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Classic measures of graph centrality capture distinct aspects of node importance, from the local (e.g., degree) to the global (e.g., closeness). Here we exploit the connection between diffusion and geometry to introduce a multiscale centrality measure. A n ...
Protein-protein interaction (PPI) network alignment is a canonical operation to transfer biological knowledge among species. The alignment of PPI-networks has many applications, such as the prediction of protein function, detection of conserved network mot ...
This article studies the problem of reconstructing the topology of a network of interacting agents via observations of the state-evolution of the agents. We focus on the large-scale network setting with the additional constraint of partial observations, wh ...
Braided rivers form some of the most fascinating fluvial patterns found on Earth. They are identifiable by their unique morphology of complex networks of intertwined channels that spread across wide floodplains. Detailed knowledge of their dynamics is need ...
A hallmark of graph neural networks is their ability to distinguish the isomorphism class of their inputs. This study derives hardness results for the classification variant of graph isomorphism in the message-passing model (MPNN). MPNN encompasses the maj ...
Seminal works on graph neural networks have primarily targeted semi-supervised node classification problems with few observed labels and high-dimensional signals. With the development of graph networks, this setup has become a de facto benchmark for a sign ...
In time-sensitive networks, regulators can be used to reshape traffic, and their usage may be necessary to guarantee stability by providing worst-case delay bounds. In this project, I study partial regulation of time-sensitive networks with cyclic dependen ...
This article introduces a new class of models for multiple networks. The core idea is to parameterize a distribution on labeled graphs in terms of a Frechet mean graph (which depends on a user-specified choice of metric or graph distance) and a parameter t ...
We consider that a network is an observation, and a collection of observed networks forms a sample. In this setting, we provide methods to test whether all observations in a network sample are drawn from a specified model. We achieve this by deriving the j ...
Studying real-world networks such as social networks or web networks is a challenge. These networks often combine a complex, highly connected structure together with a large size. We propose a new approach for large scale networks that is able to automatic ...
