Projects / Programmes
Blockmodeling multilevel and temporal networks
| Code |
Science |
Field |
Subfield |
| 5.03.00 |
Social sciences |
Sociology |
|
| Code |
Science |
Field |
| S210 |
Social sciences |
Sociology |
| Code |
Science |
Field |
| 5.04 |
Social Sciences |
Sociology |
blockmodeling, generalised blockmodeling, stochastic blockmodeling, network analysis, multilevel networks, temporal networks, optimisation
Researchers (14)
Organisations (2)
Abstract
BACKGROUND: Blockmodeling is a method for partitioning the units of a network and determining the ties among the (obtained) clusters and therefore enables finding the global structure of the network. It tries to find such a partition of the vertices/units in the network where an appropriate model (a small network we obtain by shrinking all clusters of the partitions) describes the initial network’s globla (overall) structure.
Recently, a lot of attention has been devoted to the analysis of multilevel and temporal networks. When analysing multilevel networks, we simultaneously study the ties among units from at least two levels (the ties both within and between levels). Often the first level represents individuals and the second organisations. We will use the term “linked networks” for both types of network, that is, multilevel and temporal networks (as networks measured at several time points).
Numerous deterministic and stochastic methods have been developed for blockmodeling. Nevertheless, most of them cannot be used for blockmodeling multilevel and temporal networks or so-called linked networks. There exist versions of stochastic blockmodeling for temporal networks and we have developed methods for generalised blockmodeling of multilevel networks, which can also be used for blockmodeling other linked networks. However, the first are not applicable to multilevel networks and do not support the use of pre-specified blockmodeling, while the others in their current development stage are only appropriate for relatively small networks (up to 100 units in each network in the case of only two networks), in part also due to the use of a non-tailored optimisation procedure. With our approach, there is also a need to improve the way different parts of the solution are weighted.
PROBLEM DEFINITION: Currently, there is no method for blockmodeling linked networks that would enable the blockmodeling of larger linked networks (with at least several hundred vertices/units) within a reasonable time. This is the problem we would like to solve and by using methods that would incorporate at least some elements of generalised blockmodeling. In addition, we will study which models, especially for so-called linking networks, namely, parts of the linked networks that represent ties among individual one-mode networks, are appropriate for different kinds of linked networks (multilevel, temporal, temporal multilevel).
RESEARCH OBJECTIVES: The project has two main objectives: (i) to improve the optimisation method for generalised blockmodeling for use in blockmodeling linked networks; and (ii) to adapt faster blockmodeling approaches (e.g. stochastic blockmodeling) for blockmodeling linked networks with selected elements of generalised blockmodeling.
An additional objective is to apply the developed methods to empirical networks in the field of collaboration in science (e.g. collaboration among researchers through time and collaboration among researchers and their institutions) and supply the appropriate findings.
Within the project, we will also research local mechanisms that lead towards a certain global (blockmodeling) network structure or change the global structure and enable the generating of random networks with a given blockmodeling structure.
EXECUTION: For most of its duration, the project will be split into two main branches, each one dealing with its own core objective (that is, either improving optimisation methods or adapting faster approaches to blockmodeling for linked networks). Each branch will be led by one institution (IMFM or FSS). The work plan is actually similar for both. The initial literature review will be followed by the design of new algorithms or methods, which will then go through testing phases (including applications), improvements and, if needed, also return to design phase developed into final methods in line with the objective of the individual branch. All developed methods will also be applied to network
Significance for science
The project will have a positive impact on scientific development in several areas. The project’s main results will be an improved optimisation method for blockmodeling of connected networks, and an approach for the stochastic (or similar) blockmodeling of such networks. These results alone represent significant scientific progress in the field of methods for network analysis. Moreover, bringing two previously separate approaches to blockmodeling closer, namely generalised blockmodeling and stochastic blockmodeling, will have a positive impact on the blockmodeling field.
The newly developed blockmodeling methods will allow researchers from other disciplines (sociology, economics, biology…) more adequate and accurate analyses of their data and therefore enable new insights into their research field. Based on the current uses of multilevel networks, they will be useful especially in fields where interactions between individuals and their organisations are studied (for instance, the collaboration between scientists and their organisations or companies and their agents).
The developed methods will be applied to the analysis of scientific collaboration, and we expect a positive impact on the development of this research area. We will thus be able, for instance, to determine whether research groups that closely cooperate in various disciplines remain stable or are instead the subject of greater fluctuations. From the multilevel perspective, we will find out whether blockmodels on different levels are similar, which is especially important in the light of various stimulations and restrictions on the areas.
A further impact is expected from the results of the analysis of the mechanisms that lead to a specific global block structure. Since it is often impossible to directly influence the global structure of such complex systems, the identified mechanisms could be used to guide policies and mechanisms to result in global structures that lead to the better functioning of systems.
Significance for the country
The application of the developed methods in the field of scientific collaboration will show the stability of global structures (blockmodels) of scientific collaboration, and the groups of researchers that form them. We will, for instance, be able to find out whether the structure centre-periphery-semiperiphery or similar is stable over time, which has previously been determined on lower levels; and whether the majority of researchers adhere to their role through time (e.g. the role of centre). The results will also reveal the coherence or incoherence of global network structures in science at different levels. These results are certainly important for decision-makers concerned with Slovenian policy in the field of science, especially the ministry and ARRS research agency. While the proposed research will identify the present status and dynamics in the global structure of scientific collaboration in Slovenia, the analysis of the mechanisms that the influence global structure will also give useful tools for changing the structure.
Most important scientific results
Interim report,
final report
Most important socioeconomically and culturally relevant results
Interim report,
final report
