Abstract Information networks are becoming increasingly popular to capture complex
relationships across various disciplines, such as social networks, citation networks, and …

Deep learning methods for graphs achieve remarkable performance on many node-level
and graph-level prediction tasks. However, despite the proliferation of the methods and their …

Non-genetic factors can cause individual cells to fluctuate substantially in gene expression
levels over time. It remains unclear whether these fluctuations can persist for much longer …

In topological data analysis and visualization, topological descriptors such as persistence
diagrams, merge trees, contour trees, Reeb graphs, and Morse–Smale complexes play an …

Abstract The Weisfeiler–Lehman graph kernel exhibits competitive performance in many
graph classification tasks. However, its subtree features are not able to capture connected …

While the study of graphs has been very popular, simplicial complexes are relatively new in
the network science community. Despite being a source of rich information, graphs are …

Motivated by the recent surge of criminal activities with cross-cryptocurrency trades, we
introduce a new topological perspective to structural anomaly detection in dynamic …

Mental workload (MWL) assessment is crucial in fatigue evaluation applications to avoid
potential health problems or serious accidents. This article proposes an MWL recognition …

Techniques from computational topology, in particular persistent homology, are becoming
increasingly relevant for data analysis. Their stable metrics permit the use of many distance …

Persistent homology, a technique from computational topology, has recently shown strong
empirical performance in the context of graph classification. Being able to capture long …