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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.

168,742 papers · 148 categories

Trend · papers per month

count% of corpus
0111 · Oct 201919922001200920172026
5 results for “PABM”
The Hierarchy of Block Modelsstat.ML

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.
Nested Block ModelNetwork Block ModelsSBMDCBMPABM
2020-02-07abs ↗pdf ↗
Optimal Graph Clustering without Edge Density Signalscs.LG

Paper characterizes optimal graph clustering limits under a new model.

problem Graph clustering under varying edge density signals.
method Introduced Popularity-Adjusted Block Model (PABM) to address SBM and DCBM limitations.
result Cluster recovery possible even when edge density signals vanish, highlighting local connectivity differences.
Graph ClusteringPopularity-Adjusted Block ModelStochastic Block ModelDegree-Corrected Block ModelEdge Density
2025-10-24abs ↗pdf ↗
Popularity Adjusted Block Models are Generalized Random Dot Product Graphsstat.ML

New algorithms improve community detection and parameter estimation for PABM.

problem Improving community detection and parameter estimation for PABM.
method Connecting PABM to GRDPG, constructing new algorithms, and deriving asymptotic properties.
result Absolute number of community detection errors tends to zero as graph vertices increase.
Popularity Adjusted Block ModelGeneralized Random Dot Product GraphCommunity DetectionParameter EstimationAsymptotic Properties
2021-09-09abs ↗pdf ↗
Strongly Consistent Community Detection in Popularity Adjusted Block Modelsstat.ME

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

problem Challenges in effectively adapting spectral clustering techniques and achieving strong consistency in label recovery.
method Proposed Thresholded Cosine Spectral Clustering (TCSC) and one-step Refined TCSC algorithms, with strong consistency proofs.
result One-step Refined TCSC achieves strong consistency in community detection under PABM, correctly recovering all labels with high probability.
community detectionblock modelsspectral clusteringstrong consistencypopularity adjustment
2025-06-08abs ↗pdf ↗
Sparse Popularity Adjusted Stochastic Block Modelstat.ML

In the present paper we study a sparse stochastic network enabled with a block structure. The popular Stochastic Block Model (SBM) and the Degree Corrected Block Model (DCBM) address sparsity by placing an upper bound on the maximum probability of connections between any pair of nodes. As a result, sparsity describes o…

2019-10-03abs ↗pdf ↗