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.
Random projections help in representing sparse graphs efficiently.
Linear time algorithm for random walk kernels on sparse graphs.
Sparse RSP routing improves graph exploration and classification.
Detecting correlated trees helps align sparse graphs.
Uniform drift estimates found for random walks on graph products.
We propose a novel statistical model for sparse networks with overlapping community structure. The model is based on representing the graph as an exchangeable point process, and naturally generalizes existing probabilistic models with overlapping block-structure to the sparse regime. Our construction builds on vectors …
Estimating dimension from sparse random geometric graphs.
New algorithms find half-optimal independent sets in sparse graphs.
New framework for neural networks converging to low loss without overparameterization.
We consider the community detection problem in sparse random hypergraphs. Angelini et al. (2015) conjectured the existence of a sharp threshold on model parameters for community detection in sparse hypergraphs generated by a hypergraph stochastic block model. We solve the positive part of the conjecture for the case of…
Study detects edge correlation between unlabeled random graphs.
This paper presents VEC-NBT, a variation on the unsupervised graph clustering technique VEC, which improves upon the performance of the original algorithm significantly for sparse graphs. VEC employs a novel application of the state-of-the-art word2vec model to embed a graph in Euclidean space via random walks on the n…
New method for efficient ERG fitting on large graphs.
We propose a method for solving statistical mechanics problems defined on sparse graphs. It extracts a small Feedback Vertex Set (FVS) from the sparse graph, converting the sparse system to a much smaller system with many-body and dense interactions with an effective energy on every configuration of the FVS, then learn…
Sharp threshold found for Frechet mean of inhomogeneous graphs.
A method for inferring graph from multivariate time series using ADMM.
Graph matching with feature vectors is solved using a two-layer graph neural network.
DiAL uses Bayesian Dirichlet random fields for active learning with sparse labels.
Conjugate gradient methods improve efficiency for high-dimensional GLMMs.
The paper tackles sparse graph learning under Laplacian-related constraints, improving upon existing methods.
Statistical network modeling has focused on representing the graph as a discrete structure, namely the adjacency matrix, and considering the exchangeability of this array. In such cases, the Aldous-Hoover representation theorem (Aldous, 1981;Hoover, 1979} applies and informs us that the graph is necessarily either dens…
Unified analysis of multi-attribute graph learning with non-convex penalties.
GCNs converge and remain stable on large random graphs, revealing geometric insights.
The paper shows that relaxing assumptions about causal graphs can lead to exponentially large equivalence classes.
FGN models networks with fractal structures using Gaussian Multiplicative Chaos.
The sparse representation classifier (SRC) is shown to work well for image recognition problems that satisfy a subspace assumption. In this paper we propose a new implementation of SRC via screening, establish its equivalence to the original SRC under regularity conditions, and prove its classification consistency for …
Study spectral properties of sparse random graphs to recover latent vectors.
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…
Although many successful ensemble clustering approaches have been developed in recent years, there are still two limitations to most of the existing approaches. First, they mostly overlook the issue of uncertain links, which may mislead the overall consensus process. Second, they generally lack the ability to incorpora…
We consider the problem of detecting a tight community in a sparse random network. This is formalized as testing for the existence of a dense random subgraph in a random graph. Under the null hypothesis, the graph is a realization of an Erdös-Rényi graph on vertices and with connection probability ; under the …
A nonparametric Bayesian sparse graph linear dynamical system (SGLDS) is proposed to model sequentially observed multivariate data. SGLDS uses the Bernoulli-Poisson link together with a gamma process to generate an infinite dimensional sparse random graph to model state transitions. Depending on the sparsity pattern of…
Many computer vision tasks involve processing large amounts of data contaminated by outliers, which need to be detected and rejected. While outlier detection methods based on robust statistics have existed for decades, only recently have methods based on sparse and low-rank representation been developed along with guar…
New algorithms detect communities in sparse graphs with labeled data.
Study evaluates RKHS choices for assessing graph models using KSD tests.
New research shows how preconditioning can solve sparse linear regression problems efficiently.
The study extends stochastic block models to geometric settings, focusing on community detection and information flow.
New algorithms improve community detection and parameter estimation for PABM.
New algorithm tackles multi-agent bandits with heavy-tailed data.
We present an algorithm to identify sparse dependence structure in continuous and non-Gaussian probability distributions, given a corresponding set of data. The conditional independence structure of an arbitrary distribution can be represented as an undirected graph (or Markov random field), but most algorithms for lea…
Crowdsourcing platforms are now extensively used for conducting subjective pairwise comparison studies. In this setting, a pairwise comparison dataset is typically gathered via random sampling, either \emph{with} or \emph{without} replacement. In this paper, we use tools from random graph theory to analyze these two ra…
Researchers prove it's impossible to partially recover graph alignments in certain conditions.
We propose Sparse Neural Network architectures that are based on random or structured bipartite graph topologies. Sparse architectures provide compression of the models learned and speed-ups of computations, they can also surpass their unstructured or fully connected counterparts. As we show, even more compact topologi…
New method estimates Nishimori temperature for node classification in weighted graphs.
Stochastic Kronecker graphs supply a parsimonious model for large sparse real world graphs. They can specify the distribution of a large random graph using only three or four parameters. Those parameters have however proved difficult to choose in specific applications. This article looks at method of moments estimators…
Analyzes geodesic lengths in sparse networks, deriving a distribution.
Many popular network models rely on the assumption of (vertex) exchangeability, in which the distribution of the graph is invariant to relabelings of the vertices. However, the Aldous-Hoover theorem guarantees that these graphs are dense or empty with probability one, whereas many real-world graphs are sparse. We prese…
AutoGraph uses transformers to efficiently generate graphs as sequences.
Random graph matching refers to recovering the underlying vertex correspondence between two random graphs with correlated edges; a prominent example is when the two random graphs are given by Erdős-Rényi graphs . This can be viewed as an average-case and noisy version of the graph isomorphism problem.…