Network-based modeling of complex systems and data using the language of graphs has
become an essential topic across a range of different disciplines. Arguably, this graph-based …

The last decade saw an enormous boost in the field of computational topology: methods and
concepts from algebraic and differential topology, formerly confined to the realm of pure …

Persistent homology (PH) is a method used in topological data analysis (TDA) to study
qualitative features of data that persist across multiple scales. It is robust to perturbations of …

Inferring topological and geometrical information from data can offer an alternative
perspective in machine learning problems. Methods from topological data analysis, eg …

A suitable feature representation that can both preserve the data intrinsic information and
reduce data complexity and dimensionality is key to the performance of machine learning …

Persistence diagrams (PDs) play a key role in topological data analysis (TDA), in which they
are routinely used to describe succinctly complex topological properties of complicated …

Topological data analysis (TDA) is an emerging mathematical concept for characterizing
shapes in complex data. In TDA, persistence diagrams are widely recognized as a useful …

Regularization plays a crucial role in supervised learning. Most existing methods enforce a
global regularization in a structure agnostic manner. In this paper, we initiate a new direction …

Propelled by a fast evolving landscape of techniques and datasets, data science is growing
rapidly. Against this background, topological data analysis (TDA) has carved itself a niche …

Glioblastoma multiforme (GBM) is an aggressive form of human brain cancer that is under
active study in the field of cancer biology. Its rapid progression and the relative time cost of …