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HOOC - Higher Order Opportunities and Challenges
We are happy to announce the HOOC workshop on higher-order networks. Attendance is free! Go to https://conf.netsci.rwth-aachen.de for more information and registration.
Network analysis has revolutionized our understanding of complex systems, and graph-based methods have emerged as powerful tools to process signals on non-Euclidean domains via graph signal processing and graph neural networks. However, graphs are ill-equipped to encode multi-way and higher-order relations – features that are essential to understanding many systems such as group-dynamics in social systems, multi-gene interactions in genetic data, or multi-way drug interactions.
Accordingly, there is a need for new analytical methods to address the challenges of higher-order data, and a growing body of work in this direction. With this workshop, we especially want to explore current challenges which arise when bridging theory and data. This means on the one hand discussing what higher-order methods have already been used with real-world data, and on the other hand, what challenges currently prevent modelling systems with higher-order interactions.
The workshop will take place from the 11th to the 13th of August 2025. We look forward to welcoming you in Aachen, Germany!
Paper accepted at EUSIPCO 2025
Our paper "Faster Inference of Cell Complexes from Flows via Matrix Factorization" got accepted at EUSIPCO 2025.
In this paper, we consider the problem of inferring 2-cells from signals observed on the edges of a graph, s.t. the signals can be represented as a sparse combination of gradient and curl flows (see also our previous paper). We show matrix factorization to lead to an efficient heuristic for inferring said 2-cells.
Paper accepted at KDD 2025
Our paper "HLSAD: Hodge Laplacian-based Simplicial Anomaly Detection" has beeen accepted at KDD 2025, taking place in Toronto, Canada from August 2-7 this year.
HLSAD is a novel event and change-point detection algorithm for time-evolving simplicial complexes. It leverages the Hodge Laplacian to capture higher-order topological features and detect anomalies in the dynamic data by analyzing the evolution of the Hodge Laplacian spectrum. We show the effectiveness of out approach for both graph-lifting and inherently higher-order scenarios.
The preprint is available on arXiv and source code on GitLab.
Paper accepted at ICLR 2024
Our paper "Learning From Simplicial Data Based on Random Walks and 1D Convolutions" has been accepted at ICLR 2024.
In this paper, we propose a learning algorithm on topological domains based on random walks, which are processed by 1D convolutional neural networks. We show that this approach outperforms existing methods such as SCNN and MPSN on several datasets.
The paper is available on OpenReview and source code on GitLab.