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Networks with Correlated Edge Processes

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Maria Suveges, Sofia C. Olhede

Methodology · Submitted 6 Jul 2022

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We illustrate the power of our proposed fitting method by analysing a hospital contact network, and this shows the high dimensional data challenge of modelling and inferring correlation between a large number of variables.

how it is stated · asserted 1.00 read the record →

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arXiv ID2207.02545 (version 1)
Pith IDDWA4D6Z5, Pith's permanent record of this paper and its review
CitationMaria Suveges, Sofia C. Olhede. "Networks with Correlated Edge Processes." arXiv:2207.02545 (2022). https://pith.science/paper/DWA4D6Z5
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,605 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.56 · human participants 0.54 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.06 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

This article proposes methods to model nonstationary temporal graph processes. This corresponds to modelling the observation of edge variables (relationships between objects) indicating interactions between pairs of nodes (or objects) exhibiting dependence (correlation) and evolution in time over interactions. This article thus blends (integer) time series models with flexible static network models to produce models of temporal graph data, and statistical fitting procedures for time-varying interaction data. We illustrate the power of our proposed fitting method by analysing a hospital contact network, and this shows the high dimensional data challenge of modelling and inferring correlation between a large number of variables.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 This article proposes methods to model nonstationary temporal graph processes. contribution 1.00 0.86 0.12 asserted 0.99
s1 This corresponds to modelling the observation of edge variables (relationships between objects) indicating interactions between pairs of nodes (or objects) exhibiting dependence (correlation) and evolution in time over interactions. background 0.47 0.19 0.06 asserted 0.98
s2 This article thus blends (integer) time series models with flexible static network models to produce models of temporal graph data, and statistical fitting procedures for time-varying interaction data. method 0.74 0.86 0.18 asserted 1.00
s3 We illustrate the power of our proposed fitting method by analysing a hospital contact network, and this shows the high dimensional data challenge of modelling and inferring correlation between a large number of variables. result 0.59 0.94 0.28 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from analysis of existing data 1.00.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

This article proposes methods to model nonstationary temporal graph processes. This corresponds to modelling the observation of edge variables (relationships between objects) indicating interactions between pairs of nodes (or objects) exhibiting dependence (correlation) and evolution in time over interactions. This article thus blends (integer) time series models with flexible static network models to produce models of temporal graph data, and statistical fitting procedures for time-varying interaction data. We illustrate the power of our proposed fitting method by analysing a hospital contact network, and this shows the high dimensional data challenge of modelling and inferring correlation between a large number of variables.

Human review

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How the literature uses it

What this paper does with the arXiv papers it cites

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

What it cites · 8 sentences without a confident label

Compares with 1 paper
  • Our approach differs in several aspects from the currently available models [ref] that explicitly take into account the autocorrelated nature of the links.

    Autoregressive Networks · Discussion · 0.97

Cites as background 2 papers

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