Extreme analyses of graphs and text

Scientific talk: Extreme analyses of graphs and text

This talk presents my research on extreme analyses of graphs and text. For example, in text analysis we consider the task of extreme multi-label classification and show that our simple WideMLP outperforms state-of-the-art graph-based models like TextGCN. In graph analysis, we are analysing graphs with billions of edges. Our CIKM 2020 paper shows that graph summaries can be efficiently computed in a parallel and incremental algorithm. This is important for reflecting temporal changes in web graphs. Finally, we are considering my most recent research on stratified k-bisimulation on very large graphs with up to almost two billion edges.


Teaching talk: Logistic Regression: Classification

Linear and non-linear regression are well-known approaches for modeling data distributions. In the MSc module "Data Mining and Machine Learning", we are considering logistic regression to effectively solve binary classification tasks.