Graph Cross Networks with Vertex Infomax Pooling
Authors: Maosen Li, Siheng Chen, Ya Zhang, Ivor Tsang
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that the proposed GXN improves the classification accuracy by 2.12% and 1.15% on average for graph classification and vertex classification, respectively. 5 Experimental Results |
| Researcher Affiliation | Collaboration | Maosen Li Shanghai Jiao Tong University maosen_li@sjtu.edu.cn Siheng Chen B Shanghai Jiao Tong University sihengc@sjtu.edu.cn Ya Zhang B Shanghai Jiao Tong University ya_zhang@sjtu.edu.cn Ivor Tsang Australian Artificial Intelligence Institute University of Technology Sydney Ivor.Tsang@uts.edu.au * This work was done while Siheng Chen was working at Mitsubishi Electric Research Laboratories (MERL). |
| Pseudocode | No | The paper describes the methodology in text and uses diagrams (e.g., Figure 3) but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1 The code could be downloaded at https://github.com/limaosen0/GXN |
| Open Datasets | Yes | For graph classification, we use social network datasets: IMDBB, IMDB-M and COLLAB [52], and bioinformatic datasets: D&D [17], PROTEINS [21], and ENZYMES [4]. ... For vertex classification, we use three classical citation networks: Cora, Citeseer and Pubmed [32]. |
| Dataset Splits | Yes | We use the same dataset separation as in [23], perform 10-fold cross-validation, and show the average accuracy for evaluation. For vertex classification, we use three classical citation networks: Cora, Citeseer and Pubmed [32]. We perform both full-supervised and semi-supervised vertex classification; that is, for full-supervised classification, we label all the vertices in training sets for model training, while for semi-supervised, we only label a few vertices (around 7% on average) in training sets. We use the default separations of training/validation/test subsets. |
| Hardware Specification | Yes | We implement GXN with Py Torch 1.1 on one GTX-1080Ti GPU. |
| Software Dependencies | Yes | We implement GXN with Py Torch 1.1 on one GTX-1080Ti GPU. ... We use Adam optimizer [16] |
| Experiment Setup | Yes | For graph classification, we consider three scales, which preserve 50% to 100% vertices from the original scales, respectively. For both input and readout layers, we use 1-layer GCNs; for multiscale feature extraction, we use two GCN layers followed by ReLUs at each scale and feature-crossing layers between any two consecutive scales at any layers. ... In the VIPool, we use a 2-layer MLP and R-layer GCN (R = 1 or 2) as Ew( ) and Pw( ), and use a linear layer as Sw( , ). The hidden dimensions are 48. ... For vertex classification, we use similar architecture as in graph classification, while the hidden feature are 128-dimension. ... In the loss function L, α decays from 2 to 0 during training, ... We use Adam optimizer [16] and the learining rates range from 0.0001 to 0.001 for different datasets. |