Assortative-Constrained Stochastic Block Models

• URL: http://arxiv.org/abs/2004.11890v1
• Date: Tue, 21 Apr 2020 19:52:59 GMT
• Title: Assortative-Constrained Stochastic Block Models
• Authors: Daniel Gribel, Thibaut Vidal, Michel Gendreau
• Abstract summary: Block models (SBMs) are often used to find assortative community structures in networks. In this study, we discuss the implications of this model-inherent indifference towards assortativity or disassortativity. We introduce a constrained SBM that imposes strong assortativity constraints, along with efficient algorithmic approaches to solve it.
• Score: 6.058868817939519
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Stochastic block models (SBMs) are often used to find assortative community structures in networks, such that the probability of connections within communities is higher than in between communities. However, classic SBMs are not limited to assortative structures. In this study, we discuss the implications of this model-inherent indifference towards assortativity or disassortativity, and show that this characteristic can lead to undesirable outcomes for networks which are presupposedy assortative but which contain a reduced amount of information. To circumvent this issue, we introduce a constrained SBM that imposes strong assortativity constraints, along with efficient algorithmic approaches to solve it. These constraints significantly boost community recovery capabilities in regimes that are close to the information-theoretic threshold. They also permit to identify structurally-different communities in networks representing cerebral-cortex activity regions.

Related papers

• Offline Learning for Combinatorial Multi-armed Bandits [56.96242764723241]
  Off-CMAB is the first offline learning framework for CMAB. Off-CMAB combines pessimistic reward estimations with solvers. Experiments on synthetic and real-world datasets highlight the superior performance of CLCB.
  arXiv  Detail & Related papers  (2025-01-31T16:56:18Z)
• VEC-SBM: Optimal Community Detection with Vectorial Edges Covariates [67.51637355249986]
  We study an extension of the Block Model (SBM), a widely used statistical framework for community detection. We propose a novel algorithm based on iterative refinement techniques and show that it optimally recovers the latent communities. We rigorously assess the added value of leveraging edge's side information in the community detection process.
  arXiv  Detail & Related papers  (2024-02-29T02:19:55Z)
• Capsa: A Unified Framework for Quantifying Risk in Deep Neural Networks [142.67349734180445]
  Existing algorithms that provide risk-awareness to deep neural networks are complex and ad-hoc. Here we present capsa, a framework for extending models with risk-awareness.
  arXiv  Detail & Related papers  (2023-08-01T02:07:47Z)
• Numerically assisted determination of local models in network scenarios [55.2480439325792]
  We develop a numerical tool for finding explicit local models that reproduce a given statistical behaviour. We provide conjectures for the critical visibilities of the Greenberger-Horne-Zeilinger (GHZ) and W distributions. The developed codes and documentation are publicly available at281.com/mariofilho/localmodels.
  arXiv  Detail & Related papers  (2023-03-17T13:24:04Z)
• Semantic Probabilistic Layers for Neuro-Symbolic Learning [83.25785999205932]
  We design a predictive layer for structured-output prediction (SOP) It can be plugged into any neural network guaranteeing its predictions are consistent with a set of predefined symbolic constraints. Our Semantic Probabilistic Layer (SPL) can model intricate correlations, and hard constraints, over a structured output space.
  arXiv  Detail & Related papers  (2022-06-01T12:02:38Z)
• SpatioTemporal Focus for Skeleton-based Action Recognition [66.8571926307011]
  Graph convolutional networks (GCNs) are widely adopted in skeleton-based action recognition. We argue that the performance of recent proposed skeleton-based action recognition methods is limited by the following factors. Inspired by the recent attention mechanism, we propose a multi-grain contextual focus module, termed MCF, to capture the action associated relation information.
  arXiv  Detail & Related papers  (2022-03-31T02:45:24Z)
• Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions [24.389388509299543]
  Implicit Maximum Likelihood Estimation is a framework for end-to-end learning of models combining discrete exponential family distributions and differentiable neural components. We show that I-MLE is competitive with and often outperforms existing approaches which rely on problem-specific relaxations.
  arXiv  Detail & Related papers  (2021-06-03T12:42:21Z)
• Extended Stochastic Block Models with Application to Criminal Networks [3.2211782521637393]
  We study covert networks that encode relationships among criminals. The coexistence of noisy block patterns limits the reliability of routinely-used community detection algorithms. We develop a new class of extended block models (ESBM) that infer groups of nodes having common connectivity patterns.
  arXiv  Detail & Related papers  (2020-07-16T19:06:16Z)
• Statistical inference of assortative community structures [0.0]
  We develop a methodology to infer assortative communities in networks based on a nonparametric Bayesian formulation of the planted partition model. We show that our method is not subject to a resolution limit, and can uncover an arbitrarily large number of communities.
  arXiv  Detail & Related papers  (2020-06-25T15:44:05Z)
• Community Detection in Bipartite Networks with Stochastic Blockmodels [0.0]
  In bipartite networks, community structures are restricted to being disassortative, in that nodes of one type are grouped according to common patterns of connection with nodes of the other type. This makes the block model (SBM) an intuitive choice for bipartite community detection. We introduce a nonparametric formulation of the SBM and a corresponding algorithm to efficiently find communities in bipartite networks.
  arXiv  Detail & Related papers  (2020-01-22T05:58:19Z)
• Pairwise Covariates-adjusted Block Model for Community Detection [9.423321226644891]
  Community detection is one of the most fundamental problems in network study. We introduce a pairwise co-adjusted generalization block model (PCABM) We show that PCABM is consistent under suitable sparsity conditions.
  arXiv  Detail & Related papers  (2018-07-10T03:37:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.