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..
Editing Partially Observable Networks via Graph Diffusion Models
Authors: Puja Trivedi, Ryan A. Rossi, David Arbour, Tong Yu, Franck Dernoncourt, Sungchul Kim, Nedim Lipka, Namyong Park, Nesreen K. Ahmed, Danai Koutra
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate the ability of graph diffusion models to perform editing tasks, and demonstrate the benefits of using SGDM. Specifically, we seek to answer the following research questions: (RQ1) Is there a benefit to SAMPLING on the expansion and denoising tasks? (RQ2) Is there a benefit to using GLOBAL-CONTEXT in our proposed graph editing tasks? (RQ3) How do different GDDM backbones affect the performance of SGDM? Below, we first describe our evaluation setup. |
| Researcher Affiliation | Collaboration | Puja Trivedi 1 Ryan A. Rossi 2 David Arbour 2 Tong Yu 2 Franck Dernoncourt 2 Sungchul Kim 2 Nedim Lipka 2 Namyong Park 3 Nesreen K. Ahmed 4 Danai Koutra 1 1CSE Dept, University of Michigan, Ann Arbor 2Adobe Research Inc 3Carnegie Mellon University 4Intel AI Research. Correspondence to: Puja Trivedi <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 SGDM: Subgraph-based Diffusion ... Algorithm 2 Editing with SGDM ... Algorithm 3 Large Graph Generation with SGDM |
| Open Source Code | Yes | Our code can be accessed at https://github.com/pujacomputes/sgdm. |
| Open Datasets | Yes | For the editing tasks, we consider 3 large, single networks BA-Shapes, Pol Blogs and CORA (Table 1) and corrupt them to create the incomplete, noisy observed graphs. ... BA-Shapes dataset (Ying et al., 2019) |
| Dataset Splits | No | The paper mentions 'train' and 'test' in the context of model training and evaluation but does not specify a separate 'validation' split or its size/percentage. It also mentions 'standard split' in related work, but not for its own experimental setup. |
| Hardware Specification | Yes | We trained all models using Tesla T4s (16GB GPU Memory, 124GB RAM). |
| Software Dependencies | No | The paper mentions 'Py Torch Geometric (Fey & Lenssen, 2019) and Py Torch' but does not specify their version numbers. It also refers to official code by name (e.g., 'Di GRESS', 'EDGE', 'GDSS') but without associated version numbers for reproducibility. |
| Experiment Setup | Yes | We trained all models using Tesla T4s (16GB GPU Memory, 124GB RAM). To ensure fair comparison across methods and prevent overfitting to a corrupted graph, all models are trained for at most 24 hours or 5000 epochs, which ever came first. ... Hyper-parameters and architectures suggested by each method s authors are used. |