# Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning

@article{Jiao2020SubgraphCF, title={Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning}, author={Yizhu Jiao and Yun Xiong and Jiawei Zhang and Yao Zhang and Tianqi Zhang and Yangyong Zhu}, journal={2020 IEEE International Conference on Data Mining (ICDM)}, year={2020}, pages={222-231} }

Graph representation learning has attracted lots of attention recently. Existing graph neural networks fed with the complete graph data are not scalable due to limited computation and memory costs. Thus, it remains a great challenge to capture rich information in large-scale graph data. Besides, these methods mainly focus on supervised learning and highly depend on node label information, which is expensive to obtain in the real world. As to unsupervised network embedding approaches, they…

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## References

SHOWING 1-10 OF 37 REFERENCES

### Graph-Bert: Only Attention is Needed for Learning Graph Representations

- Computer ScienceArXiv
- 2020

This paper introduces a new graph neural network, namely GRAPH-BERT (Graph based BERT), solely based on the attention mechanism without any graph convolution or aggregation operators, which can out-perform the existing GNNs in both the learning effectiveness and efficiency.

### Deep Graph Infomax

- Computer ScienceICLR
- 2019

Deep Graph Infomax (DGI) is presented, a general approach for learning node representations within graph-structured data in an unsupervised manner that is readily applicable to both transductive and inductive learning setups.

### Representation Learning on Graphs: Methods and Applications

- Computer ScienceIEEE Data Eng. Bull.
- 2017

A conceptual review of key advancements in this area of representation learning on graphs, including matrix factorization-based methods, random-walk based algorithms, and graph neural networks are provided.

### Graph Attention Networks

- Computer ScienceICLR
- 2018

We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior…

### Graph Representation Learning via Graphical Mutual Information Maximization

- Computer ScienceWWW
- 2020

An unsupervised learning model trained by maximizing GMI between the input and output of a graph neural encoder is developed, which outperforms state-of-the-art unsuper supervised counterparts, and even sometimes exceeds the performance of supervised ones.

### GraphSAINT: Graph Sampling Based Inductive Learning Method

- Computer ScienceICLR
- 2020

GraphSAINT is proposed, a graph sampling based inductive learning method that improves training efficiency in a fundamentally different way and can decouple the sampling process from the forward and backward propagation of training, and extend GraphSAINT with other graph samplers and GCN variants.

### Inductive Representation Learning on Large Graphs

- Computer ScienceNIPS
- 2017

GraphSAGE is presented, a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data and outperforms strong baselines on three inductive node-classification benchmarks.

### FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling

- Computer ScienceICLR
- 2018

Enhanced with importance sampling, FastGCN not only is efficient for training but also generalizes well for inference, and is orders of magnitude more efficient while predictions remain comparably accurate.

### How Powerful are Graph Neural Networks?

- Computer ScienceICLR
- 2019

This work characterize the discriminative power of popular GNN variants, such as Graph Convolutional Networks and GraphSAGE, and show that they cannot learn to distinguish certain simple graph structures, and develops a simple architecture that is provably the most expressive among the class of GNNs.

### Collective Link Prediction Oriented Network Embedding with Hierarchical Graph Attention

- Computer ScienceCIKM
- 2019

This paper proposes an application oriented network embedding framework, Hierarchical Graph Attention based Network Embedding (HGANE), for collective link prediction over directed aligned networks and introduces a hierarchical graph attention mechanism for the intra-network neighbors and inter-network partners respectively, which resolves the network characteristic differences and the link directivity challenges effectively.