VQGraph: A New Method to Encode and Learn from Graphs

Introduction

Graphs are complex and diverse data structures that can represent various kinds of information, such as social networks, molecular structures, or knowledge bases. However, learning from graphs is not easy, as it requires models to capture the rich and subtle patterns of the graph structure. Graph neural networks (GNNs) and multilayer perceptrons (MLPs) are two popular models for graph learning, but they have their own drawbacks. GNNs often suffer from two major challenges: over-smoothing and over-fitting. Over-smoothing occurs when the node representations become indistinguishable after multiple layers of message passing, leading to a loss of node-specific information. Over-fitting happens when the model memorizes the training data and fails to generalize to unseen graphs, especially when the graph distribution is diverse or noisy.

To address these challenges, a team of researchers from the Peking University, Ant Group, and Stanford University have developed a novel model called VQGraph. VQGraph stands for Graph Vector-Quantization, which is a technique that compresses the node representations into discrete codes that can be efficiently processed by multilayer perceptrons (MLPs). VQGraph aims to bridge the gap between GNNs and MLPs and leverage the advantages of both models.

What is VQGraph? 

VQGraph is a graph vector-quantization method that aims to bridge the gap between GNNs and MLPs. It does this by introducing a novel graph vector-quantization method that can effectively encode the structural information of graphs into fixed-length vectors.

Key Features of VQGraph

VQGraph has several key features that make it an effective and efficient model for graph learning. Some of these features are:

• Code compression: VQGraph transforms the node features into discrete codes that are compact and robust. These codes can capture the essence of the graph and enable cross-graph transfer learning.
• Self-supervision: VQGraph trains the codebook and the vector quantizer with contrastive learning, which is a self-supervised technique that maximizes the agreement between similar inputs and minimizes the confusion between dissimilar inputs. VQGraph applies contrastive learning at both the node level and the graph level to boost the discriminative power of the codes.
• MLP adaptation: VQGraph uses an MLP as the decoder to predict the graph-level output from the codes. The MLP is a simple but expressive model that can learn complex nonlinear functions from high-dimensional inputs. The MLP can also be easily customized to different tasks and domains by changing its structure or activation functions.
• Graph encoding: VQGraph can effectively encode the structural information of graphs into fixed-length vectors. It can improve the performance of GNNs on graph classification tasks by reducing over-smoothing and over-fitting. It can also improve the performance of MLPs on graph classification tasks by enhancing generalization and expressiveness.

Capabilities/Use Case of VQGraph 

VQGraph is a versatile model that can be applied to various graph learning tasks and domains. Here are some examples of how VQGraph can be used:

• Drug discovery: VQGraph can classify molecules or proteins based on their properties or functions, which can help in finding new drugs or understanding how existing drugs work.
• Social network analysis: VQGraph can analyze social networks based on their structure or dynamics, which can help in identifying influential nodes, communities, or patterns in the network.
• Knowledge graph completion: VQGraph can complete missing or noisy information in knowledge graphs based on their semantics or relations, which can help in enhancing the quality and coverage of the knowledge graph.

How does VQGraph Work?

VQGraph works by using a variant of VQ-VAE, which is a technique that compresses the input data into discrete codes that can be efficiently processed by MLPs. VQGraph first learns a codebook of prototype vectors that represent different substructures in the graphs. Then, for each graph, it vectorizes and quantizes its adjacency matrix using the codebook to obtain a fixed-length vector representation of the graph. This allows VQGraph to create a new powerful graph representation space that is structure-aware and expressive. The codebook size can be flexibly controlled to fit the graphs of different scales and enrich the expressiveness of the graph representation space. As shown in Figure below, VQGraph uses this process to encode the graphs into vectors.

source - https://github.com/YangLing0818/VQGraph

VQGraph also uses a tailored distillation objective to effectively bridge GNNs and MLPs. The method maximizes the consistency between the soft token assignments of GNNs and MLPs, which are obtained by comparing the encoder output and all token embeddings. This enables the MLP to learn both the local structural knowledge from GNNs and the global structural context of graph data. This way, VQGraph can improve the performance of both GNNs and MLPs on graph learning tasks.

Performance Evaluation

Extensive experiments and analyses demonstrate the strong performance of VQGraph, where it achieves new state-of-the-art performance on GNN-MLP distillation in both transductive and inductive settings across seven graph datasets. VQGraph infers faster than GNNs by 828x and also achieves accuracy improvement over GNNs and stand-alone MLPs by 3.90% and 28.05% on average, respectively. 

source - https://arxiv.org/pdf/2308.02117.pdf

The model was evaluated on five widely used public benchmark datasets and two large OGB datasets. VQGraph outperforms all baselines, including teacher GNN models, across all datasets. Specifically, VQGraph improves performance by an average of 3.90% compared to its teacher GNN, highlighting its ability to capture superior structural information without relying on explicit graph structure input. Further model analysis of VQGraph is presented in Table above.

Above table shows the node classification results of VQGraph and other models (including two state-of-the-art GNN-MLP distillation methods: GLNN and NOSMOG) under the standard setting, where the models are trained and tested on the same graph. The results show the accuracy of each model, which means how well they can predict the correct labels of the nodes. Higher accuracy means better performance.

For further information on the experiments conducted, additional visualizations and statistical analyses, as well as ablation studies, it is recommended to read the research paper. This will provide a more detailed and comprehensive understanding of the subject.

How to access and use this model?

VQGraph is available on GitHub Website. The repository contains instructions on how to install and use the model locally. To access and use the model, you can visit the GitHub repository, clone or download the code, and follow the instructions provided in the repository to install and use the model on your local machine.  It is recommended to contact the authors of the VQGraph model for more information on its licensing and usage.

Future Work

some possible directions for future work, which are:

• Extending to more complex scenarios: The current version of VQGraph assumes that the graphs are undirected and unweighted, and that the node features are binary or categorical. However, in many real-world applications, the graphs can be directed, weighted, or attributed with continuous or high-dimensional features. Therefore, a possible future work is to extend VQGraph to handle these more complex scenarios and test its performance on more diverse and challenging datasets.
• Exploring other codebook learning methods: The current version of VQGraph uses a variant of VQ-VAE to learn the codebook of prototype vectors that represent different substructures in the graphs. However, there may be other methods that can learn a better codebook that can capture more fine-grained or hierarchical information of the graph structure. Therefore, a possible future work is to explore other codebook learning methods, such as discrete autoencoders, generative adversarial networks, or reinforcement learning, and compare their results with VQGraph.
• Investigating other applications and tasks: The current version of VQGraph focuses on graph classification tasks, where the goal is to predict a label for each graph based on its structure and features. However, there may be other applications and tasks that can benefit from VQGraph, such as graph generation, graph matching, graph clustering, or graph embedding. Therefore, a possible future work is to investigate how VQGraph can be applied or adapted to these other applications and tasks and evaluate its performance and usefulness.

Conclusion

VQGraph is a promising new method for bridging the gap between GNNs and MLPs by introducing a novel graph vector-quantization method. The method has been shown to be effective in improving the performance of both GNNs and MLPs on graph classification tasks and is available for use by researchers and practitioners alike.

Source
research paper - https://arxiv.org/abs/2308.02117
research document - https://arxiv.org/pdf/2308.02117.pdf
GitHub Repo - https://github.com/YangLing0818/VQGraph