arXiv research

Unified spectral clustering for sparse networks with heterogeneous degrees.

New spectral clustering method for graphs with uneven node degrees.

A federated model learns shared archetypes from heterogeneous clients in continual learning.

In this article, we study spectral methods for community detection based on $α$-parametrized normalized modularity matrix hereafter called ${\bf L}_α$ in heterogeneous graph models. We show, in a regime where community detection is not asymptotically trivial, that ${\bf L}_α$ can be well approximated by a more tract…

Study on identifying and inferring nonlinear dynamics on unknown networks.

Flexible model captures varying scales in data clusters.

Deep learning method clusters multi-view data matrices.

Study identifies cancer genes through graph anomaly analysis of protein interactions.

Spectral clustering is one of the most popular, yet still incompletely understood, methods for community detection on graphs. This article studies spectral clustering based on the Bethe-Hessian matrix $H_r = (r^2-1)I_n + D-rA$ for sparse heterogeneous graphs (following the degree-corrected stochastic block model) in a …

CCs learn high-dimensional distributions from heterogeneous data.

Proposes HeteroJIVE for joint subspace estimation in multi-view data with statistical and structural heterogeneity.

Change detection in dynamic networks is an important problem in many areas, such as fraud detection, cyber intrusion detection and health care monitoring. It is a challenging problem because it involves a time sequence of graphs, each of which is usually very large and sparse with heterogeneous vertex degrees, resultin…

Improved algorithm for conditional linear regression with heterogeneous covariances.

Unified approach for interpretable regression with flexible modeling.

The paper corrects for node degree in spectral clustering using random walk Laplacian.

This paper characterizes and explains the disagreement between two graph embedding methods.

Paper improves robust spectral clustering for noisy data.

New method identifies latent treatment effects from proxy models.

ie-HGCN addresses HIN challenges by efficiently learning node representations.

TransNet improves community detection on target networks using privacy-preserved source networks.

Spectral clustering is a fast and popular algorithm for finding clusters in networks. Recently, Chaudhuri et al. (2012) and Amini et al.(2012) proposed inspired variations on the algorithm that artificially inflate the node degrees for improved statistical performance. The current paper extends the previous statistical…

We consider community detection in Degree-Corrected Stochastic Block Models (DC-SBM). We propose a spectral clustering algorithm based on a suitably normalized adjacency matrix. We show that this algorithm consistently recovers the block-membership of all but a vanishing fraction of nodes, in the regime where the lowes…

Single-particle electron cryomicroscopy is an essential tool for high-resolution 3D reconstruction of proteins and other biological macromolecules. An important challenge in cryo-EM is the reconstruction of non-rigid molecules with parts that move and deform. Traditional reconstruction methods fail in these cases, resu…

Researchers approximate spectral targets on manifolds with constant negative curvature.

GWCA analyzes cross-graph correlations for movie retrieval.

We consider the problem of estimating a low-rank matrix from a noisy observed matrix. Previous work has shown that the optimal method depends crucially on the choice of loss function. In this paper, we use a family of weighted loss functions, which arise naturally for problems such as submatrix denoising, denoising wit…

New method estimates heterogeneous treatment effects with improved guarantees.

Electroencephalographic (EEG) monitoring of neural activity is widely used for sleep disorder diagnostics and research. The standard of care is to manually classify 30-second epochs of EEG time-domain traces into 5 discrete sleep stages. Unfortunately, this scoring process is subjective and time-consuming, and the defi…

Enhancing spectral embedding for low-dimensional embeddings in rare disease cohorts

A panoply of multi-view clustering algorithms has been developed to deal with prevalent multi-view data. Among them, spectral clustering-based methods have drawn much attention and demonstrated promising results recently. Despite progress, there are still two fundamental questions that stay unanswered to date. First, h…

HSSE framework embeds single-cell RNA-seq data at multiple scales.

A tensor network is a diagram that specifies a way to "multiply" a collection of tensors together to produce another tensor (or matrix). Many existing algorithms for tensor problems (such as tensor decomposition and tensor PCA), although they are not presented this way, can be viewed as spectral methods on matrices bui…

The paper improves spectral ranking methods for diverse comparison graphs.

Improves community detection in directed networks with theoretical guarantees.

Proposes a spectral method for jointly smooth functions on multiple manifolds.

We define a random-matrix ensemble given by the infinite-time covariance matrices of Ornstein-Uhlenbeck processes at different temperatures coupled by a Gaussian symmetric matrix. The spectral properties of this ensemble are shown to be in qualitative agreement with some stylized facts of financial markets. Through the…

Paper proposes efficient methods for high-order clustering in tensor block models.

Clustering and community detection with multiple graphs have typically focused on aligned graphs, where there is a mapping between nodes across the graphs (e.g., multi-view, multi-layer, temporal graphs). However, there are numerous application areas with multiple graphs that are only partially aligned, or even unalign…

New algorithms improve community detection in network data with strong consistency.

Multilayer graphs are commonly used for representing different relations between entities and handling heterogeneous data processing tasks. Non-standard multilayer graph clustering methods are needed for assigning clusters to a common multilayer node set and for combining information from each layer. This paper present…

Multilayer graphs are commonly used for representing different relations between entities and handling heterogeneous data processing tasks. New challenges arise in multilayer graph clustering for assigning clusters to a common multilayer node set and for combining information from each layer. This paper presents a theo…

Reduces complexity of financial contagion dynamics on networks.

New bounds on learning shared representations improve model performance and efficiency.

Proposes a new algorithm to estimate invariant subspaces across multilayer networks.

Paper characterizes optimal graph clustering limits under a new model.

Advances neural tri-factorization for clustering and discordance analysis of multi-typed data.

We present a scalable Gaussian process model for identifying and characterizing smooth multidimensional changepoints, and automatically learning changes in expressive covariance structure. We use Random Kitchen Sink features to flexibly define a change surface in combination with expressive spectral mixture kernels to …

Paper introduces RAS for robust MTL with contamination.