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arXiv research

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.

169,341 papers · 148 categories

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89178266355 · Jun 202019922001200920182026
48 results for random geometric digraphs

Develops new classifiers using proximity catch digraphs for better class imbalance handling.

problem Class imbalance in classification problems.
method Constructs semi-parametric classifiers using random geometric digraphs called proximity catch digraphs (PCDs).
result PE-PCDs find exact minimum dominating sets in polynomial time, leading to efficient classifiers.

A new graph-based clustering method for moderate-dimensional data.

problem Performance degradation of existing graph-based clustering methods in high dimensions.
method Introduces UN-CCDs using NND-based MC-SRT for covering radii determination.
result UN-CCDs provide stable and competitive performance in moderate-sized datasets.

Parameter-free clustering method using cluster catch digraphs (CCDs).

problem Finding the correct number of clusters in data without specifying a parameter.
method Hybrid of density-based and graph-based clustering methods using Ripley's K function.
result Minimum dominating sets of RK-CCDs estimate and distinguish clusters from noise.

In this note we derive enumerative formulas for several types of labelled acyclic directed graphs by slight modifications of the familiar recursive formula for simple acyclic digraphs. These considerations are motivated by, and based upon, recent combinatorial results in geometric topology obtained by S.Choi, who estab…

2008-04-15abs ↗pdf ↗

ParPIC clusters directed graphs using random walks and diffusion operators.

problem Challenges in vertex-level clustering for directed graphs due to edge directionality.
method Parametrized Power-Iteration Clustering (ParPIC) based on reversible random walks and diffusion operators.
result ParPIC achieves competitive clustering accuracy with improved scalability compared to spectral and teleportation-based methods.

Study financial contagion and risk in sparse networks with directed edges.

problem Analyzing systemic risk in sparse financial networks with balance-sheet interactions.
method Linear fraction of institutions with zero out-degree, sender-truncated subgraph G_sh, adversarial and random systemic events, explicit fan-in accumulation bound.
result Maximal forward reachability in G_sh is O(log n) with high probability in the subcritical regime, and multi-hit defaults are negligible in the supercritical regime.

Directed acyclic graphs are the basic representation of the structure underlying Bayesian networks, which represent multivariate probability distributions. In many practical applications, such as the reverse engineering of gene regulatory networks, not only the estimation of model parameters but the reconstruction of t…

2012-02-29abs ↗pdf ↗

New spectral clustering for directed graphs reveals socio-economic patterns.

problem Spectral clustering for directed graphs is unsatisfactory due to edge directionality.
method Proposes a complex-valued matrix representation and analysis for directed graphs.
result Our approach reveals socio-economic patterns in internal migration data.

Two new outlyingness scores improve outlier detection in high-dimensional data.

problem Detecting outliers in high-dimensional data with varying cluster shapes and intensities.
method Outlyingness scores (OOS and IOS) based on Cluster Catch Digraphs (CCDs).
result Both OOS and IOS outperform CCD-based methods in identifying global and local outliers, especially IOS.

A graph (digraph) G=(V,E)G=(V,E) with a set TVT\subseteq V of terminals is called inner Eulerian if each nonterminal node vv has even degree (resp. the numbers of edges entering and leaving vv are equal). Cherkassky and Lovász showed that the maximum number of pairwise edge-disjoint TT-paths in an inner Eulerian graph $G…

2005-10-21abs ↗pdf ↗

Directed graphs can contain arbitrarily complex knots and links.

problem Proving the existence of directed graphs with arbitrarily complex knots and links.
method Proved the existence of a directed graph with an intrinsic nn-component link and an oriented link with specific properties.
result Directed graphs can contain arbitrarily complex knots and links, with specific properties of link components and their Conway polynomials.

In the present paper we find a bijection between the set of small covers over an nn-cube and the set of acyclic digraphs with nn labeled nodes. Using this, we give a formula of the number of small covers over an nn-cube (generally, a product of simplices) up to Davis-Januszkiewicz equivalence classes and $\mathbf{Z}…

2008-02-14abs ↗pdf ↗

New algorithms detect outliers in high-dimensional data with arbitrary shapes.

problem Challenges of high dimensionality and varying cluster shapes in traditional outlier detection methods.
method Cluster Catch Digraphs (CCDs) and their variants (U-MCCD, UN-MCCD, SU-MCCD, SUN-MCCD).
result U-MCCD efficiently identifies outliers with high true negative rates, and SU-MCCD improves handling of non-uniform clusters.

We prove an explicit formula of the Berezin star product on Kaehler manifolds. The formula is expressed as a summation over certain strongly connected digraphs. The proof relies on a combinatorial interpretation of Englis' work on the asymptotic expansion of the Laplace integral.

2011-03-21abs ↗pdf ↗

Directed graphs can be intrinsically knotted and 4-linked.

problem Intrinsic linking and knotting in directed graphs.
method Construction of examples and operations (consistent edge contraction, H-cyclic subcontraction).
result Directed graphs can have consistently oriented knotted cycles and intrinsically 3- and 4-linked structures.

Group lattices (Cayley digraphs) of a discrete group are in natural correspondence with differential calculi on the group. On such a differential calculus geometric structures can be introduced following general recipes of noncommutative differential geometry. Despite of the non-commutativity between functions and (gen…

2002-12-18abs ↗pdf ↗

GNNRank uses neural networks to learn global rankings from competition match data.

problem Learning global rankings from pairwise comparisons in directed graphs.
method Proposes GNNRank, a trainable GNN-based framework with digraph embedding and new objectives.
result GNNRank achieves competitive and superior performance compared to baselines.

New method clusters directed and undirected graphs without losing directional information.

problem Clustering directed graphs due to asymmetry in edge connectivity.
method Generalized Dirichlet Energy (GDE) and generalized spectral clustering (GSC).
result GSC outperforms existing methods in clustering accuracy and robustness.

Acyclic digraphs are the underlying representation of Bayesian networks, a widely used class of probabilistic graphical models. Learning the underlying graph from data is a way of gaining insights about the structural properties of a domain. Structure learning forms one of the inference challenges of statistical graphi…

2015-04-20abs ↗pdf ↗

It has been known since 1981 that if one fixes an orientable surface SS of genus gg, then there is a real number λmin,g>1λ_{min,g} > 1 that is the dilatation of a pA diffeomorphism of SS, and every other pA diffeomorphism of SS has dilatation λmin,g\geq λ_{min,g}. We will show how a little-known theorem about digraphs gives …

2011-04-14abs ↗pdf ↗

This paper sets thresholds for recovering vertex correspondences in partially correlated graphs.

problem Recovering hidden vertex correspondences in partially correlated graphs.
method Proposed partially correlated Erdős-Rényi graphs model; information-theoretic thresholds; correlated functional digraphs.
result Optimal rates for partial and exact recovery of vertex correspondences.

J. Przytycki has established a connection between the Hochschild homology of an algebra AA and the chromatic graph homology of a polygon graph with coefficients in AA. In general the chromatic graph homology is not defined in the case where the coefficient ring is a non-commutative algebra. In this paper we define a …

2010-01-29abs ↗pdf ↗

The paper proves properties for random graphs based on geometric submanifolds.

problem Establishing measure-metric properties of random geometric graphs.
method Analyzing ε\varepsilon-neighborhood graphs with specific conditions on submanifold and distribution.
result Volume doubling and local Poincaré inequalities hold for random geometric graphs with high probability.

Study on random surfaces in hyperbolic 3-manifolds, focusing on geometric and topological properties.

problem Distribution of nearly geodesic surfaces in hyperbolic 3-manifolds.
method Invariant measures on the Grassmann bundle G(M) derived from limits of random minimal surfaces.
result Topological limiting measures are totally scarring if M contains a totally geodesic subsurface, while geometrical limiting measures are not.

Uniform models for random 3-manifolds with controlled metrics.

problem Understanding the geometric properties of random 3-manifolds.
method Two constructions of hyperbolic metrics on 3-manifolds with Heegaard splittings.
result The diameter of a random Heegaard splitting grows coarsely linearly in the length of the associated random walk.

Random branched covers of groups are homotopy equivalent to geometrically small cancellation complexes.

problem Understanding the topological properties of random branched covers of groups.
method Constructing a random model for branched covers and showing asymptotic homotopy equivalence to geometrically small cancellation complexes.
result The fundamental group of a random branched cover is Gromov hyperbolic and has small cohomological dimension.

Let $φ\in \mbox{Out}(F_n)$ be a free group outer automorphism that can be represented by an expanding, irreducible train-track map. The automorphism φφ determines a free-by-cyclic group Γ=FnφZ,Γ=F_n \rtimes_φ\mathbb Z, and a homomorphism αH1(Γ;Z)α\in H^1(Γ; \mathbb Z). By work of Neumann, Bieri-Neumann-Strebel and Dowdall-Kapovi…

2013-10-28abs ↗pdf ↗

Randomized Geometric Algebra for Convex Neural Networks Optimizes Transfer Learning.

problem Training neural networks to global optimality via convex optimization.
method Randomized algorithms in Clifford's Geometric Algebra for hypercomplex vector spaces.
result Convex optimization and geometric algebra improve LLMs' robustness and reliability in transfer learning.

The ellipticity graph of a free group FF was defined by I. Kapovich and M. Lustig in order to study the outer automorphism group of FF, which acts on this graph. The graph was constructed to be analogous to the curve complex of a surface. It is a bipartite graph, whose vertices are conjugacy classes of nontrivial ele…

2010-06-24abs ↗pdf ↗

Study investigates ruin probability with random premiums and risky investments.

problem Ruin probability with random premiums and risky investments.
method Laplace transform applied to a model with geometric Brownian motion.
result Asymptotic behavior of ruin probability for large initial capital values.

In this paper we offer a novel type of network model which can capture the precise structure of a financial market based, for example, on empirical findings. With the attached stochastic framework it is further possible to study how an arbitrary network structure and its expected counterparty credit risk are analytical…

2015-04-26abs ↗pdf ↗

This paper proposes a new Monte Carlo sampler that balances geometric exploitation and computational cost.

problem Sampling from high-dimensional targets with multiple modes or strong correlations.
method Geometric adaptive Monte Carlo sampler in a random environment.
result The sampler achieves a high effective sample size for a given computational cost.

Bayesian methods estimate regression functions on submanifolds using graph Laplacian eigenbasis.

problem Estimating regression functions on unknown smooth submanifolds.
method Random geometric graph structure, Bayesian priors based on random basis expansion in graph Laplacian eigenbasis.
result Posterior contraction rates are minimax optimal for any positive smoothness index.

The study of record statistics of correlated series is gaining momentum. In this work, we study the records statistics of the time series of select stock market data and the geometric random walk, primarily through simulations. We show that the distribution of the age of records is a power law with the exponent αα lyi…

2014-06-24abs ↗pdf ↗

Generalizes entropy-drift inequality for specific geometric spaces.

problem Entropy, drift, and critical exponent in Gibbs measures on geometrically finite manifolds.
method Generalization of Guivarc'h's inequality for CAT(-1) spaces, analysis of random walks.
result Equality in entropy-drift inequality achieved if and only if Gibbs density is equivalent to hitting measure.