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.
Generative Link Sequence Modeling predicts future links in evolving networks.
The paper examines linking numbers in grid models and finds polynomial moments.
New model for links uses meander diagrams and combinatorics.
Develops a new causal model for path-dependent link prediction.
Aicardi's invariant is extended to colored singular links using graphical calculus.
A. S. Lipson constructed two state models yielding the same classical link invariant obtained from the Kauffman polynomial . In this paper, we apply Lipson's state models to marked graph diagrams of surface-links, and observe when they induce surface-link invariants.
Recurrent Neural Networks (RNNs) have been proven to be effective in modeling sequential data and they have been applied to boost a variety of tasks such as document classification, speech recognition and machine translation. Most of existing RNN models have been designed for sequences assumed to be identically and ind…
FakeEdge tackles dataset shift in link prediction tasks.
We study random knots and links in R^3 using the Petaluma model, which is based on the petal projections developed by Adams et al. (2012). In this model we obtain a formula for the distribution of the linking number of a random two-component link. We also obtain formulas for the expectations and the higher moments of t…
DEAL model predicts links for new nodes with only attribute info.
Proposes a method to enhance graph models by injecting unseen connections.
The paper confirms a conjecture linking link bipyramid volume and Mahler measure.
We construct a 2-variable link polynomial, called , for classical links by considering simultaneously the Kauffman state models for the Alexander and for the Jones polynomials. We conjecture that this polynomial is the product of two 1-variable polynomials, one of which is the Alexander polynomial. We refine …
Latent topic models have been successfully applied as an unsupervised topic discovery technique in large document collections. With the proliferation of hypertext document collection such as the Internet, there has also been great interest in extending these approaches to hypertext [6, 9]. These approaches typically mo…
GEN tackles few-shot out-of-graph link prediction in evolving multi-relational graphs.
Improves biomedical entity linking with latent type modeling.
Paper introduces symmetric divergence link models for probability distributions.
NPGNN improves graph link prediction by adapting to new graphs.
The paper studies pseudo links in genus g handlebodies, generalizing knot theory.
Bayesian MS-VAR model for pricing equity-linked life insurance products.
Topological model created for HOMFLY-PT polynomial from link diagrams.
New method optimizes policies without assuming known link functions between preferences and rewards.
We employ a solution of the Yang-Baxter equation to construct invariants for knot-like objects. Specifically, we consider a Yang-Baxter state model for the sl(n) polynomial of classical links and extend it to oriented singular links and balanced oriented 4-valent knotted graphs with rigid vertices. We also define a rep…
Random walks and polygons are used to model polymers. In this paper we consider the extension of writhe, self-linking number and linking number to open chains. We then study the average writhe, self-linking and linking number of random walks and polygons over the space of configurations as a function of their length. W…
This paper aims at the problem of link pattern prediction in collections of objects connected by multiple relation types, where each type may play a distinct role. While common link analysis models are limited to single-type link prediction, we attempt here to capture the correlations among different relation types and…
Proposes a novel tensor-based approach for multi-level link prediction.
Study Alexander polynomials of special alternating links and generalize Fox's conjecture.
Random hyperbolic 3-manifolds can be obtained via Dehn surgery.
New model values equity-linked securities with guaranteed return.
We are concerned with modeling the strength of links in networks by taking into account how often those links are used. Link usage is a strong indicator of how closely two nodes are related, but existing network models in Bayesian Statistics and Machine Learning are able to predict only wether a link exists at all. As …
Study tackles nonlinear factor models with unknown monotone links from incomplete and noisy data.
Link prediction is an important way to complete knowledge graphs (KGs), while embedding-based methods, effective for link prediction in KGs, perform poorly on relations that only have a few associative triples. In this work, we propose a Meta Relational Learning (MetaR) framework to do the common but challenging few-sh…
Using a simplistic model of juggling based on physics, a natural map is constructed from the set of periodic juggling patterns (or site swaps) to links. We then show that all topological links can be juggled.
Graph Convolutional Networks (GCNs) have proved to be a most powerful architecture in aggregating local neighborhood information for individual graph nodes. Low-rank proximities and node features are successfully leveraged in existing GCNs, however, attributes that graph links may carry are commonly ignored, as almost …
Graph Convolutional Gaussian Processes predict missing links.
Proposes a new model for high-dimensional data analysis with unknown link function.
This paper describes a method to obtain state model parameters for an infinite series of Links-Gould link invariants LG^{m,n}, based on quantum R matrices associated with the (\dot{0}_m | \dotα_n) representations of the quantum superalgebras U_q[gl(m|n)]. Explicit details of the state models for the cases n=1 and m=1,2…
State surfaces are spanning surfaces of links that are obtained from link diagrams guided by the combinatorics underlying Kauffman's construction of the Jones polynomial via state models. Geometric properties of such surfaces are often dictated by simple link diagrammatic criteria, and the surfaces themselves carry imp…
NePTuNe combines neural and tensor methods for efficient link prediction in knowledge graphs.
Proposes a new tensor factorization model for better link prediction in knowledge graphs.
GG-SAGE predicts links in directed graphs with attributes, outperforming existing methods.
Abstract summarizes level-rank duality in knot and link invariants.
Link prediction is a fundamental task in statistical network analysis. Recent advances have been made on learning flexible nonparametric Bayesian latent feature models for link prediction. In this paper, we present a max-margin learning method for such nonparametric latent feature relational models. Our approach attemp…
We study the spaces of string links and homotopy string links in an arbitrary manifold using multivariable manifold calculus of functors. We construct multi-cosimplicial models for both spaces and deduce certain convergence properties of the associated Bousfield-Kan homotopy and cohomology spectral sequences when the a…
Classifies -space surgeries on all two-bridge links
We extend the state models for Jones and Alexander polynomials of classical links to state models of 2-variable polynomials in the case of singular links. Moreover, we extend both of them to polynomials with d+1 variables for long singular knots with exactly d double points. These extensions can detect non-invertibilit…
Paper derives Thiele's equation for unit-linked policies in a stochastic volatility model.
The web link selection problem is to select a small subset of web links from a large web link pool, and to place the selected links on a web page that can only accommodate a limited number of links, e.g., advertisements, recommendations, or news feeds. Despite the long concerned click-through rate which reflects the at…