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 | Conference PDF | Archive PDF | Plain Text | 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 <pujat@umich.edu>. |
| 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. |