Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
Authors: Aleksandar Bojchevski, Stephan Günnemann
ICLR 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real world networks demonstrate the high performance of our approach, outperforming state-of-the-art network embedding methods on several different tasks. |
| Researcher Affiliation | Academia | Aleksandar Bojchevski, Stephan G unnemann Technical University of Munich, Germany {a.bojchevski,guennemann}@in.tum.de |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. It describes the method using mathematical formulations and textual descriptions. |
| Open Source Code | Yes | We provide all datasets, the source code of G2G, and further supplementary material (https://www.kdd.in.tum.de/g2g). |
| Open Datasets | Yes | We use several attributed graph datasets. Cora (Mc Callum et al., 2000)... We provide all datasets, the source code of G2G, and further supplementary material (https://www.kdd.in.tum.de/g2g). |
| Dataset Splits | Yes | To evaluate the performance we hide a set of edges/non-edges from the original graph and train on the resulting graph. Similarly to Kipf & Welling (2016b) and Wang et al. (2016) we create a validation/test set that contains 5%/10% randomly selected edges respectively and equal number of randomly selected non-edges.We used the validation set for hyper-parameter tuning and early stopping and the test set only to report the performance. |
| Hardware Specification | Yes | In fact, for graphs beyond 15K nodes we had to revert to slow training on the CPU since the data did not fit on the GPU memory (12GB). |
| Software Dependencies | No | The paper mentions using Adam for optimization and rectifier units/exponential linear units as activation functions. However, it does not specify software versions for programming languages, libraries, or frameworks (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | The parameters are optimized using Adam (Kingma & Ba, 2014) with a fixed learning rate of 0.001. ... As a sensible default we recommend an encoder with a single hidden layer of size s1 = 512. ... small number of epochs T needed for convergence (T 2000 for all shown experiments, see e.g. Fig. 3(b)). |