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..
Mitigating Emergent Robustness Degradation while Scaling Graph Learning
Authors: Xiangchi Yuan, Chunhui Zhang, Yijun Tian, Yanfang Ye, Chuxu Zhang
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments have been conducted to demonstrate the anti-degraded robustness and scalability of our method, as compared to popular graph adversarial learning methods, under diverse attack intensities and various datasets of different sizes. |
| Researcher Affiliation | Academia | 1Brandeis University, EMAIL 2Dartmouth College, {chunhui.zhang.gr}@dartmouth.edu 3University of Notre Dame, EMAIL |
| Pseudocode | No | The paper describes the method using figures and text descriptions, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code can be accessed through https://github.com/chunhuizng/emergent-degradation. |
| Open Datasets | Yes | We evaluate the robustness and scalability of our proposed DRAGON framework using the Graph Robustness Benchmark (GRB) dataset (Zheng et al., 2021), which includes graphs of varying scales, such as grb-cora (small-scale), grb-citeseer (small-scale), grb-flickr (medium-scale), grb-reddit (large-scale), and grb-aminer (large-scale). |
| Dataset Splits | Yes | Additionally, we adhere to the GRB benchmark s data splitting protocol, with 60% of the graph data as the training set, 10% as the validation set, and 30% as the test set for each benchmark dataset. |
| Hardware Specification | Yes | All experiments are performed on an NVIDIA V100 GPU with 32 GB of memory. |
| Software Dependencies | No | The paper refers to using GCN as a surrogate model and adhering to GRB benchmark configurations for baselines, but it does not specify software dependencies with version numbers (e.g., Python, PyTorch/TensorFlow versions, or specific library versions). |
| Experiment Setup | Yes | The hyperparameters of DPMo E are given in Table 5, including hyperparameters of adversarial training listed in Table 3 Specifically, for Cora, we set the total number of experts to N = 10 and the number of activated experts to k = 2. For other datasets, by default, we set the total number of experts to N = 4 and the number of activated experts to k = 1. Additionally, hyperparameters of DMGAN are shown in Table 10. The mask rate is 0.7, the walks per node is 1, and the walk length is 3. |