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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.

169,051 papers · 148 categories

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2.4%4.7%7.1%9.5% · May 201919922001200920182026
48 results for vertex labels

Proposes methods to recover labels from shuffled networks using graph averages.

problem Recovering labels from a shuffled network using graph averages.
method Cluster networks into classes, then match the new graph to cluster-averages, minimizing the graph matching objective function.
result Higher fidelity matching performance when clustering networks into different classes.

A variation of the preferential attachment random graph model of Barabási and Albert is defined that incorporates planted communities. The graph is built progressively, with new vertices attaching to the existing ones one-by-one. At every step, the incoming vertex is randomly assigned a label, which represents a commun…

2018-01-21abs ↗pdf ↗

The paper introduces a method for detecting principal communities and embedding vertices.

problem Detecting and embedding vertices in graphs with community structure.
method Principal graph encoder embedding method that detects principal communities and produces vertex embeddings.
result The method successfully detects principal communities and produces accurate vertex embeddings.

Graph neural networks often assume vertex labels are independent, but we show this is rarely true and propose a method to improve predictions.

problem Graph neural networks often assume vertex labels are conditionally independent given their neighborhood features, which is rarely true.
method We model the joint distribution of residuals on vertices with a parameterized multivariate Gaussian and estimate parameters by maximizing the marginal likelihood of the observed labels.
result Our method achieves substantially higher accuracy than competing baselines and can be interpreted as the strength of correlation among connected vertices.

Given a 2-crossing minimal chart ΓΓ, a minimal chart with two crossings, set α=min{ i  α=\min\{~i~|~there exists an edge of label ii containing a white vertex}\}, and β=max{ i  β=\max\{~i~|~there exists an edge of label ii containing a white vertex}\}. In this paper we study the structure of a neighbourhood of ΓαΓβΓ_α\cupΓ_β, and p…

2017-09-26abs ↗pdf ↗

For random graphs distributed according to stochastic blockmodels, a special case of latent position graphs, adjacency spectral embedding followed by appropriate vertex classification is asymptotically Bayes optimal; but this approach requires knowledge of and critically depends on the model dimension. In this paper, w…

2013-11-23abs ↗pdf ↗

A labeled oriented graph (LOG) is an oriented graph with a labeling function from the edge set into the vertex set. The complexity of a LOG is the minimal cardinality of an initial set SS of vertices such that every vertex can be reached successively from SS only using edges with labels in SS or already visited vert…

2014-12-23abs ↗pdf ↗

A scalable graph-based SSL method for large-scale data with few labels.

problem Challenges in semi-supervised learning with limited labeled data and large unlabeled data.
method Constructs a graph from a small set of high-dense vertexes to learn relationships and improve performance.
result Achieves good classification performance, especially with few labels.

While many multiple graph inference methodologies operate under the implicit assumption that an explicit vertex correspondence is known across the vertex sets of the graphs, in practice these correspondences may only be partially or errorfully known. Herein, we provide an information theoretic foundation for understand…

2016-05-08abs ↗pdf ↗

Power of network tests degrades when vertices are misaligned.

problem Power loss in network hypothesis testing due to vertex shuffling.
method Theoretical analysis and simulations of Frobenius norm differences in random dot product and stochastic block models.
result Shuffling vertices can significantly reduce the power of network tests.

A natural approach to analyze interaction data of form "what-connects-to-what-when" is to create a time-series (or rather a sequence) of graphs through temporal discretization (bandwidth selection) and spatial discretization (vertex contraction). Such discretization together with non-negative factorization techniques c…

2014-06-24abs ↗pdf ↗

Method analyzes large-scale network data to detect communication pattern shifts.

problem Analyzing large-scale time-series network data is challenging.
method Temporal encoder embedding method using ground-truth or estimated vertex labels.
result Detects communication pattern shifts across all levels of network structure.

Efficiently updates vertex representations for dynamic graphs using random walks.

problem Updating vertex representations for dynamic graphs without re-generating them on each update.
method Proposes algorithms that extend random walk-based methods to dynamic graphs, considering the extent and rate of changes.
result Achieves competitive results to state-of-the-art methods while being computationally efficient.

Suppose that a graph is realized from a stochastic block model where one of the blocks is of interest, but many or all of the vertices' block labels are unobserved. The task is to order the vertices with unobserved block labels into a ``nomination list'' such that, with high probability, vertices from the interesting b…

2013-12-10abs ↗pdf ↗

Biological and cellular systems are often modeled as graphs in which vertices represent objects of interest (genes, proteins, drugs) and edges represent relational ties among these objects (binds-to, interacts-with, regulates). This approach has been highly successful owing to the theory, methodology and software that …

2017-03-14abs ↗pdf ↗

There has been a recent interest in understanding the power of local algorithms for optimization and inference problems on sparse graphs. Gamarnik and Sudan (2014) showed that local algorithms are weaker than global algorithms for finding large independent sets in sparse random regular graphs. Montanari (2015) showed t…

2015-08-10abs ↗pdf ↗

Algorithm predicts graph label changes online with cluster specialists.

problem Online prediction of graph label changes with changing data.
method Specialist approach with cluster specialists, focusing on probabilistic cluster structure.
result Algorithm achieves O(logn)\mathcal{O}(\log n) time complexity, significantly faster than existing methods.

A federated method for feature selection in multi-label data.

problem Feature selection in multi-label data for distributed and federated environments.
method Semi-Supervised Federated Multi-Label Feature Selection (SSFMLFS) using fuzzy information measures.
result SSFMLFS outperforms other methods in feature selection for multi-label data in federated settings.

The paper finds and visualizes unique geometric polyhedra and tori with few vertices.

problem Finding and visualizing geometric polyhedra and tori with specific vertex configurations.
method Using Schlegel diagrams and geometric realization in 3D and 4D space.
result Identifies and visualizes 12 triangulations of the 2-torus and 12 triangulations of the 2D projective plane.

Given a vertex of interest in a network G1G_1, the vertex nomination problem seeks to find the corresponding vertex of interest (if it exists) in a second network G2G_2. A vertex nomination scheme produces a list of the vertices in G2G_2, ranked according to how likely they are judged to be the corresponding vertex of …

2017-11-15abs ↗pdf ↗

634 vertex-transitive and over 10^103 non-vertex-transitive 27-vertex triangulations of octonionic projective plane.

problem Constructing and classifying triangulations of the octonionic projective plane.
method Combinatorial construction and analysis of symmetry groups.
result Found 634 vertex-transitive and over 10^103 non-vertex-transitive 27-vertex triangulations.

Neural embeddings have been used with great success in Natural Language Processing (NLP). They provide compact representations that encapsulate word similarity and attain state-of-the-art performance in a range of linguistic tasks. The success of neural embeddings has prompted significant amounts of research into appli…

2017-05-29abs ↗pdf ↗

Study Type CC skein modules using Sp(2n)Sp(2n) webs and construct transparent elements.

problem Understanding Type CC skein modules and constructing transparent elements.
method Diagrammatic approach using multivariable Chebyshev polynomials and explicit braiding formulas.
result Construction of transparent elements in the skein module at roots of unity.