Structural Properties of Ego Networks

The structure of real-world social networks in large part determines the evolution of social phenomena, including opinion formation, diffusion of information and influence, and the spread of disease. Globally, network structure is characterized by features such as degree distribution, degree assortativity, and clustering coefficient. However, information about global structure is usually not available to each vertex. Instead, each vertex's knowledge is generally limited to the locally observable portion of the network consisting of the subgraph over its immediate neighbors. Such subgraphs, known as ego networks, have properties that can differ substantially from those of the global network. In this paper, we study the structural properties of ego networks and show how they relate to the global properties of networks from which they are derived. Through empirical comparisons and mathematical derivations, we show that structural features, similar to static attributes, suffer from paradoxes. We quantify the differences between global information about network structure and local estimates. This knowledge allows us to better identify and correct the biases arising from incomplete local information.Comment: Accepted by SBP 2015, to appear in the proceeding

Exploring reasons and consequences of academic procrastination: an interview study

Grunschel C, Patrzek J, Fries S. Exploring reasons and consequences of academic procrastination: an interview study. European Journal of Psychology of Education. 2013;28(3):841-861.In the present study, we broadly investigated reasons and consequences of academic procrastination. Additionally, we explored whether students seeking help from student counselling services to overcome academic procrastination (counselling group) report more serious reasons and consequences of academic procrastination than students who procrastinate but seek no counselling support (non-counselling group). We conducted standardized interviews with university students (N = 36, of which 16 belonged to the counselling group) and analysed these using qualitative content analysis and frequency analysis. The reasons and consequences of academic procrastination, each summarized in a separate category system, were manifold. The category systems consisted of 20 main categories in total, subsuming 81 subcategories, of which 32 were inductively developed. The counselling group reported more serious reasons and consequences of academic procrastination than the non-counselling group. Our results suggest considering academic procrastination as a self-regulation failure and contribute to constructing interventions tailored to students' specific needs