Large language models on graphs: A comprehensive survey
Large language models (LLMs), such as GPT4 and LLaMA, are creating significant
advancements in natural language processing, due to their strong text encoding/decoding …
advancements in natural language processing, due to their strong text encoding/decoding …
The heterophilic graph learning handbook: Benchmarks, models, theoretical analysis, applications and challenges
Talk like a graph: Encoding graphs for large language models
Graphs are a powerful tool for representing and analyzing complex relationships in real-
world applications such as social networks, recommender systems, and computational …
world applications such as social networks, recommender systems, and computational …
Graph retrieval-augmented generation: A survey
Recently, Retrieval-Augmented Generation (RAG) has achieved remarkable success in
addressing the challenges of Large Language Models (LLMs) without necessitating …
addressing the challenges of Large Language Models (LLMs) without necessitating …
Towards graph foundation models: A survey and beyond
Emerging as fundamental building blocks for diverse artificial intelligence applications,
foundation models have achieved notable success across natural language processing and …
foundation models have achieved notable success across natural language processing and …
A survey of large language models for graphs
Graphs are an essential data structure utilized to represent relationships in real-world
scenarios. Prior research has established that Graph Neural Networks (GNNs) deliver …
scenarios. Prior research has established that Graph Neural Networks (GNNs) deliver …