Projects / Programmes
Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks
| Code |
Science |
Field |
Subfield |
| 5.03.00 |
Social sciences |
Sociology |
|
| Code |
Science |
Field |
| 5.04 |
Social Sciences |
Sociology |
network analysis, dynamic networks, blockmodeling, generalised blockmodeling, stochastic blockmodeling, random networks, algorithms for generating networks, network evolution models, co-authorship networks, scientific collaboration
Data for the last 5 years (citations for the last 10 years) on
March 30, 2023;
A3 for period 2017-2021
Data for ARRS tenders (
04.04.2019 – Programme tender,
archive
)
| Database |
Linked records |
Citations |
Pure citations |
Average pure citations |
| WoS |
185 |
2,899 |
2,613 |
14.12 |
| Scopus |
233 |
4,034 |
3,592 |
15.42 |
Researchers (12)
Organisations (2)
Abstract
BACKGROUND: Network analysis has become the main approach to analysing social interactions. A network is defined by the set of nodes (or vertices, units, actors) and by the set of links which represent ties between the nodes. Recently, much attention has been devoted to the analysis of dynamic networks. We use the term “dynamic networks” to describe a set of networks with the same set of nodes (incomers and outgoers are allowed), observed at/in different consecutive time points/periods. Blockmodeling is an approach for partitioning the nodes of a network (according to the structure of their links) and determining the ties among the clusters then obtained and thus may be used to describe the global structure of a studied network. Blockmodeling is widely used in the operationalization of social role and for data reduction. Since most approaches to blockmodeling dynamic networks were developed only in the last decade, researchers usually blockmodel networks from different time points separately. However, this approach is inadequate because it assumes that the networks from the different points in time are independent. The dependency between links from different points in time can be taken into account by the approaches to the blockmodeling of dynamic networks. Such analysis allows for more accurate results, smoother changes in time and hence improves the study of these changes. PROBLEM DEFINITION: Blockmodeling approaches for dynamic networks have not yet been systematically evaluated and compared using Monte Carlo simulations. Their evaluation is needed to help develop guidelines on the selection and use of blockmodeling approaches. To perform such simulations, one needs to generate random dynamic networks based on local mechanisms with a specified blockmodel type and partition for at each point in time. Currently, none of the existing approaches is able to do this. The approaches to blockmodeling dynamic networks should be very useful for studying scientific collaboration as operationalized by co-authorship (due to ‘noise’, lag in publications, etc.), and should make observing changes over time easier, namely, a very important issue in the social sciences. RESEARCH OBJECTIVES: The project has three main objectives: (i) To evaluate different blockmodeling approaches to dynamic networks that have different characteristics (e.g. density, nodes’ fluctuation, number of nodes at different time points, change of a blockmodel type in time). (ii) To develop algorithms for generating dynamic random networks based on local network mechanisms that allow the specification of the partition and the global structure for each time point. Using local mechanisms is necessary so that the networks then generated are closer to the real-world network dynamics, while known partition and global structure is needed for evaluation of the blockmodeling results. (iii) To apply one or several appropriate blockmodeling approaches to the dynamic co-authorship networks of Slovenian researchers. This should add to the validity of the results by way of more stable partitions and more easily observable changes. EXECUTION: The project will be organized in five work packages (WPs). These are: WP1 – Preparation phase; WP2 – Development of algorithms for generating dynamic random networks; WP3 – Evaluation of blockmodeling algorithms using Monte Carlo simulations; WP4 – Application of appropriate blockmodeling approaches(s) to the dynamic co-authorship networks of Slovenian researchers; WP5 – General project management and dissemination. WP3, WP4 and WP5 are very interdependent. For example, the results of WP5’s earlier stages are needed for WP3, while the main results of WP3 are needed in WP4. Similarly, the results of WP4 are needed by WP5. Therefore, for much of the project, all of the WPs will be running, but at different intensities. The project group consists of researchers who share many common research activities.
