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Comparing discriminating abilities of evaluation metrics in link prediction

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Xinshan Jiao, Shuyan Wan, Qian Liu, Yilin Bi, Yan-Li Lee, En Xu, Dong Hao, Tao Zhou

Social and Information Networks · Submitted 8 Jan 2024

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

The authors' main claim The results indicate that the discriminating abilities of the three metrics, AUC, AUPR, and NDCG, are significantly higher than those of other metrics.

how it is stated · hedged possibility 0.58 read the record →

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arXiv ID2401.03673 (version 1)
Pith IDZY2O6JAI, Pith's permanent record of this paper and its review
CitationDong Hao, En Xu, Qian Liu, Shuyan Wan, Tao Zhou, Xinshan Jiao, Yan-Li Lee, Yilin Bi. "Comparing discriminating abilities of evaluation metrics in link prediction." arXiv:2401.03673 (2024). https://pith.science/paper/ZY2O6JAI
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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, 5,674 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.07 · human participants 0.02 · controlled comparison 0.12 · randomized assignment 0.02 · states quantitative result 0.47 · compares with prior results 0.28 · mentions code or data availability 0.02.

The abstract, sentence by sentence

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

Link prediction aims to predict the potential existence of links between two unconnected nodes within a network based on the known topological characteristics. Evaluation metrics are used to assess the effectiveness of algorithms in link prediction. The discriminating ability of these evaluation metrics is vitally important for accurately evaluating link prediction algorithms. In this study, we propose an artificial network model, based on which one can adjust a single parameter to monotonically and continuously turn the prediction accuracy of the specifically designed link prediction algorithm. Building upon this foundation, we show a framework to depict the effectiveness of evaluating metrics by focusing on their discriminating ability. Specifically, a quantitative comparison in the abilities of correctly discerning varying prediction accuracies was conducted encompassing nine evaluation metrics: Precision, Recall, F1-Measure, Matthews Correlation Coefficient (MCC), Balanced Precision (BP), the Area Under the receiver operating characteristic Curve (AUC), the Area Under the Precision-Recall curve (AUPR), Normalized Discounted Cumulative Gain (NDCG), and the Area Under the magnified ROC (AUC-mROC). The results indicate that the discriminating abilities of the three metrics, AUC, AUPR, and NDCG, are significantly higher than those of other metrics.

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 Link prediction aims to predict the potential existence of links between two unconnected nodes within a network based on the known topological characteristics. background 1.00 0.04 0.01 asserted 0.99
s1 Evaluation metrics are used to assess the effectiveness of algorithms in link prediction. background 1.00 0.04 0.01 asserted 1.00
s2 The discriminating ability of these evaluation metrics is vitally important for accurately evaluating link prediction algorithms. background 0.99 0.05 0.03 asserted 1.00
s3 In this study, we propose an artificial network model, based on which one can adjust a single parameter to monotonically and continuously turn the prediction accuracy of the specifically designed link prediction algorithm. method 0.88 0.91 0.02 asserted 0.98
s4 Building upon this foundation, we show a framework to depict the effectiveness of evaluating metrics by focusing on their discriminating ability. contribution 0.62 0.86 0.03 asserted 0.99
s5 Specifically, a quantitative comparison in the abilities of correctly discerning varying prediction accuracies was conducted encompassing nine evaluation metrics: Precision, Recall, F1-Measure, Matthews Correlation Coefficient (MCC), Balanced Precision (BP), the Area Under the receiver operating characteristic Curve (AUC), the Area Under the Precision-Recall curve (AUPR), Normalized Discounted Cumulative Gain (NDCG), and the Area Under the magnified ROC (AUC-mROC). method 0.96 0.95 0.05 asserted 0.93
s6 The results indicate that the discriminating abilities of the three metrics, AUC, AUPR, and NDCG, are significantly higher than those of other metrics. result 1.00 0.96 0.90 hedged possibility 0.58

What kind of paper the classifier reports this is: simulation study 0.78 · evidence from simulation or computation 0.94.

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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

Link prediction aims to predict the potential existence of links between two unconnected nodes within a network based on the known topological characteristics. Evaluation metrics are used to assess the effectiveness of algorithms in link prediction. The discriminating ability of these evaluation metrics is vitally important for accurately evaluating link prediction algorithms. In this study, we propose an artificial network model, based on which one can adjust a single parameter to monotonically and continuously turn the prediction accuracy of the specifically designed link prediction algorithm. Building upon this foundation, we show a framework to depict the effectiveness of evaluating metrics by focusing on their discriminating ability. Specifically, a quantitative comparison in the abilities of correctly discerning varying prediction accuracies was conducted encompassing nine evaluation metrics: Precision, Recall, F1-Measure, Matthews Correlation Coefficient (MCC), Balanced Precision (BP), the Area Under the receiver operating characteristic Curve (AUC), the Area Under the Precision-Recall curve (AUPR), Normalized Discounted Cumulative Gain (NDCG), and the Area Under the magnified ROC (AUC-mROC). The results indicate that the discriminating abilities of the three metrics, AUC, AUPR, and NDCG, are significantly higher than those of other metrics.

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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

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  • For example, if we intend to encourage the early retrieval ability, NDCG and AUC-mROC are good candidates [ref] , while if we emphasize on the discriminating ability, AUC and NDCG are superior [ref] .

    Inconsistency of evaluation metrics in link prediction · Discussion · 0.97

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