Unsupervised Neighborhood Propagation Kernel Layers for Semi-supervised Node Classification
Authors: Sonny Achten, Francesco Tonin, Panagiotis Patrinos, Johan A.K. Suykens
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate the effectiveness of the proposed framework. Experiments Datasets and Main Setting As datasets, we use four homophilious graphs: Cora, Cite Seer, Pub Med (Sen et al. 2008; Yang, Cohen, and Salakhudinov 2016), and OGBArxiv (Hu et al. 2020), which are citation graphs, as well as two heterophilious graphs: Chameleon and Squirrel (Wikipedia graphs (Rozemberczki, Allen, and Sarkar 2021)). |
| Researcher Affiliation | Academia | Sonny Achten, Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics {sonny.achten, francesco.tonin, panos.patrinos, johan.suykens}@esat.kuleuven.be |
| Pseudocode | Yes | Algorithm 1 Optimization algorithm of GCKM. 1: Initialize {H(1) 0 , H(2) 0 , H(3) 0 } 2: for k 0, 1, . . . , T do 3: Compute K(1) c from aggregated X 4: Compute K(2) c from aggregated H(1) k 5: Update {H(1) k+1, H(2) k+1} Cayley Adam(JGCKM) 6: Compute K(3) from H(2) k+1 7: Update {H(3) k+1} Solve (11) with K(3) |
| Open Source Code | Yes | The reported results can be reproduced using our code on Git Hub1 and the Appendix is available at Ar Xiv2. 1https://github.com/sonnyachten/GCKM |
| Open Datasets | Yes | As datasets, we use four homophilious graphs: Cora, Cite Seer, Pub Med (Sen et al. 2008; Yang, Cohen, and Salakhudinov 2016), and OGBArxiv (Hu et al. 2020), which are citation graphs, as well as two heterophilious graphs: Chameleon and Squirrel (Wikipedia graphs (Rozemberczki, Allen, and Sarkar 2021)). |
| Dataset Splits | Yes | For Cora, Cite Seer, and Pub Med, there are 4 training labels per class, 100 labels for validation, and 1000 labels for testing. For Chameleon and Squirrel, a 0.5%/0.5%/99% train/validation/test-split is used. For OGB-Arxiv, we use a 2.5%/2.5%/95% random split |
| Hardware Specification | No | We also thank the Flemish Supercomputer (VSC). |
| Software Dependencies | No | We therefore employ the Cayley Adam optimizer (Li, Li, and Todorovic 2019) to update H(1), H(2) with H(3) fixed. |
| Experiment Setup | Yes | We used GCN aggregation for the citation networks and sum aggregation for the heterophilious graphs. We used a deep GCKM with only two unsupervised layers, with RBF bandwidth σ2 RBF = mσ2 with m the input dimension and σ2 the variance of the inputs, and clustering obtained by k-means on H(2). |