd out of 74 in OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Here is a summary of this article:

Degree centrality is considered to be one of the most basic measures of social network analysis, which has been used extensively in diverse research domains for measuring network positions of actors in respect of the connections with their immediate neighbors. In network analysis, it emphasizes the number of connections that an actor has with others. However, it does not accommodate the value of the duration of relations with other actors in a network; and therefore, this traditional degree centrality approach regards only the presence or absence of links. Here, we introduce a time-variant approach to the degree centrality measure – time scale degree centrality (TSDC), which considers both presence and duration of links among actors within a network. We illustrate the difference between the traditional and time scale degree centrality measure by applying these two approaches to explore the impact of degree attributes of a patient-physician network evolving during patient hospitalization periods on the hospital length of stay (LOS) both at a macro- and a micro-level. At a macro-level, both the traditional and time-scale approaches to degree centrality can explain the relationship between the degree attribute of the patient-physician network and LOS. However, at a micro-level or small cluster level, TSDC provides better explanation while the traditional degree centrality approach is found to be inadequate in explaining its relationship with LOS. This measure (i.e. TSDC) can explore time-variant relations that evolve among actors of a given social network.

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