New AI Research Presents EXPHORMER: A Framework for Scaling Graph Transformers While Reducing Costs

Graph transformers are a type of machine learning algorithm that operates on data structured in graphs. Graphs are mathematical structures composed of nodes and edges, where the nodes represent entities and the edges represent the relationships between these entities.

Graph transformers are used in a variety of applications, including natural language processing, social network analysis, and computer vision. They are typically used for node classification, link prediction, and graph clustering tasks.

A popular type of graph transformer is the Graph Convolutional Network (GCN), which applies convolutional filters to a graph to extract features from nodes and edges. Other types of graph transformers include graph attention networks (GATs), graph isomorphism networks (GINs), and graph neural networks (GNNs).

Graph transformers have shown great promise in machine learning, especially for graph-structured data tasks.

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Graph transformers have shown promise in various learning and graph representation tasks. However, scaling them to larger graphics while maintaining competitive accuracy with messaging networks remains challenging. To solve this problem, a new framework called EXPHORMER has been introduced by a group of researchers from the University of British Columbia, Google Research and the Alberta Machine Intelligence Institute. This framework uses a sparse attention mechanism based on virtual global nodes and expansion graphs, which possess desirable mathematical characteristics such as spectral expansion, sparseness, and pseudo-randomness. As a result, EXPHORMER enables the construction of powerful and scalable graph transformers with linear complexity at the graph size while also providing the theoretical properties of the resulting models. The integration of EXPHORMER into GraphGPS produces models with competitive empirical results on various graph datasets, including three peak datasets. Additionally, EXPHORMER can handle larger graphs than previous graph transformer architectures.

Exphormer is a method that applies an expander-based sparse attention mechanism to graph transformers (GTs). It constructs an interaction graph using three main components: expansion graph attention, global attention, and local neighborhood attention. The attention of the Expander graph allows the propagation of information between nodes without connecting all pairs of nodes. Global attention adds virtual nodes to create a global “storage sink” and provides universal approximation functions for full processors. Local neighborhood attention models local interactions to obtain connectivity information.

Their empirical study evaluated the Exphormer method on graph and node prediction tasks. The team found that Exphormer, combined with message-passing neural networks (MPNNs) in the GraphGPS framework, achieved state-of-the-art results on several benchmark datasets. Although it had fewer parameters, it overcame all sparse attention mechanisms and remained competitive with dense transformers.

The main contributions of the team consist in proposing sparse attention mechanisms with linear computational costs in number of nodes and edges, by introducing Exphormer, which combines two techniques to create sparse overlay graphs and by introducing expansion graphs as a powerful primitive in the design of scalable graph transformer architectures. They were able to demonstrate that Expressormer, which combines expansion graphs with global nodes and local neighborhoods, spectrally approximates the full attention mechanism with only a small number of layers and has universal approximation properties. The proposed Exphormer is based on and inherits desirable properties from the modular GraphGPS framework, a recently introduced framework for building general, consequential and scalable graph transformers with linear complexity. GraphGPS combines traditional local message passing and a global attention mechanism, allowing sparse attention mechanisms to improve performance and reduce computational costs.

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Niharika is a Technical Consulting Intern at Marktechpost. She is in her third year of undergraduate and currently pursuing her B.Tech at Indian Institute of Technology (IIT), Kharagpur. She is a very enthusiastic person with a keen interest in machine learning, data science and AI and an avid reader of the latest developments in these fields.

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