Compression of Weighted Graphs

We propose to compress weighted graphs (networks), motivated by the observation that large networks of social, biological, or other relations can be complex to handle and visualize. In the process also known as graph simplication, nodes and (unweighted) edges are grouped to supernodes and superedges, respectively, to obtain a smaller graph. We propose models and algorithms for weighted graphs. The interpretation (i.e. decompression) of a compressed, weighted graph is that a pair of original nodes is connected by an edge if their supernodes are connected by one, and that the weight of an edge is approximated to be the weight of the superedge. The compression problem now consists of choosing supernodes, superedges, and superedge weights so that the approximation error is minimized while the amount of compression is maximized. In this paper, we formulate this task as the 'simple weighted graph compression problem'. We then propose a much wider class of tasks under the name of 'generalized weighted graph compression problem'. The generalized task extends the optimization to preserve longer-range connectivities between nodes, not just individual edge weights. We study the properties of these problems and propose a range of algorithms to solve them, with dierent balances between complexity and quality of the result. We evaluate the problems and algorithms experimentally on real networks. The results indicate that weighted graphs can be compressed efficiently with relatively little compression error.

Interest in university studies : Its role and relation to other motivational variables

This dissertation empirically explored interest as a motivational force in university studies, including the role it currently plays and possible ways of enhancing this role as a student motivator. The general research questions were as follows: 1) What role does interest play in university studies? 2) What explains academic success if studying is not based on interest? 3) How do different learning environments support or impede interest-based studying? Four empirical studies addressed these questions. Study 1 (n=536) compared first-year students explanations of their disciplinary choices in three fields: veterinary medicine, humanities and law. Study 2 (n=28) focused on the role of individual interest in the humanities and veterinary medicine, fields which are very different from each other as regards their nature of studying. Study 3 (n=52) explored veterinary students motivation and study practices in relation to their study success. Study 4 (n=16) explored veterinary students interest experience in individual lectures on a daily basis. By comparing different fields and focusing on one study field in more detail, it was possible to obtain a many-sided picture of the role of interest in different learning environments. Questionnaires and quantitative methods have often been used to measure interest in academic learning. The present work is based mostly on qualitative data, and qualitative methods were applied to add to the previous research. Study 1 explored students open-ended answers, and these provided a basis for the interviews in Study 2. Study 3 explored veterinary students portfolios in a longitudinal setting. For Study 4, a diary including both qualitative and quantitative measures was designed to capture veterinary students interest experience. Qualitative content analysis was applied in all four studies, but quantitative analyses were also added. The thesis showed that university students often explain their disciplinary choices in terms of interest. Because interest is related to high-quality learning, the students seemed to have a good foundation for successful studies. However, the learning environments did not always support interest-based studying; Time-management and coping skills were found to be more important than interest in terms of study success. The results also indicated that interest is not the only motivational variable behind university studies. For example, future goals are needed in order to complete a degree. Even so, the results clearly indicated that it would be worth supporting interest-based studying both in professionally and generally oriented study fields. This support is important not only to promote high-quality learning but also meaningful studying, student well-being, and life-long learning.