A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New theorem on graph curvature thresholds and uniqueness.
Proves Khovanov homology has no torsion for bipartite circle graphs.
In this article, we improve extreme learning machines for regression tasks using a graph signal processing based regularization. We assume that the target signal for prediction or regression is a graph signal. With this assumption, we use the regularization to enforce that the output of an extreme learning machine is s…
The paper finds minimum Steklov eigenvalues on combinatorial graphs.
Study finds Scherk type surfaces as extremals for zero-curvature minimal graphs.
We find families of prime knot diagrams with arbitrary extreme coefficients in their Jones polynomials. Some graph theory is presented in connection with this problem, generalizing ideas by Yongju Bae and Morton and giving a positive answer to a question in their paper.
Study supports conjecture about pretzel links' homology.
It was proven by González-Meneses, Manchón and Silvero that the extreme Khovanov homology of a link diagram is isomorphic to the reduced (co)homology of the independence simplicial complex obtained from a bipartite circle graph constructed from the diagram. In this paper we conjecture that this simplicial complex is al…
Critical graphs of quadratic differentials equidistribute in moduli space.
This paper uses MIS to identify key financial institutions with minimal risk contagion.
Proposes a network-based strategy to manage financial market risks.
In this letter, two explicit self-similar solutions to a graph representation of time-like extremal hypersurfaces in Minkowski spacetime are given. Meanwhile, there is an untable eigenvalue in the linearized time-like extremal hypersurfaces equation around two explicit self-similar solutions.
Discrete analogues of classical spectral geometric inequalities and extremal eigenvalue problems on graphs.
We prove that the hypothetical extreme Khovanov cohomology of a link is the cohomology of the independence simplicial complex of its Lando graph. We also provide a family of knots having as many non-trivial extreme Khovanov cohomology modules as desired, that is, examples of -thick knots which are as far of being $H…
New rigidity result for hyperbolic surfaces based on curve lengths.
In extreme classification problems, learning algorithms are required to map instances to labels from an extremely large label set. We build on a recent extreme classification framework with logarithmic time and space, and on a general approach for error correcting output coding (ECOC) with loss-based decoding, and intr…
New method learns graphical models with latent variables for extreme events.
We adapt Thistlethwaite's alternating tangle decomposition of a knot diagram to identify the potential extreme terms in its bracket polynomial, and give a simple combinatorial calculation for their coefficients, based on the intersection graph of certain chord diagrams.
Spectral clustering identifies clusters of multivariate extremes.
A simpler proof for apex graphs in McCarty and Thomas' conjecture.
This paper defines girth for knots and links, linking it to Khovanov homology.
In this paper, we study the possibility of inferring early warning indicators (EWIs) for periods of extreme bitcoin price volatility using features obtained from Bitcoin daily transaction graphs. We infer the low-dimensional representations of transaction graphs in the time period from 2012 to 2017 using Bitcoin blockc…
New method identifies extreme risk propagation in financial networks.
Balancing graph summarization and change detection in streaming data.
This note provides an alternate account of Calegari's rationality theorem for stable commutator length in free groups.
Circle graph complexes reveal link properties via Khovanov homology.
Uniform diameter bound for reflection group disk patterns.
We discuss the equivalence between the categories of certain ribbon graphs and subgroups of the modular group and use it to construct exponentially large families of not Hurwitz equivalent simple braid monodromy factorizations of the same element. As an application, we also obtain exponentially large families of {\…
Deep learning model predicts wildfire spread in Australia.
Graph generation is an extremely important task, as graphs are found throughout different areas of science and engineering. In this work, we focus on the modern equivalent of the Erdos-Renyi random graph model: the graph variational autoencoder (GVAE). This model assumes edges and nodes are independent in order to gene…
Spectral sparsification improves Laplacian-constrained graph learning.
Alt-GNNs improve travel mode choice modeling by integrating graph neural networks with GEV models.
Paper proposes efficient GCN learning method for limited data.
Research predicts XRP price anomalies using graph topologies.
AGML model improves indoor localization with sparse fingerprints using meta-learning and graph neural networks.
Extends Teichmüller space quasi-isometry to -multicurve graphs.
We present graph partition neural networks (GPNN), an extension of graph neural networks (GNNs) able to handle extremely large graphs. GPNNs alternate between locally propagating information between nodes in small subgraphs and globally propagating information between the subgraphs. To efficiently partition graphs, we …
DefenseVGAE defends graph neural networks against adversarial attacks.
Despite the exploding interest in graph neural networks there has been little effort to verify and improve their robustness. This is even more alarming given recent findings showing that they are extremely vulnerable to adversarial attacks on both the graph structure and the node attributes. We propose the first method…
Given two graphs, the graph matching problem is to align the two vertex sets so as to minimize the number of adjacency disagreements between the two graphs. The seeded graph matching problem is the graph matching problem when we are first given a partial alignment that we are tasked with completing. In this paper, we m…
In this paper, we present a graph-based semi-supervised framework for hyperspectral image classification. We first introduce a novel superpixel algorithm based on the spectral covariance matrix representation of pixels to provide a better representation of our data. We then construct a superpixel graph, based on carefu…
This article proposes a method to quantify the structure of a bipartite graph using a network entropy per link. The network entropy of a bipartite graph with random links is calculated both numerically and theoretically. As an application of the proposed method to analyze collective behavior, the affairs in which parti…
In a wide variety of situations, anomalies in the behaviour of a complex system, whose health is monitored through the observation of a random vector X = (X1,. .. , X d) valued in R d , correspond to the simultaneous occurrence of extreme values for certain subgroups {1,. .. , d} of variables Xj. Under th…
Improved EXACT strategy reduces GNN memory consumption and runtime.
The goal in extreme multi-label classification is to learn a classifier which can assign a small subset of relevant labels to an instance from an extremely large set of target labels. Datasets in extreme classification exhibit a long tail of labels which have small number of positive training instances. In this work, w…
XGES improves GES by favoring early edge deletion, outperforming GES in finite data settings.
Cryptocurrency markets exhibit violent, synchronised drawdowns, challenging diversification claims.
Graph learning from data represents a canonical problem that has received substantial attention in the literature. However, insufficient work has been done in incorporating prior structural knowledge onto the learning of underlying graphical models from data. Learning a graph with a specific structure is essential for …