构建首个去中心化社交网络图数据集，助力可信机器学习研究
Fedivertex: a Graph Dataset based on Decentralized Social Networks for Trustworthy Machine Learning
- 从联邦宇宙7个平台采集14周动态图数据，每周更新一次
- 包含182张图，支持去中心化算法基准测试与链路删除任务
- 适合研究去中心化学习、社交网络演化及可信机器学习的学者
去中心化机器学习通过客户端本地存储数据并协作训练模型，提升可扩展性和数据控制力。但其学习动态受通信拓扑影响，需真实图数据集进行基准测试。现有数据多来自盈利社交平台，且为单次快照，受平台算法干扰。我们提出Fedivertex，一个涵盖7个去中心化社交网络（如Mastodon、Misskey、Lemmy）的新型图数据集，历时14周每周采集，共生成182张图。数据集已开源，并配套提供Python工具包。我们展示了其在多个任务中的应用，包括一项新提出的去联邦化任务，模拟实际网络中链接被移除的过程。
原文摘要 · Abstract (English)
Decentralized machine learning - where each client keeps its own data locally and uses its own computational resources to collaboratively train a model by exchanging peer-to-peer messages - is increasingly popular, as it enables better scalability and control over the data. A major challenge in this setting is that learning dynamics depend on the topology of the communication graph, which motivates the use of real graph datasets for benchmarking decentralized algorithms. Unfortunately, existing graph datasets are largely limited to for-profit social networks crawled at a fixed point in time and often collected at the user scale, where links are heavily influenced by the platform and its recommendation algorithms. The Fediverse, which includes several free and open-source decentralized social media platforms such as Mastodon, Misskey, and Lemmy, offers an interesting real-world alternative. We introduce Fedivertex, a new dataset of 182 graphs, covering seven social networks from the Fediverse, crawled weekly over 14 weeks. We release the dataset along with a Python package to facilitate its use, and illustrate its utility on several tasks, including a new defederation task, which captures a process of link deletion observed on these networks.
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