Papers › Predicting Missing Links via Local Information
Predicting Missing Links via Local Information
Tao Zhou, Linyuan Lu, Yi-Cheng Zhang
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.
Missing link prediction of networks is of both theoretical interest and practical significance in modern science. In this paper, we empirically investigate a simple framework of link prediction on the basis of node similarity. We compare nine well-known local similarity measures on six real networks. The results indicate that the simplest measure, namely common neighbors, has the best overall performance, and the Adamic-Adar index performs the second best. A new similarity measure, motivated by the resource allocation process taking place on networks, is proposed and shown to have higher prediction accuracy than common neighbors. It is found that many links are assigned same scores if only the information of the nearest neighbors is used. We therefore design another new measure exploited information of the next nearest neighbors, which can remarkably enhance the prediction accuracy.
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Code
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Code Syntology ran Syntology
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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-ppa | Resource Allocation | Ext. data | No | #14 of 26 | Archive leaderboard | report |
| Link Property Prediction | ogbl-ppa | Resource Allocation | Number of params | 0 | #14 of 26 | Archive leaderboard | report |
| Link Property Prediction | ogbl-ppa | Resource Allocation | Test Hits@100 | 0.4933 ± 0.0000 | #14 of 26 | Archive leaderboard | report |
| Link Property Prediction | ogbl-ppa | Resource Allocation | Validation Hits@100 | 0.4722 ± 0.0000 | #14 of 26 | 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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