Revisiting Graph Analytics Benchmark
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866915705638617088 |
|---|---|
| author | Meng, Lingkai Shao, Yu Yuan, Long Lai, Longbin Cheng, Peng Li, Xue Yu, Wenyuan Zhang, Wenjie Lin, Xuemin Zhou, Jingren |
| author_facet | Meng, Lingkai Shao, Yu Yuan, Long Lai, Longbin Cheng, Peng Li, Xue Yu, Wenyuan Zhang, Wenjie Lin, Xuemin Zhou, Jingren |
| contents | The rise of graph analytics platforms has led to the development of various benchmarks for evaluating and comparing platform performance. However, existing benchmarks often fall short of fully assessing performance due to limitations in core algorithm selection, data generation processes (and the corresponding synthetic datasets), as well as the neglect of API usability evaluation. To address these shortcomings, we propose a novel graph analytics benchmark. First, we select eight core algorithms by extensively reviewing both academic and industrial settings. Second, we design an efficient and flexible data generator and produce eight new synthetic datasets as the default datasets for our benchmark. Lastly, we introduce a multi-level large language model (LLM)-based framework for API usability evaluation-the first of its kind in graph analytics benchmarks. We conduct comprehensive experimental evaluations on existing platforms (GraphX, PowerGraph, Flash, Grape, Pregel+, Ligra and G-thinker). The experimental results demonstrate the superiority of our proposed benchmark. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_21811 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Revisiting Graph Analytics Benchmark Meng, Lingkai Shao, Yu Yuan, Long Lai, Longbin Cheng, Peng Li, Xue Yu, Wenyuan Zhang, Wenjie Lin, Xuemin Zhou, Jingren Databases Graphics The rise of graph analytics platforms has led to the development of various benchmarks for evaluating and comparing platform performance. However, existing benchmarks often fall short of fully assessing performance due to limitations in core algorithm selection, data generation processes (and the corresponding synthetic datasets), as well as the neglect of API usability evaluation. To address these shortcomings, we propose a novel graph analytics benchmark. First, we select eight core algorithms by extensively reviewing both academic and industrial settings. Second, we design an efficient and flexible data generator and produce eight new synthetic datasets as the default datasets for our benchmark. Lastly, we introduce a multi-level large language model (LLM)-based framework for API usability evaluation-the first of its kind in graph analytics benchmarks. We conduct comprehensive experimental evaluations on existing platforms (GraphX, PowerGraph, Flash, Grape, Pregel+, Ligra and G-thinker). The experimental results demonstrate the superiority of our proposed benchmark. |
| title | Revisiting Graph Analytics Benchmark |
| topic | Databases Graphics |
| url | https://arxiv.org/abs/2506.21811 |