Scalable Graph Neural Networks via Bidirectional Propagation
Authors: Ming Chen, Zhewei Wei, Bolin Ding, Yaliang Li, Ye Yuan, Xiaoyong Du, Ji-Rong Wen
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An extensive empirical study demonstrates that GBP achieves state-of-the-art performance with significantly less training/testing time. Most notably, GBP can deliver superior performance on a graph with over 60 million nodes and 1.8 billion edges in less than half an hour on a single machine. |
| Researcher Affiliation | Collaboration | Ming Chen Renmin University of China chennnming@ruc.edu.cn Zhewei Wei Renmin University of China zhewei@ruc.edu.cn Bolin Ding Alibaba Group bolin.ding@alibaba-inc.com Yaliang Li Alibaba Group yaliang.li@alibaba-inc.com Ye Yuan Beijing Institute of Technology yuan-ye@bit.edu.cn Xiaoyong Du Renmin University of China duyong@ruc.edu.cn Ji-Rong Wen Renmin University of China jrwen@ruc.edu.cn |
| Pseudocode | Yes | Algorithm 1: Bidirectional Propagation Algorithm Input: Graph G, level L, training set Vt, weight coefficients wℓ, convolutional coefficient r, threshold rmax, number of walks per node nr, feature matrix Xn F Output: Embedding matrix Pn F 1 S(ℓ) Sparse (0n n) for ℓ= 0, . . . , L; 2 for each node s Vt do 3 Generate nr random walks from s, each of length L; 4 if The j-th random walk visits node u at the ℓ-th step, ℓ= 0, . . . , L, j = 1, . . . , nr then 5 S(ℓ)(s, u) += 1 nr ; 6 Q(ℓ), R(ℓ) Sparse (0n F ) for ℓ= 1, . . . , L; 7 Q(0) 0n F and R(0) Column Normalized (D r X); 8 for ℓfrom 0 to L 1 do 9 for each u V and k {0, . . . , F 1} with R(ℓ)(u, k) > rmax do 10 for each v N(u) do 11 R(ℓ+1)(v, k) += R(ℓ)(u,k) 12 Q(ℓ)(u, k) R(ℓ)(u, k) and R(ℓ)(u, k) 0; 13 Q(L) R(L); 14 ˆP PL ℓ=0 wℓ Dr Q(ℓ) + Pℓ t=0 S(ℓ t)R(t) ; 15 return Embedding matrix ˆPn F ; |
| Open Source Code | No | The paper does not explicitly state that the source code for the described methodology (GBP) is made publicly available, nor does it provide a direct link to a repository for its implementation. It only mentions using the officially released code of baselines. |
| Open Datasets | Yes | We use seven open graph datasets with different size: three citation networks Cora, Citeser and Pubmed [25], a Protein-Protein interaction network PPI [11], a customer interaction network Yelp [37], a co-purchasing networks Amazon [8] and a large social network Friendster [34]. Table 2 summarizes the statistics of the datasets. |
| Dataset Splits | Yes | Following [15], we apply the standard fixed training/validation/testing split with 20 nodes per class for training, 500 nodes for validation and 1,000 nodes for testing. For each method, we set the number of hidden layers to 2 and take the mean accuracy with the standard deviation after ten runs. ... We use fixed-partition splits for each dataset, following [37, 8] (see the supplementary materials for further details). ... Among the labeled nodes, we use 50,000 nodes (100 from each class) for training, 15,982 for validation, and 25,000 for testing. |
| Hardware Specification | Yes | All the experiments are conducted on a machine with an NVIDIA TITAN V GPU (12GB memory), Intel Xeon CPU (2.20 GHz), and 256GB of RAM. |
| Software Dependencies | No | The paper states, 'We implement GBP in PyTorch and C++,' but does not provide specific version numbers for PyTorch, C++, or any other software dependencies. |
| Experiment Setup | Yes | We set L = 4 across all datasets. Table 3 summaries other hyper-parameters of GBP on different datasets. We use Adam optimizer to train our model, with a maximum of 1000 epochs and a learning rate of 0.01. For a fair comparison, we use the officially released code of each baseline (see the supplementary materials for URL and commit numbers) and perform a grid search to tune hyperparameters for models. |