3 citations · 10 across the 9 of their papers we have counts for
10 papers
Causal Cellular Context Transfer Learning (C3TL): An Efficient Architecture for Prediction of Unseen Perturbation Effects
Michael Scholkemper, Sach Mukherjee
Predicting the effects of chemical and genetic perturbations on quantitative cell states is a central challenge in computational biology, molecular medicine and drug discovery. Rec…
Extrapolation Guarantees for Perturbation Modeling Under the Additive Latent Shift Assumption
Julius von Kügelgen, Jakob Ketterer, Michael Vollenweider +4
We consider the problem of modeling the effects of perturbations like gene knockouts on measurements such as single-cell RNA counts. Given data for some perturbations, we aim to pr…
Residual Connections and Normalization Can Provably Prevent Oversmoothing in GNNs
Michael Scholkemper, Xinyi Wu, Ali Jadbabaie +1
Residual connections and normalization layers have become standard design choices for graph neural networks (GNNs), and were proposed as solutions to the mitigate the oversmoothing…
A Wasserstein Graph Distance Based on Distributions of Probabilistic Node Embeddings
Michael Scholkemper, Damin Kühn, Gerion Nabbefeld +3
Distance measures between graphs are important primitives for a variety of learning tasks. In this work, we describe an unsupervised, optimal transport based approach to define a d…
ICML 2023 Topological Deep Learning Challenge : Design and Results
Mathilde Papillon, Mustafa Hajij, Helen Jenne +53
This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning. The competi…
Learning the effective order of a hypergraph dynamical system
Leonie Neuhäuser, Michael Scholkemper, Francesco Tudisco +1
Dynamical systems on hypergraphs can display a rich set of behaviours not observable for systems with pairwise interactions. Given a distributed dynamical system with a putative hy…
An Optimization-based Approach To Node Role Discovery in Networks: Approximating Equitable Partitions
Michael Scholkemper, Michael T. Schaub
Similar to community detection, partitioning the nodes of a network according to their structural roles aims to identify fundamental building blocks of a network. The found partiti…
Neighborhood Structure Configuration Models
Felix I. Stamm, Michael Scholkemper, Markus Strohmaier +1
We develop a new method to efficiently sample synthetic networks that preserve the d-hop neighborhood structure of a given network for any given d. The proposed algorithm trades of…
Blind Extraction of Equitable Partitions from Graph Signals
Michael Scholkemper, Michael Schaub
Finding equitable partitions is closely related to the extraction of graph symmetries and of interest in a variety of applications context such as node role detection, cluster sync…
Local, global and scale-dependent node roles
Michael Scholkemper, Michael T. Schaub
This paper re-examines the concept of node equivalences like structural equivalence or automorphic equivalence, which have originally emerged in social network analysis to characte…