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..
Graph Cross Networks with Vertex Infomax Pooling
Authors: Maosen Li, Siheng Chen, Ya Zhang, Ivor Tsang
NeurIPS 2020 | Venue PDF | 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 EMAIL Siheng Chen B Shanghai Jiao Tong University EMAIL Ya Zhang B Shanghai Jiao Tong University EMAIL Ivor Tsang Australian Artificial Intelligence Institute University of Technology Sydney EMAIL * 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. |