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Link Prediction With Personalized Social Influence
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| learn personalized entropy-based social influence from activity timestamps (PSI) instead of activity-based SRW | link prediction · URL dataset, 100% activities | MAP: 0.6165 -> 0.6919 | Helped |
| PSI instead of TI (temporal influence matrix factorization) | link prediction · Higgs dataset, 100% activities | MAP: 0.5327 -> 0.6026 | Helped |
| use only 10% of user activities (sparse activity) with PSI vs baselines | PSI · URL dataset, 10% activities | MAP: 0.4998 -> 0.5438 (SRW vs PSI) | Helped |
| PSI vs SRW under sparse activities | PSI · Higgs dataset, 10% activities | MAP: 0.4303 -> 0.4538 | Helped |
| SRW vs TI with all activities in Higgs (activity-informed baseline) | SRW · Higgs dataset, 100% activities | MAP: 0.5327 -> 0.5250 | Hurt |
| increase fraction of user activities used from 10% to 100% | PSI · URL dataset | MAP: 0.5438 -> 0.6919 | Helped |
Closest papers by meaning
- DiffIM: Differentiable Influence Minimization with Surrogate Modeling and Continuous Relaxation (AAAI 2025 · similarity 0.85)
- Revisiting Link Prediction: a data perspective (ICLR 2024 · similarity 0.84)
- Disentangled Modeling of Preferences and Social Influence for Group Recommendation (AAAI 2025 · similarity 0.84)
- Link Prediction in Multilayer Networks via Cross-Network Embedding (AAAI 2024 · similarity 0.83)
- Non-Euclidean Mixture Model for Social Network Embedding (NeurIPS 2024 · similarity 0.83)
- Graph Bayesian Optimization for Multiplex Influence Maximization (AAAI 2024 · similarity 0.83)
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