Recently, research teams from the University of Washington, Peking University, and JD Research jointly released a breakthrough research result: a universal framework suitable for the graph field. The framework is able to handle graph classification tasks on any data set, any task type, and any scenario, which is a significant advancement in the graph field. By unifying node and edge information into a natural language framework, text graphs are used to achieve distributed alignment and unified representation of graph data, thus solving many problems in graph data processing. This research brings new possibilities to research and applications in the field of graphs and deserves attention.
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Professor Chen Yixin’s team at the University of Washington, Zhang Muhan from Peking University, and Tao Dacheng from JD Research jointly proposed the first general framework in the graph field. This framework can solve the classification tasks of any data set, any task type, and any scene in the graph field. By using a unified natural language framework to describe node information and edge information in all graph data, and using text graphs, the distribution alignment and unification of graph data is achieved, as well as the unified representation of subtasks in various graph fields.The proposal of this general framework marks that graph data processing technology has entered a new stage. Its application prospects in various fields are broad, and it is expected to promote the further development and application of graph data analysis technology. In the future, we can look forward to more innovative applications based on this framework.