Influence Prediction in Collaboration Networks: An Empirical Study on arXiv
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918373451890688 |
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| author | Lin, Marina Schaposnik, Laura P. Wu, Raina |
| author_facet | Lin, Marina Schaposnik, Laura P. Wu, Raina |
| contents | This paper provides an empirical study of the Social Sphere Model for influence prediction, previously introduced by the authors, combining link prediction with top-k centrality-based selection. We apply the model to the temporal arXiv General Relativity and Quantum Cosmology collaboration network, evaluating its performance under varying edge sampling rates and prediction horizons to reflect different levels of initial data completeness and network evolution. Accuracy is assessed using mean squared error in both link prediction and influence maximization tasks. The results show that the model effectively identifies latent influencers, i.e., nodes that are not initially central but later influential, and performs best with denser initial graphs. Among the similarity measures tested, the newly introduced RA-2 metric consistently yields the lowest prediction errors. These findings support the practical applicability of the model to predict real-world influence in evolving networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13029 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Influence Prediction in Collaboration Networks: An Empirical Study on arXiv Lin, Marina Schaposnik, Laura P. Wu, Raina Social and Information Networks Combinatorics This paper provides an empirical study of the Social Sphere Model for influence prediction, previously introduced by the authors, combining link prediction with top-k centrality-based selection. We apply the model to the temporal arXiv General Relativity and Quantum Cosmology collaboration network, evaluating its performance under varying edge sampling rates and prediction horizons to reflect different levels of initial data completeness and network evolution. Accuracy is assessed using mean squared error in both link prediction and influence maximization tasks. The results show that the model effectively identifies latent influencers, i.e., nodes that are not initially central but later influential, and performs best with denser initial graphs. Among the similarity measures tested, the newly introduced RA-2 metric consistently yields the lowest prediction errors. These findings support the practical applicability of the model to predict real-world influence in evolving networks. |
| title | Influence Prediction in Collaboration Networks: An Empirical Study on arXiv |
| topic | Social and Information Networks Combinatorics |
| url | https://arxiv.org/abs/2508.13029 |