Influence Maximization in Temporal Social Networks with a Cold-Start Problem: A Supervised Approach
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912329695756288 |
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| author | Xie, Laixin Zhang, Ying Wang, Xiyuan Liu, Shiyi Gao, Shenghan Xing, Xingxing Wan, Wei Zhang, Haipeng Li, Quan |
| author_facet | Xie, Laixin Zhang, Ying Wang, Xiyuan Liu, Shiyi Gao, Shenghan Xing, Xingxing Wan, Wei Zhang, Haipeng Li, Quan |
| contents | Influence Maximization (IM) in temporal graphs focuses on identifying influential "seeds" that are pivotal for maximizing network expansion. We advocate defining these seeds through Influence Propagation Paths (IPPs), which is essential for scaling up the network. Our focus lies in efficiently labeling IPPs and accurately predicting these seeds, while addressing the often-overlooked cold-start issue prevalent in temporal networks. Our strategy introduces a motif-based labeling method and a tensorized Temporal Graph Network (TGN) tailored for multi-relational temporal graphs, bolstering prediction accuracy and computational efficiency. Moreover, we augment cold-start nodes with new neighbors from historical data sharing similar IPPs. The recommendation system within an online team-based gaming environment presents subtle impact on the social network, forming multi-relational (i.e., weak and strong) temporal graphs for our empirical IM study. We conduct offline experiments to assess prediction accuracy and model training efficiency, complemented by online A/B testing to validate practical network growth and the effectiveness in addressing the cold-start issue. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_11245 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Influence Maximization in Temporal Social Networks with a Cold-Start Problem: A Supervised Approach Xie, Laixin Zhang, Ying Wang, Xiyuan Liu, Shiyi Gao, Shenghan Xing, Xingxing Wan, Wei Zhang, Haipeng Li, Quan Social and Information Networks Artificial Intelligence Influence Maximization (IM) in temporal graphs focuses on identifying influential "seeds" that are pivotal for maximizing network expansion. We advocate defining these seeds through Influence Propagation Paths (IPPs), which is essential for scaling up the network. Our focus lies in efficiently labeling IPPs and accurately predicting these seeds, while addressing the often-overlooked cold-start issue prevalent in temporal networks. Our strategy introduces a motif-based labeling method and a tensorized Temporal Graph Network (TGN) tailored for multi-relational temporal graphs, bolstering prediction accuracy and computational efficiency. Moreover, we augment cold-start nodes with new neighbors from historical data sharing similar IPPs. The recommendation system within an online team-based gaming environment presents subtle impact on the social network, forming multi-relational (i.e., weak and strong) temporal graphs for our empirical IM study. We conduct offline experiments to assess prediction accuracy and model training efficiency, complemented by online A/B testing to validate practical network growth and the effectiveness in addressing the cold-start issue. |
| title | Influence Maximization in Temporal Social Networks with a Cold-Start Problem: A Supervised Approach |
| topic | Social and Information Networks Artificial Intelligence |
| url | https://arxiv.org/abs/2504.11245 |