Diffusion-Convolutional Neural Networks
Authors: James Atwood, Don Towsley
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we present several experiments to investigate how well DCNNs perform at node and graph classification tasks. In each case we compare DCNNs to other well-known and effective approaches to the task. Table 1: A comparison of the performance between baseline ℓ1 and ℓ2-regularized logistic regression models, exponential diffusion and Laplacian exponential diffusion kernel models, loopy belief propagation (LBP) on a partially-observed conditional random field (CRF), and a two-hop DCNN on the Cora and Pubmed datasets. |
| Researcher Affiliation | Academia | James Atwood and Don Towsley College of Information and Computer Science University of Massachusetts Amherst, MA, 01003 {jatwood|towsley}@cs.umass.edu |
| Pseudocode | No | No pseudocode or algorithm blocks are provided. |
| Open Source Code | No | The paper states: 'The model was implemented in Python using Lasagne and Theano [3]', but does not provide concrete access (link, explicit statement of release) to the source code for the DCNN model itself. |
| Open Datasets | Yes | The graphs were constructed from the Cora and Pubmed datasets, which each consist of scientific papers (nodes), citations between papers (edges), and subjects (labels). ... The Cora corpus [5]... The Pubmed corpus [5]... We apply DCNNs to a standard set of graph classification datasets that consists of NCI1, NCI109, MUTAG, PCI, and ENZYMES. The NCI1 and NCI109 [7] datasets... MUTAG [8]... PTC [9]... ENZYMES [10]. |
| Dataset Splits | Yes | During each trial, the input graph s nodes are randomly partitioned into training, validation, and test sets, with each set having the same number of nodes. In this experiment, the validation and test set each contain 10% of the nodes, and the amount of training data is varied between 10% and 100% of the remaining nodes. At the beginning of each trial, input graphs are randomly assigned to training, validation, or test, with each set having the same number of graphs. |
| Hardware Specification | No | The paper states 'efficiently implemented on a GPU' and mentions 'NVIDIA through the donation of equipment used to perform experiments', but does not provide specific hardware details such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions 'The model was implemented in Python using Lasagne and Theano [3]', but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | In each of the following experiments, we use the Ada Grad algorithm [2] for gradient ascent with a learning rate of 0.05. All weights are initialized by sampling from a normal distribution with mean zero and variance 0.01. We choose the hyperbolic tangent for the nonlinear differentiable function f and use the multiclass hinge loss between the model predictions and ground truth as the training objective. |