Hierarchical Representation Learning for Bipartite Graphs
Authors: Chong Li, Kunyang Jia, Dan Shen, C.J. Richard Shi, Hongxia Yang
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The provided text only contains the title and conference information. While papers at the International Joint Conference on Artiļ¬cial Intelligence (IJCAI) are typically experimental, the full content of the paper is not available to identify explicit indicators such as evaluation on datasets, comparisons to baselines, or performance tables. |
| Researcher Affiliation | Academia | The provided text only contains the title and conference information. Author affiliations are not present, so it is impossible to classify them based on the given information. Assuming academia given the conference type. |
| Pseudocode | No | The provided text only contains the title and conference information. There is no content to check for pseudocode or algorithm blocks. |
| Open Source Code | No | The provided text only contains the title and conference information. There is no mention of open-source code availability. |
| Open Datasets | No | The provided text only contains the title and conference information. There is no content describing dataset usage or availability. |
| Dataset Splits | No | The provided text only contains the title and conference information. There is no content describing training, validation, or test splits. |
| Hardware Specification | No | The provided text only contains the title and conference information. There is no content describing hardware specifications. |
| Software Dependencies | No | The provided text only contains the title and conference information. There is no content describing software dependencies. |
| Experiment Setup | No | The provided text only contains the title and conference information. There is no content describing the experimental setup or hyperparameters. |