794 citations · 896 across the 13 of their papers we have counts for
14 papers
Coalgebraic analysis of social systems
Nima Motamed, Nina Otter, Emily Roff
The algebraic analysis of social systems, or algebraic social network analysis, refers to a collection of methods designed to extract information about the structure of a social sy…
Geometric characterisation of structural and regular equivalences in undirected (hyper)graphs
Marzieh Eidi, Nina Otter
Similarity notions between vertices in a graph, such as structural and regular equivalence, are one of the main ingredients in clustering tools in complex network science. We gener…
Pull-back Geometry of Persistent Homology Encodings
Shuang Liang, Renata Turkeš, Jiayi Li +2
Persistent homology (PH) is a method for generating topology-inspired representations of data. Empirical studies that investigate the properties of PH, such as its sensitivity to p…
On the effectiveness of persistent homology
Renata Turkeš, Guido Montúfar, Nina Otter
Persistent homology (PH) is one of the most popular methods in Topological Data Analysis. Even though PH has been used in many different types of applications, the reasons behind i…
Alpha magnitude
Miguel O'Malley, Sara Kalisnik, Nina Otter
Magnitude is an isometric invariant for metric spaces that was introduced by Leinster around 2010, and is currently the object of intense research, since it has been shown to encod…
Amplitudes in persistence theory
Barbara Giunti, John S. Nolan, Nina Otter +1
The use of persistent homology in applications is justified by the validity of certain stability results. At the core of such results is a notion of distance between the invariants…
Weisfeiler and Lehman Go Cellular: CW Networks
Cristian Bodnar, Fabrizio Frasca, Nina Otter +4
Graph Neural Networks (GNNs) are limited in their expressive power, struggle with long-range interactions and lack a principled way to model higher-order structures. These problems…
A topological perspective on weather regimes
Kristian Strommen, Matthew Chantry, Joshua Dorrington +1
It has long been suggested that the mid-latitude atmospheric circulation possesses what has come to be known as `weather regimes', loosely categorised as regions of phase space wit…
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks
Cristian Bodnar, Fabrizio Frasca, Yu Guang Wang +4
The pairwise interaction paradigm of graph machine learning has predominantly governed the modelling of relational systems. However, graphs alone cannot capture the multi-level int…
Can neural networks learn persistent homology features?
Guido Montúfar, Nina Otter, Yuguang Wang
Topological data analysis uses tools from topology -- the mathematical area that studies shapes -- to create representations of data. In particular, in persistent homology, one stu…
A unified framework for equivalences in social networks
Nina Otter, Mason A. Porter
A key concern in network analysis is the study of social positions and roles of actors in a network. The notion of "position" refers to an equivalence class of nodes that have simi…
Stratifying multiparameter persistent homology
Heather A. Harrington, Nina Otter, Hal Schenck +1
A fundamental tool in topological data analysis is persistent homology, which allows extraction of information from complex datasets in a robust way. Persistent homology assigns a…
Operads and Phylogenetic Trees
John C. Baez, Nina Otter
We construct an operad whose operations are the edge-labelled trees used in phylogenetics. This operad is the coproduct of , the operad for commutativ…
A roadmap for the computation of persistent homology
Nina Otter, Mason A. Porter, Ulrike Tillmann +2
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 perturbation…