Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Dynamic Rescaling for Training GNNs
Authors: Nimrah Mustafa, Rebekka Burkholz
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We primarily study the effect of training GAT in a balanced state based on the relative gradients criterion (see Eq.(4)), by dynamic rescaling on five real-world heterophilic benchmark datasets [32]. We explore our conceptual ideas empirically and find promising directions to utilize dynamic rescaling for more practical benefits, by training in balance or controlling order of learning among network layers. |
| Researcher Affiliation | Academia | Nimrah Mustafa CISPA 66123 Saarbrücken, Germany EMAIL Rebekka Burkholz CISPA 66123 Saarbrücken, Germany EMAIL |
| Pseudocode | No | The paper describes a procedure for balancing and provides equations (Eq. 6 and 7) but does not present it in a structured pseudocode or algorithm block. |
| Open Source Code | Yes | Our experimental code is available at https://github.com/RelationalML/Dynamic_Rescaling_GAT. |
| Open Datasets | Yes | We primarily study the effect of training GAT in a balanced state based on the relative gradients criterion (see Eq.(4)), by dynamic rescaling on five real-world heterophilic benchmark datasets [32]. |
| Dataset Splits | Yes | Given the input graph G with a .75/.25/.25 train/validation/test split, we train a L = k layer GAT network with the same architecture as Mk but initialized with a looks-linear orthogonal structure which ensures that the network must learn the non-linear transformations of the target network. |
| Hardware Specification | Yes | Experiments were run on an NVIDIA RTX A6000 GPU with 50GB RAM. |
| Software Dependencies | No | The paper mentions 'Adam optimizer' and 'looks-linear orthogonal structure' but does not specify version numbers for any software dependencies or libraries used. |
| Experiment Setup | Yes | All experiments use the Adam optimizer and networks are randomly initialized with looks-linear orthogonal structure [36, 1] unless specified otherwise. [...] A maximum of 10 iterations for the rebalancing procedure outlined in Eq. (6) and (7) were used. [...] The best learning rate from {0.01, 0.001, 0.005}. |