A new SBM for bipartite networks improves community detection in noisy data.
problem Community detection in bipartite networks with stochastic blockmodels.
method Bayesian nonparametric formulation of SBM for bipartite networks, algorithm to find communities efficiently.
result Improves community detection results over general SBMs, especially in noisy data.
A growing number of systems are represented as networks whose architecture conveys significant information and determines many of their properties. Examples of network architecture include modular, bipartite, and core-periphery structures. However inferring the network structure is a non trivial task and can depend som…
New spectral clustering method improves community detection in sparse networks.
problem Community detection in sparse networks using spectral clustering.
method Data-driven regularization and novel spectral truncation for adjacency matrix.
result Consistency results for community detection in general SBM and beyond.
CN-SBM clusters cancer samples and regions based on copy number variants.
problem Clonal evolution in cancer monitored by noisy copy number variants.
method Probabilistic framework using bipartite categorical block model.
result Improved model fit and clinically relevant subtypes identified.
New network approach improves topic modeling.
problem Improving topic models with better justification and practical solutions.
method Representing text corpora as bipartite networks and using a stochastic block model with non-parametric priors.
result Our SBM approach leads to better topic models than LDA in terms of statistical model selection.
A new model corrects SBM's bias for power-law degree networks.
problem SBM's incapability to handle power-law degree distributions.
method Introducing degree decay variables to encode varying degree distributions.
result PLD-SBM approximately preserves the scale-free feature in real networks and corrects SBM's bias.
Improved community detection in heterogeneous SBM with side information.
problem Misclassification in community detection with noisy labels.
method Optimal weighted message passing and minimum energy flow.
result Optimal weighting improves misclassification rate in heterogeneous SBM.
New algorithms cluster nodes in SBM graphs faster and more accurately.
problem Efficiently clustering nodes in graphs generated from SBM models.
method Inspired by Lloyd's algorithm, proposes model-free clustering methods for SBM graphs.
result Consistent estimation of node clusters and parameters in SBM graphs.
VEC-SBM detects communities using side information like texts and images.
problem Community detection in social networks with side information.
method Proposes a novel algorithm based on iterative refinement techniques.
result Optimally recovers latent communities with side information.
Combines SBMs and graph neural nets for graph embeddings.
problem Discovering community structure and link prediction on graphs.
method Sparse variational autoencoder integrating SBMs and graph neural nets.
result Encouraging link prediction results with interpretable latent structure.
Proposes a method for evaluating multiple dimensions of organizational effectiveness using DEA.
problem Evaluating multiple dimensions of organizational effectiveness in large data sets.
method Introduces two regularized DEA models (SBM and GP-SBM) to estimate both dimension-specific and aggregate efficiency scores.
result Demonstrates improved efficiency and validity compared to conventional methods.
The stochastic block model (SBM) is a popular tool for community detection in networks, but fitting it by maximum likelihood (MLE) involves a computationally infeasible optimization problem. We propose a new semidefinite programming (SDP) solution to the problem of fitting the SBM, derived as a relaxation of the MLE. W…
The Stochastic Block Model (SBM) is a widely used random graph model for networks with communities. Despite the recent burst of interest in recovering communities in the SBM from statistical and computational points of view, there are still gaps in understanding the fundamental information theoretic and computational l…
The stochastic block model accurately describes most empirical networks but struggles with large diameter and slow-mixing networks.
problem Assessing the quality of fit of the stochastic block model for empirical networks.
method Posterior predictive model checking using network descriptors.
result The stochastic block model can accurately describe most empirical networks but struggles with large diameter and slow-mixing networks.
SBMs learn manifold-like structures by mixing samples with a non-conservative field.
problem How SBMs learn data distributions on low-dimensional manifolds.
method Investigating linear approximations and subspaces of local feature vectors during diffusion.
result SBMs mix samples by a non-conservative field within the manifold, maintaining manifold-like structure.
Enhances SBM with continuous attributes for better network analysis.
problem Community detection in networks with multiple continuous attributes.
method Augmented stochastic block model with multivariate Gaussian parameters.
result Satisfactory performance in link prediction and collaborative filtering tasks.
This paper tackles exact recovery of clusters in a stochastic Ising model on a SBM graph.
problem Recovering clusters in a stochastic Ising model on a SBM graph.
method Proposes a Stochastic Ising Block Model (SIBM) and establishes a sharp threshold for exact recovery.
result Sharp threshold m∗ for exact recovery of clusters in SIBM, with O(n) time complexity for m≥m∗. Smoothing graphons improve link prediction in Bayesian SBM without increasing computational complexity.
problem Accurate modeling of exchangeable relational data with flexible and computationally efficient graphons.
method Introducing smoothing procedures to piecewise-constant graphons to create smoothing graphons, which allow continuous intensity values for relations.
result Smoothing graphons improve AUC and precision for link prediction in real-world data sets.
SubSearch detects graph outliers and estimates SBM parameters robustly.
problem Real-world graphs often deviate from ideal SBM assumptions.
method Subgraph search to find subgraphs that align with SBM assumptions.
result SubSearch accurately estimates SBM parameters and detects outliers.
Study explores properties of bipartite knots.
problem None explicitly stated; focuses on properties of bipartite knots.
method Exploration of combinatorial structure.
result Rich combinatorial structure of bipartite knots.
New method extends knot theory to non-bipartite knots, revealing PDs.
problem Extending knot theory to non-bipartite knots.
method Developed a new positive decomposition (PD) for HOMFLY polynomials of non-bipartite knots.
result PD exists for non-bipartite knots, not just bipartite ones.
DeepWalk embeddings converge on SBM graphs, recovering cluster structure.
problem Theoretical guarantees for DeepWalk embeddings on complex graphs.
method Solving a nonconvex optimization problem using random walks.
result DeepWalk embeddings on SBM graphs recover cluster structure with high probability.
Simplified Khovanov polynomials for bipartite links.
problem Computing Khovanov polynomials for bipartite links.
method Reduced Khovanov-Rozansky technique to Kauffman-Khovanov cycle calculus.
result Consistency demonstrated between reduced technique and bipartite Khovanov polynomials.
Bipartite networks are a common type of network data in which there are two types of vertices, and only vertices of different types can be connected. While bipartite networks exhibit community structure like their unipartite counterparts, existing approaches to bipartite community detection have drawbacks, including im…
A new SBM for non-negative zero-inflated edge weights in networks.
problem Modeling international trading networks with non-negative zero-inflated edge weights.
method Restricted Tweedie distribution and nodal information accounting.
result Efficient two-step algorithm for estimating covariate effects.
New method for community detection in sparse directed SBMs with exact recovery guarantees.
problem Exact recovery in sparse directed SBMs, especially with growing communities.
method Two-stage procedure: neighborhood-smoothing followed by K-means clustering. result Exact recovery of all community labels with probability tending to one under mild sparsity and separation conditions.
New model for detecting communities in weighted bipartite networks.
problem Lack of models for weighted bipartite networks.
method Introducing Bipartite Distribution-Free model and its extension.
result Spectral algorithms for consistent estimation of node labels.
Bipartite graphs with more edges than a threshold have positive curvature.
problem Determining the curvature of bipartite graphs based on edge density.
method Using a new formula for Lin--Lu--Yau curvature, the study establishes conditions for bipartite graphs to have positive curvature.
result Bipartite graphs with more edges than the specified threshold have positive Lin--Lu--Yau curvature.
Novel active learning detects network nodes for community detection.
problem Detecting community structure in networks.
method Maximal Expected Model Change (MEMC) criterion for querying network nodes.
result MEMC detects nodes that maximize community assignment likelihood changes.
New model improves community detection in networks with strong assortativity.
problem Classic SBMs fail to recover assortative communities in networks with reduced information.
method Introduced a constrained SBM with strong assortativity constraints and efficient algorithms.
result Significant boost in community recovery capabilities, especially close to information-theoretic threshold.
Unified algorithm for latent patterns in SBM and SWM models.
problem Invalid analysis due to misspecified models in graph analysis.
method Combining kernel learning, spectral graph theory, and dimensionality reduction.
result First statistically sound polynomial-time algorithm for latent patterns.
A new model adjusts for covariates in community detection.
problem Community detection in networks with covariate information.
method Pairwise covariates-adjusted stochastic block model (PCABM) with spectral clustering.
result Consistent community detection and coefficient estimates under sparsity conditions.
Cascade-BGNN efficiently learns node representations for large-scale bipartite graphs.
problem Efficiently learning node representations for large-scale bipartite graphs with limited labels.
method Cascade-BGNN uses customized Inter-domain Message Passing (IDMP) and Intra-domain Alignment (IDA) for efficient information aggregation.
result Cascade-BGNN achieves domain-consistent, self-supervised, and efficient node representation learning.
New research finds six bipartite intrinsically knotted graphs with 23 edges.
problem Identifying intrinsically knotted bipartite graphs with 23 edges.
method Analyzing embeddings and graph minors to find minimal intrinsically knotted graphs.
result No minor minimal intrinsically knotted bipartite graph exists with 23 edges.
Simplified Khovanov-Rozansky calculus for bipartite knots.
problem Complexity in calculating superpolynomials for knots.
method Bipartite calculus generalizes Khovanov-Rozansky calculus for a restricted class of knots.
result Simplification of Khovanov-Rozansky polynomials for bipartite knots.
Proves Khovanov homology has no torsion for bipartite circle graphs.
problem Proving properties of Khovanov homology for bipartite circle graphs.
method Proved homotopy equivalence of independence complexes to wedges of spheres.
result Extreme Khovanov homology has no torsion.
Improved bipartite link prediction using 2-hop paths.
problem Link prediction in bipartite networks without node attributes.
method Multiply reconstructed adjacency matrix with symmetrically normalized training adjacency matrix to form 2-hop paths.
result 2-hop paths improve link prediction performance.
Develops a model to detect shared communities in non-aligned graphs.
problem Clustering and community detection in non-aligned graphs with heterogeneous populations.
method Joint Stochastic Blockmodel (Joint SBM) and efficient spectral clustering.
result The joint model better estimates communities compared to separate SBMs on individual graphs.
Formula for interior polynomial of bipartite graphs derived from knot theory.
problem Deriving a formula for the interior polynomial of bipartite graphs.
method Applied knot theory, Ehrhart reciprocity, flyping and mutation.
result Proved a mirroring formula for the interior polynomial of bipartite graphs.
New model for detecting communities in weighted bipartite networks.
problem No model for community detection in overlapping bipartite weighted networks.
method Introduces BiMMDF model allowing any distribution with block structure.
result Efficient algorithm with theoretical guarantee of consistent estimation.
Paper introduces a new edge exchangeable block model for complex networks.
problem Limitations of the stochastic block model in analyzing complex networks.
method Develops a Bayesian nonparametric edge exchangeable block model.
result The new model outperforms state-of-the-art SBMs for link prediction.
Proposes a Nested Block Model to unify various network block models.
problem Lack of nested structure and differing parameter complexity among block models.
method Formulates a hierarchy of block models (NBM) that includes SBM, DCBM, and PABM as special cases.
result Allows clustering and estimation without preliminary testing, simplifying model selection.
A new method calculates HOMFLY-PT polynomials for bipartite links.
problem Computing HOMFLY-PT polynomials for bipartite links efficiently.
method Generalizes Goeritz matrix method for bipartite links.
result Reduces HOMFLY-PT polynomial calculation to matrix algebra.
Paper shows SBMs are like surface tension problems, aiding network clustering.
problem Cluster network nodes into communities with dense internal connections.
method Used maximum likelihood estimation and network analogs of surface-tension algorithms.
result Successfully recovered planted community structure in synthetic networks.
We define integral odd Khovanov homology of principally unimodular bipartite graph-links.
A new method for efficient inference and model selection in SBMs using OT.
problem Efficient inference and model selection in stochastic block models.
method Interpreting MLVI as srGW with entropic regularization, then unregularizing for sparse solutions, and adding a sparsity-promoting regularizer.
result The method consistently recovers SBM parameters and selects the number of clusters in finite samples.
New algorithm recovers communities in broader network models.
problem Finding communities in complex networks is challenging.
method Spectral clustering on Preference Frame Models with Normalized Laplacian.
result Spectral clustering works on broader network models with similar guarantees.
Research tackles learning vertex representations for bipartite networks.
problem Lack of research on learning vertex representations for bipartite networks.
method Apply generic methods like node2vec and LINE, but ignore vertex type information.
result Generic methods are suboptimal for bipartite networks due to different properties and patterns.