Nonlinear Spectral Graph Theory
SIAM Review, 2026
Nonlinear spectral graph theory is an extension of the traditional (linear) spectral graph theory and studies relationships between spectral properties of nonlinear operators defined on a graph and topological properties of the graph itself. Many of these relationships get tighter when going from the linear to the …
Efficient Sparsification of Simplicial Complexes via Local Densities of States
SIAM Journal on Mathematics of Data Science, 2026
Simplicial complexes (SCs), a generalization of graph models for relational data that account for higher-order relations between data items, have become a popular abstraction for analyzing complex data using tools from topological data analysis or topological signal processing. However, the analysis of many real-world …
A Low-Rank Perspective on Oversmoothing in Graph Neural Networks
AAAI Conference on Artificial Intelligence (AAAI), Workshop on Connecting Low-Rank Representations in AI, 2025
Oversmoothing is a fundamental challenge in graph neural networks (GNNs): as the number of layers increases, node embeddings become increasingly similar, and model performance drops sharply. Traditionally, oversmoothing has been quantified using metrics that measure the similarity of neighbouring node features, such as …
The graph $\infty$-Laplacian eigenvalue problem
SIAM Journal on Mathematical Analysis, 2026
We analyze various formulations of the $\infty$-Laplacian eigenvalue problem on graphs, comparing their properties and highlighting their respective advantages and limitations. First, we investigate the graph $\infty$-eigenpairs arising as limits of $p$-Laplacian eigenpairs, extending key results from the continuous …
Cholesky-like Preconditioner for Hodge Laplacians via Heavy Collapsible Subcomplex
SIAM Journal on Matrix Analysis and Applications, 2024
Techniques based on $k$-th order Hodge Laplacian operators $L_k$ are widely used to describe the topology as well as the governing dynamics of high-order systems modeled as simplicial complexes. In all of them, it is required to solve a number of least square problems with $L_k$ as coefficient matrix, for example in …
Quantifying the structural stability of simplicial homology
Journal of Scientific Computing, vol. 97, 2023
The homology groups of a simplicial complex reveal fundamental properties of the topology of the data or the system and the notion of topological stability naturally poses an important yet not fully investigated question. In the current work, we study the stability in terms of the smallest perturbation sufficient to …
Nonlinear Spectral Duality
Annali della Scuola Normale Superiore di Pisa, Classe di Scienze, 2026
Nonlinear eigenvalue problems for pairs of homogeneous convex functions are particular nonlinear constrained optimization problems that arise in a variety of settings, including graph mining, machine learning, and network science. By considering different notions of duality transforms from both classical and recent …
Nodal domain count for the generalized graph $p$-Laplacian
Applied and Computational Harmonic Analysis, vol. 64, pp. 1–32, 2023
Inspired by the linear Schrödinger operator, we consider a generalized $p$-Laplacian operator on discrete graphs and present new results that characterize several spectral properties of this operator with particular attention to the nodal domain count of its eigenfunctions. Just like the one-dimensional continuous …
The self-consistent field iteration for p-spectral clustering
The self-consistent field (SCF) iteration, combined with its variants, is one of the most widely used algorithms in quantum chemistry. We propose a procedure to adapt the SCF iteration for the p-Laplacian eigenproblem, which is an important problem in the field of unsupervised learning. We formulate the p-Laplacian …
Generalized matrix means for semisupervised learning with multilayer graphs
Advances in Neural Information Processing Systems (NeurIPS), 2019
We study the task of semi-supervised learning on multilayer graphs by taking into account both labeled and unlabeled observations together with the information encoded by each individual graph layer. We propose a regularizer based on the generalized matrix mean, which is a one-parameter family of matrix means that …
Spectral Clustering of Signed Graphs via Matrix Power Means
International Conference on Machine Learning (ICML), pp. 4526–4536, 2019
Signed graphs encode positive (attractive) and negative (repulsive) relations between nodes. We extend spectral clustering to signed graphs via the one-parameter family of Signed Power Mean Laplacians, defined as the matrix power mean of normalized standard and signless Laplacians of positive and negative edges. We …
The Power Mean Laplacian for Multilayer Graph Clustering
International Conference on Artificial Intelligence and Statistics (AISTATS), vol. 84, pp. 1828–1838, 2018
Multilayer graphs encode different kind of interactions between the same set of entities. When one wants to cluster such a multilayer graph, the natural question arises how one should merge the information form different layers. We introduce in this paper a one-parameter family of matrix power means for merging the …
A nodal domain theorem and a higher-order Cheeger inequality for the graph p-Laplacian
EMS Journal of Spectral Theory, vol. 8, pp. 883–908, 2018
We consider the nonlinear graph $p$-Laplacian and its set of eigenvalues and associated eigenfunctions of this operator defined by a variational principle. We prove a nodal domain theorem for the graph $p$-Laplacian for any $p\geq 1$. While for $p>1$ the bounds on the number of weak and strong nodal domains are the …
Clustering Signed Networks with the Geometric Mean of Laplacians
Advances in Neural Information Processing Systems (NeurIPS), vol. 29, 2016
Signed networks allow to model positive and negative relationships. We analyze existing extensions of spectral clustering to signed networks. It turns out that existing approaches do not recover the ground truth clustering in several situations where either the positive or the negative network structures contain no …