Deep Identification of Propagation Trees
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866929738233151488 |
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| author | Memon, Zeeshan Ling, Chen Kong, Ruochen Seshagiri, Vishwanath Zufle, Andreas Zhao, Liang |
| author_facet | Memon, Zeeshan Ling, Chen Kong, Ruochen Seshagiri, Vishwanath Zufle, Andreas Zhao, Liang |
| contents | Understanding propagation structures in graph diffusion processes, such as epidemic spread or misinformation diffusion, is a fundamental yet challenging problem. While existing methods primarily focus on source localization, they cannot reconstruct the underlying propagation trees i.e., "who infected whom", which are substantial for tracking the propagation pathways and investigate diffusion mechanisms. In this work, we propose Deep Identification of Propagation Trees (DIPT), a probabilistic framework that infers propagation trees from observed diffused states. DIPT models local influence strengths between nodes and leverages an alternating optimization strategy to jointly learn the diffusion mechanism and reconstruct the propagation structure. Extensive experiments on five real-world datasets demonstrate the effectiveness of DIPT in accurately reconstructing propagation trees. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_00646 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Identification of Propagation Trees Memon, Zeeshan Ling, Chen Kong, Ruochen Seshagiri, Vishwanath Zufle, Andreas Zhao, Liang Social and Information Networks Understanding propagation structures in graph diffusion processes, such as epidemic spread or misinformation diffusion, is a fundamental yet challenging problem. While existing methods primarily focus on source localization, they cannot reconstruct the underlying propagation trees i.e., "who infected whom", which are substantial for tracking the propagation pathways and investigate diffusion mechanisms. In this work, we propose Deep Identification of Propagation Trees (DIPT), a probabilistic framework that infers propagation trees from observed diffused states. DIPT models local influence strengths between nodes and leverages an alternating optimization strategy to jointly learn the diffusion mechanism and reconstruct the propagation structure. Extensive experiments on five real-world datasets demonstrate the effectiveness of DIPT in accurately reconstructing propagation trees. |
| title | Deep Identification of Propagation Trees |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2503.00646 |