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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2204.07703 |
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| _version_ | 1866929209028378624 |
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| author | Zhou, Min Li, Bisheng Yang, Menglin Pan, Lujia |
| author_facet | Zhou, Min Li, Bisheng Yang, Menglin Pan, Lujia |
| contents | Link prediction is a key problem for network-structured data, attracting considerable research efforts owing to its diverse applications. The current link prediction methods focus on general networks and are overly dependent on either the closed triangular structure of networks or node attributes. Their performance on sparse or highly hierarchical networks has not been well studied. On the other hand, the available tree-like benchmark datasets are either simulated, with limited node information, or small in scale. To bridge this gap, we present a new benchmark dataset TeleGraph, a highly sparse and hierarchical telecommunication network associated with rich node attributes, for assessing and fostering the link inference techniques. Our empirical results suggest that most of the algorithms fail to produce a satisfactory performance on a nearly tree-like dataset, which calls for special attention when designing or deploying the link prediction algorithm in practice. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2204_07703 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | TeleGraph: A Benchmark Dataset for Hierarchical Link Prediction Zhou, Min Li, Bisheng Yang, Menglin Pan, Lujia Social and Information Networks Artificial Intelligence Link prediction is a key problem for network-structured data, attracting considerable research efforts owing to its diverse applications. The current link prediction methods focus on general networks and are overly dependent on either the closed triangular structure of networks or node attributes. Their performance on sparse or highly hierarchical networks has not been well studied. On the other hand, the available tree-like benchmark datasets are either simulated, with limited node information, or small in scale. To bridge this gap, we present a new benchmark dataset TeleGraph, a highly sparse and hierarchical telecommunication network associated with rich node attributes, for assessing and fostering the link inference techniques. Our empirical results suggest that most of the algorithms fail to produce a satisfactory performance on a nearly tree-like dataset, which calls for special attention when designing or deploying the link prediction algorithm in practice. |
| title | TeleGraph: A Benchmark Dataset for Hierarchical Link Prediction |
| topic | Social and Information Networks Artificial Intelligence |
| url | https://arxiv.org/abs/2204.07703 |