Best of Both Worlds: Advantages of Hybrid Graph Sequence Models
Ali Behrouz, Ali Parviz, Mahdi Karami, Clayton Sanford, Bryan Perozzi, Vahab Mirrokni
2024-11-26
Summary
This paper introduces a new framework called Graph Sequence Model (GSM) and its enhanced version GSM++, which help improve how we use sequence models for analyzing graph data, combining the strengths of different model types.
What's the problem?
Many modern sequence models, like Transformers and RNNs, are great for processing data but struggle when applied to graph-structured data. Traditional methods, like Message Passing Neural Networks (MPNNs), often focus too much on local information and miss the bigger picture. There hasn't been a clear understanding of what makes a good model for graphs, leading to challenges in performance and efficiency.
What's the solution?
The authors propose the GSM framework, which consists of three main steps: Tokenization (turning the graph into sequences), Local Encoding (capturing information around each node), and Global Encoding (using a scalable model to understand long-range relationships). They also introduce GSM++, a hybrid model that uses Hierarchical Affinity Clustering to create better sequences and combines different modeling techniques to enhance performance. Their experiments show that this approach improves accuracy and efficiency compared to existing methods.
Why it matters?
This research is important because it provides a better way to analyze complex graph data by leveraging the strengths of various sequence models. By improving how models work with graphs, GSM and GSM++ can lead to advancements in fields like social network analysis, biology, and any area where relationships between data points are crucial.
Abstract
Modern sequence models (e.g., Transformers, linear RNNs, etc.) emerged as dominant backbones of recent deep learning frameworks, mainly due to their efficiency, representational power, and/or ability to capture long-range dependencies. Adopting these sequence models for graph-structured data has recently gained popularity as the alternative to Message Passing Neural Networks (MPNNs). There is, however, a lack of a common foundation about what constitutes a good graph sequence model, and a mathematical description of the benefits and deficiencies in adopting different sequence models for learning on graphs. To this end, we first present Graph Sequence Model (GSM), a unifying framework for adopting sequence models for graphs, consisting of three main steps: (1) Tokenization, which translates the graph into a set of sequences; (2) Local Encoding, which encodes local neighborhoods around each node; and (3) Global Encoding, which employs a scalable sequence model to capture long-range dependencies within the sequences. This framework allows us to understand, evaluate, and compare the power of different sequence model backbones in graph tasks. Our theoretical evaluations of the representation power of Transformers and modern recurrent models through the lens of global and local graph tasks show that there are both negative and positive sides for both types of models. Building on this observation, we present GSM++, a fast hybrid model that uses the Hierarchical Affinity Clustering (HAC) algorithm to tokenize the graph into hierarchical sequences, and then employs a hybrid architecture of Transformer to encode these sequences. Our theoretical and experimental results support the design of GSM++, showing that GSM++ outperforms baselines in most benchmark evaluations.