Truncated Positional Encodings for Graph Neural Networks

arXiv·low signal

This paper studies truncated positional encodings (PEs) for graph neural networks, analyzing both theoretically and empirically how truncation affects the two most popular PE families and the expressive power they grant GNNs. Mostly of interest to practitioners working directly on graph representation learning rather than mainstream LLM/agent work.