InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization
Authors: Fan-Yun Sun, Jordan Hoffman, Vikas Verma, Jian Tang
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on the tasks of graph classification and molecular property prediction show that Info Graph is superior to state-of-the-art baselines and Info Graph* can achieve performance competitive with state-of-the-art semi-supervised models. |
| Researcher Affiliation | Academia | 1National Taiwan University, 2Mila-Quebec Institute for Learning Algorithms, Canada 3Aalto University, Finland 4Harvard University, USA 5HEC Montreal, Canada 6CIFAR AI Research Chair |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. The methods are described using mathematical formulations and textual explanations. |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. It only mentions using third-party libraries like Pytorch and Pytorch Geometric. |
| Open Datasets | Yes | For graph classification, we conduct experiments on 6 well-known benchmark datasets: MUTAG, PTC, REDDIT-BINARY, REDDIT-MULTI-5K, IMDB-BINARY, and IMDB-MULTI (Yanardag & Vishwanathan (2015)). For semi-supervised learning tasks, we use the publicly available QM9 dataset Ramakrishnan et al. (2014). |
| Dataset Splits | Yes | We randomly chose 5000 samples as labeled samples for training and another 10000 as validation samples, 10000 samples for testing, and use the rest as unlabeled training samples. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. It only mentions software libraries used. |
| Software Dependencies | No | The paper states 'We use Pytorch Paszke et al. (2017) and the Pytorch Geometric Fey & Lenssen (2019) libraries for all our experiments.' However, it does not provide specific version numbers for these software dependencies, only the citations. |
| Experiment Setup | Yes | For the unsupervised experiments, we use the Graph Isomorphism Network (GIN) Xu et al. (2018a). GNN layers are chosen from {4, 8, 12}. Initial learning rate is chosen from the set {10 2, 10 3, 10 4}. The number of epochs are chosen from {10, 20, 100}. The batch size is set to 128. For the semi-supervised experiments, the number of set2set computations is set to 3. Model were trained with an initial learning rate 0.001 for 500 epochs with a batch size 20. For the supervised case, the weight decay is chosen from {0, 10 3, 10 4}. For Info Graph and Info Graph*, λ is chosen from {10 3, 10 4, 10 5}. |