Graph Encoding

Graph encoding focuses on representing graph-structured data in a format suitable for processing by machine learning models, particularly large language models (LLMs) and graph neural networks (GNNs). Current research emphasizes developing effective encoding techniques that capture both local and global graph properties, addressing challenges like the impact of information distance within the graph structure on model performance. These advancements are improving the accuracy of various applications, including knowledge graph reasoning, sports analytics, and sparse neural network optimization, by enabling more sophisticated analysis of relational data.