Learning Graph Representations with Embedding Propagation
Authors: Alberto Garcia Duran, Mathias Niepert
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate EP with the following six commonly used benchmark data sets. ... Using the node classification data sets, we compare the performance of EP-B to the state of the art approaches... |
| Researcher Affiliation | Industry | Alberto García-Durán NEC Labs Europe Heidelberg, Germany alberto.duran@neclab.eu Mathias Niepert NEC Labs Europe Heidelberg, Germany mathias.niepert@neclab.eu |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-sourcing of the authors' own implementation code. |
| Open Datasets | Yes | We evaluate EP with the following six commonly used benchmark data sets. Blog Catalog [46]... PPI [6]... POS [28]... Cora, Citeseer and Pubmed [40]... |
| Dataset Splits | Yes | For the graphs with attributes (Cora, Citeseer, Pubmed) we follow the same experimental procedure as in previous work [45]. We sample 20 nodes uniformly at random for each class as training data, 1000 nodes as test data, and a different 1000 nodes as validation data. |
| Hardware Specification | Yes | All experiments were run on commodity hardware with 128GB RAM, a single 2.8 GHz CPU, and a Titan X GPU. |
| Software Dependencies | No | EP was implemented with the Theano [4] wrapper Keras [9]. We used the logistic regression classifier from Lib Linear [10]. Specific version numbers for Theano, Keras, or Lib Linear are not provided. |
| Experiment Setup | Yes | The dimension of the embeddings is always fixed to 128. For EP-B, we chose the margin γ in (3) from the set of values [1, 5, 10, 20] on validation data. For EP-B we used ADAM [17] to learn the parameters in a mini-batch setting with a learning rate of 0.001. A single learning epoch iterates through all nodes of the input graph and we fixed the number of epochs to 200 and the mini-batch size to 64. |