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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.06405 |
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| _version_ | 1866909383178321920 |
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| author | Chen, Chen Qian, Jingya Luo, Hui Li, Yongye Wang, Xiaoyang |
| author_facet | Chen, Chen Qian, Jingya Luo, Hui Li, Yongye Wang, Xiaoyang |
| contents | The k-truss model is one of the most important models in cohesive subgraph analysis. The k-truss decomposition problem is to compute the trussness of each edge in a given graph, and has been extensively studied. However, the conventional k-truss model is difficult to characterize the fine-grained hierarchical structures in networks due to the neglect of high order information. To overcome the limitation, the higher-order truss model is proposed in the literature. However, the previous solutions only consider non-parallel scenarios. To fill the gap, in this paper, we conduct the first research to study the problem of parallel higher-order truss decomposition. Specifically, a parallel framework is first proposed. Moreover, several optimizations are further developed to accelerate the processing. Finally, experiments over 6 real-world networks are conducted to verify the performance of proposed methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06405 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Parallel Higher-order Truss Decomposition Chen, Chen Qian, Jingya Luo, Hui Li, Yongye Wang, Xiaoyang Data Structures and Algorithms The k-truss model is one of the most important models in cohesive subgraph analysis. The k-truss decomposition problem is to compute the trussness of each edge in a given graph, and has been extensively studied. However, the conventional k-truss model is difficult to characterize the fine-grained hierarchical structures in networks due to the neglect of high order information. To overcome the limitation, the higher-order truss model is proposed in the literature. However, the previous solutions only consider non-parallel scenarios. To fill the gap, in this paper, we conduct the first research to study the problem of parallel higher-order truss decomposition. Specifically, a parallel framework is first proposed. Moreover, several optimizations are further developed to accelerate the processing. Finally, experiments over 6 real-world networks are conducted to verify the performance of proposed methods. |
| title | Parallel Higher-order Truss Decomposition |
| topic | Data Structures and Algorithms |
| url | https://arxiv.org/abs/2411.06405 |