Transformers are Graph Neural Networks
This document explores the relationship between Transformers and Graph Neural Networks (GNNs). It demonstrates how Transformers can be interpreted as message-passing GNNs operating on fully connected graphs of tokens, where self-attention captures the importance of tokens and positional encodings provide structural hints. While mathematically connected to GNNs, Transformers leverage dense matrix operations, resulting in superior hardware efficiency. The document discusses the evolution of NLP architectures from RNNs to Transformers, highlighting the limitations of sequential RNN processing. The central concept of the attention mechanism within Transformers is explained in detail, including query, key, and value transformations. Multi-head attention is also discussed as a method to capture multiple aspects of relationships within data. Ultimately, the paper explains how Transformers have surpassed RNNs in NLP and related applications due to their expressiveness and scalability. #transformers #gnn #graphneuralnetworks #attentionmechanism #nlp #deeplearning #representationlearning #machinelearning paper - https://arxiv.org/pdf/2506.22084v1 subscribe - https://t.me/arxivdotorg donations: USDT: 0xAA7B976c6A9A7ccC97A3B55B7fb353b6Cc8D1ef7 BTC: bc1q8972egrt38f5ye5klv3yye0996k2jjsz2zthpr ETH: 0xAA7B976c6A9A7ccC97A3B55B7fb353b6Cc8D1ef7 SOL: DXnz1nd6oVm7evDJk25Z2wFSstEH8mcA1dzWDCVjUj9e created with NotebookLM
This document explores the relationship between Transformers and Graph Neural Networks (GNNs). It demonstrates how Transformers can be interpreted as message-passing GNNs operating on fully connected graphs of tokens, where self-attention captures the importance of tokens and positional encodings provide structural hints. While mathematically connected to GNNs, Transformers leverage dense matrix operations, resulting in superior hardware efficiency. The document discusses the evolution of NLP architectures from RNNs to Transformers, highlighting the limitations of sequential RNN processing. The central concept of the attention mechanism within Transformers is explained in detail, including query, key, and value transformations. Multi-head attention is also discussed as a method to capture multiple aspects of relationships within data. Ultimately, the paper explains how Transformers have surpassed RNNs in NLP and related applications due to their expressiveness and scalability. #transformers #gnn #graphneuralnetworks #attentionmechanism #nlp #deeplearning #representationlearning #machinelearning paper - https://arxiv.org/pdf/2506.22084v1 subscribe - https://t.me/arxivdotorg donations: USDT: 0xAA7B976c6A9A7ccC97A3B55B7fb353b6Cc8D1ef7 BTC: bc1q8972egrt38f5ye5klv3yye0996k2jjsz2zthpr ETH: 0xAA7B976c6A9A7ccC97A3B55B7fb353b6Cc8D1ef7 SOL: DXnz1nd6oVm7evDJk25Z2wFSstEH8mcA1dzWDCVjUj9e created with NotebookLM