Interpretable Neural Subgraph Matching for Graph Retrieval
Authors: Indradyumna Roy, Venkata Sai Baba Reddy Velugoti, Soumen Chakrabarti, Abir De8115-8123
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on diverse datasets show that ISONET outperforms recent graph retrieval formulations and systems. |
| Researcher Affiliation | Academia | Indradyumna Roy, Venkata Sai Baba Reddy Velugoti, Soumen Chakrabarti, Abir De Indian Institute of Technology Bombay {indraroy15, abir, soumen}@cse.iitb.ac.in, saibaba.rapur@gmail.com |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block was found. |
| Open Source Code | Yes | Our code is available at https://github.com/Indradyumna/ISONET. |
| Open Datasets | Yes | We experiment with six real world datasets: PTC-FR, PTC-FM, PTC-MM, PTC-MR, MUTAG and AIDS (Morris et al. 2020). |
| Dataset Splits | Yes | Given a set of query graphs Q and a set of corpus graphs C, we split Q into 60% training, 15% validation and 25% test folds. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory, or specific cloud instances) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | The paper describes the neural network components but does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | No | The paper mentions some hyperparameter concepts like 'margin hyperparameter γ > 0', 'temperature τ > 0', and the use of MLPs and GRUs, but does not provide concrete numerical values for these parameters or detailed training configurations (e.g., specific learning rates, batch sizes, number of epochs). |