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Sequential Changepoint Approach for Online Community Detection

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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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David Marangoni-Simonsen, Yao Xie

Machine Learning · Submitted 22 Jul 2014

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

The authors' main claim Finally, numerical examples show that the mixture and the H-Mix methods can both detect a community quickly with a lower complexity than the ES method.

how it is stated · asserted 0.99 read the record →

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arXiv ID1407.5978 (version 3)
Pith IDVNPQL4MF, Pith's permanent record of this paper and its review
CitationDavid Marangoni-Simonsen, Yao Xie. "Sequential Changepoint Approach for Online Community Detection." arXiv:1407.5978 (2014). https://pith.science/paper/VNPQL4MF
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Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 8,683 input tokens, $0.0004 reported.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.94 · human participants 0.02 · controlled comparison 0.15 · randomized assignment 0.01 · states quantitative result 0.29 · compares with prior results 0.67 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v3. Results and the main claim are underlined; .

We present new algorithms for detecting the emergence of a community in large networks from sequential observations. The networks are modeled using Erdos-Renyi random graphs with edges forming between nodes in the community with higher probability. Based on statistical changepoint detection methodology, we develop three algorithms: the Exhaustive Search (ES), the mixture, and the Hierarchical Mixture (H-Mix) methods. Performance of these methods is evaluated by the average run length (ARL), which captures the frequency of false alarms, and the detection delay. Numerical comparisons show that the ES method performs the best; however, it is exponentially complex. The mixture method is polynomially complex by exploiting the fact that the size of the community is typically small in a large network. However, it may react to a group of active edges that do not form a community. This issue is resolved by the H-Mix method, which is based on a dendrogram decomposition of the network. We present an asymptotic analytical expression for ARL of the mixture method when the threshold is large. Numerical simulation verifies that our approximation is accurate even in the non-asymptotic regime. Hence, it can be used to determine a desired threshold efficiently. Finally, numerical examples show that the mixture and the H-Mix methods can both detect a community quickly with a lower complexity than the ES method.

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 We present new algorithms for detecting the emergence of a community in large networks from sequential observations. contribution 1.00 0.94 0.08 asserted 1.00
s1 The networks are modeled using Erdos-Renyi random graphs with edges forming between nodes in the community with higher probability. method 0.90 0.18 0.02 asserted 1.00
s2 Based on statistical changepoint detection methodology, we develop three algorithms: the Exhaustive Search (ES), the mixture, and the Hierarchical Mixture (H-Mix) methods. contribution 0.65 0.94 0.06 asserted 1.00
s3 Performance of these methods is evaluated by the average run length (ARL), which captures the frequency of false alarms, and the detection delay. method 0.96 0.82 0.04 asserted 0.99
s4 Numerical comparisons show that the ES method performs the best; however, it is exponentially complex. result 0.92 0.94 0.21 asserted 1.00
s5 The mixture method is polynomially complex by exploiting the fact that the size of the community is typically small in a large network. method 0.65 0.73 0.09 asserted 1.00
s6 However, it may react to a group of active edges that do not form a community. limitation 1.00 0.61 0.03 hedged possibility 1.00
s7 This issue is resolved by the H-Mix method, which is based on a dendrogram decomposition of the network. method 0.65 0.80 0.07 asserted 1.00
s8 We present an asymptotic analytical expression for ARL of the mixture method when the threshold is large. contribution 0.92 0.95 0.05 asserted 1.00
s9 Numerical simulation verifies that our approximation is accurate even in the non-asymptotic regime. result 0.99 0.95 0.06 asserted 1.00
s10 Hence, it can be used to determine a desired threshold efficiently. interpretation 0.93 0.56 0.07 asserted 0.80
s11 Finally, numerical examples show that the mixture and the H-Mix methods can both detect a community quickly with a lower complexity than the ES method. result 0.99 0.95 0.57 asserted 0.99

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 1.00.

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sources read
  • arXiv listing abstract v3
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • 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

We present new algorithms for detecting the emergence of a community in large networks from sequential observations. The networks are modeled using Erdos-Renyi random graphs with edges forming between nodes in the community with higher probability. Based on statistical changepoint detection methodology, we develop three algorithms: the Exhaustive Search (ES), the mixture, and the Hierarchical Mixture (H-Mix) methods. Performance of these methods is evaluated by the average run length (ARL), which captures the frequency of false alarms, and the detection delay. Numerical comparisons show that the ES method performs the best; however, it is exponentially complex. The mixture method is polynomially complex by exploiting the fact that the size of the community is typically small in a large network. However, it may react to a group of active edges that do not form a community. This issue is resolved by the H-Mix method, which is based on a dendrogram decomposition of the network. We present an asymptotic analytical expression for ARL of the mixture method when the threshold is large. Numerical simulation verifies that our approximation is accurate even in the non-asymptotic regime. Hence, it can be used to determine a desired threshold efficiently. Finally, numerical examples show that the mixture and the H-Mix methods can both detect a community quickly with a lower complexity than the ES method.

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