Revealing consensus and dissensus between network partitions

• URL: http://arxiv.org/abs/2005.13977v5
• Date: Wed, 21 Apr 2021 22:26:28 GMT
• Title: Revealing consensus and dissensus between network partitions
• Authors: Tiago P. Peixoto
• Abstract summary: Community detection methods attempt to divide a network into groups of nodes that share similar properties, thus revealing its large-scale structure. Many methods exist to establish a consensus among them in the form of a single partition "point estimate" that summarizes the whole distribution. Here we show that it is in general not possible to obtain a consistent answer from such point estimates when the underlying distribution is too heterogeneous. We provide a comprehensive set of methods designed to characterize and summarize complex populations of partitions in a manner that captures not only the existing consensus, but also the dissensus between elements of the population.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Community detection methods attempt to divide a network into groups of nodes that share similar properties, thus revealing its large-scale structure. A major challenge when employing such methods is that they are often degenerate, typically yielding a complex landscape of competing answers. As an attempt to extract understanding from a population of alternative solutions, many methods exist to establish a consensus among them in the form of a single partition "point estimate" that summarizes the whole distribution. Here we show that it is in general not possible to obtain a consistent answer from such point estimates when the underlying distribution is too heterogeneous. As an alternative, we provide a comprehensive set of methods designed to characterize and summarize complex populations of partitions in a manner that captures not only the existing consensus, but also the dissensus between elements of the population. Our approach is able to model mixed populations of partitions where multiple consensuses can coexist, representing different competing hypotheses for the network structure. We also show how our methods can be used to compare pairs of partitions, how they can be generalized to hierarchical divisions, and be used to perform statistical model selection between competing hypotheses.

Related papers

• Collaborative Heterogeneous Causal Inference Beyond Meta-analysis [68.4474531911361]
  We propose a collaborative inverse propensity score estimator for causal inference with heterogeneous data. Our method shows significant improvements over the methods based on meta-analysis when heterogeneity increases.
  arXiv  Detail & Related papers  (2024-04-24T09:04:36Z)
• Generative models for two-ground-truth partitions in networks [0.0]
  Cross-block model allows for two distinct partitions to be built into the mesoscale structure of a single benchmark network. We demonstrate a use case of the benchmark model by appraising the power of block models to detect implicitly planted coexisting bi-community and core-periphery structures. Our findings suggest that in most instances only one - in some way dominating - structure can be detected, even in the presence of other partitions.
  arXiv  Detail & Related papers  (2023-02-06T14:02:28Z)
• Is it easier to count communities than find them? [82.90505487525533]
  We consider certain hypothesis testing problems between models with different community structures. We show that testing between two options is as hard as finding the communities.
  arXiv  Detail & Related papers  (2022-12-21T09:35:19Z)
• Counting Like Human: Anthropoid Crowd Counting on Modeling the Similarity of Objects [92.80955339180119]
  mainstream crowd counting methods regress density map and integrate it to obtain counting results. Inspired by this, we propose a rational and anthropoid crowd counting framework.
  arXiv  Detail & Related papers  (2022-12-02T07:00:53Z)
• Implicit models, latent compression, intrinsic biases, and cheap lunches in community detection [0.0]
  Community detection aims to partition a network into clusters of nodes to summarize its large-scale structure. Some community detection methods are inferential, explicitly deriving the clustering objective through a probabilistic generative model. Other methods are descriptive, dividing a network according to an objective motivated by a particular application. We present a solution that associates any community detection objective, inferential or descriptive, with its corresponding implicit network generative model.
  arXiv  Detail & Related papers  (2022-10-17T15:38:41Z)
• Exact Recovery in the General Hypergraph Stochastic Block Model [92.28929858529679]
  This paper investigates fundamental limits of exact recovery in the general d-uniform hypergraph block model (d-HSBM) We show that there exists a sharp threshold such that exact recovery is achievable above the threshold and impossible below it.
  arXiv  Detail & Related papers  (2021-05-11T03:39:08Z)
• Partition-based formulations for mixed-integer optimization of trained ReLU neural networks [66.88252321870085]
  This paper introduces a class of mixed-integer formulations for trained ReLU neural networks. At one extreme, one partition per input recovers the convex hull of a node, i.e., the tightest possible formulation for each node.
  arXiv  Detail & Related papers  (2021-02-08T17:27:34Z)
• 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)
• Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference [73.23326654892963]
  We propose a matching method that recovers direct treatment effects from randomized experiments where units are connected in an observed network. Our method matches units almost exactly on counts of unique subgraphs within their neighborhood graphs.
  arXiv  Detail & Related papers  (2020-03-02T15:21:20Z)
• Automated extraction of mutual independence patterns using Bayesian comparison of partition models [7.6146285961466]
  Mutual independence is a key concept in statistics that characterizes the structural relationships between variables. Existing methods to investigate mutual independence rely on the definition of two competing models. We propose a general Markov chain Monte Carlo (MCMC) algorithm to numerically approximate the posterior distribution on the space of all patterns of mutual independence.
  arXiv  Detail & Related papers  (2020-01-15T16:21:48Z)

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.