Papers › On the effect of the average clustering coefficient on topology-based link prediction...
On the effect of the average clustering coefficient on topology-based link prediction in featureless graphs
Mehrdad Rafiepour, S. Mehdi Vahidipour
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
Link prediction is a fundamental problem in graph theory with diverse applications, including recommender systems, community detection, and identifying spurious connections. While feature-based methods achieve high accuracy, their reliance on node attributes limits their applicability in featureless graphs. For such graphs, structure-based approaches, including common neighbor-based and degree-dependent methods, are commonly employed. However, the effectiveness of these methods depends on graph density, with common neighbor-based algorithms performing well in dense graphs and degree-dependent methods being more suitable for sparse or tree-like graphs. Despite this, the literature lacks a clear criterion to distinguish between dense and sparse graphs. This paper introduces the average clustering coefficient as a criterion for assessing graph density to assist with the choice of link prediction algorithms. To address the scarcity of datasets for empirical analysis, we propose a novel graph generation method based on the Barabasi-Albert model, which enables controlled variation of graph density while preserving structural heterogeneity. Through comprehensive experiments on synthetic and real-world datasets, we establish an empirical boundary for the average clustering coefficient that facilitates the selection of effective link prediction techniques.
Code
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Link Property Prediction | ogbl-collab | Jaccard Index | Ext. data | No | #24 of 34 | Archive leaderboard | report |
| Link Property Prediction | ogbl-collab | Jaccard Index | Number of params | 0 | #24 of 34 | Archive leaderboard | report |
| Link Property Prediction | ogbl-collab | Jaccard Index | Test Hits@50 | 0.5050 ± 0.0000 | #24 of 34 | Archive leaderboard | report |
| Link Property Prediction | ogbl-collab | Jaccard Index | Validation Hits@50 | 0.6098 ± 0.0000 | #24 of 34 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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