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Author: rllm-project

README.md

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 # SJTUTables: A Benchmark for Relational Table Learning
 
-SJTUTables is a benchmark dataset collection designed for Relational Table Learning (RTL). Released as part of the [rLLM project](https://github.com/rllm-team/rllm), it includes three enhanced relational table datasets: TML1M, TLF2K, and TACM12K. Derived from well-known classical datasets, each dataset is paired with a standard classification task. Their simple, easy-to-use, and well-organized structure makes them an ideal choice for quickly evaluating and developing RTL methods.
+SJTUTables is a benchmark dataset collection designed for Relational Table Learning (RTL). Released as part of the [rLLM project](https://github.com/rllm-project/rllm), it includes three enhanced relational table datasets: TML1M, TLF2K, and TACM12K. Derived from well-known classical datasets, each dataset is paired with a standard classification task. Their simple, easy-to-use, and well-organized structure makes them an ideal choice for quickly evaluating and developing RTL methods.
 - TML1M is derived from the classical MovieLens1M dataset and contains three relational tables related to movie recommendation: users, movies, and ratings.
 - TLF2K is derived from the classical LastFM2K dataset and includes three relational tables related to music preferences: artists, user-artist interactions, and user-friend relationships.
 - TACM12K is derived from the ACM heterogeneous graph dataset and contains four relational tables for academic publications: papers, authors, writing relationships, and citation relationships.