Large language models on graphs: A comprehensive survey

B Jin, G Liu, C Han, M Jiang, H Ji… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Large language models (LLMs), such as GPT4 and LLaMA, are creating significant
advancements in natural language processing, due to their strong text encoding/decoding …

Graph prompt learning: A comprehensive survey and beyond

X Sun, J Zhang, X Wu, H Cheng, Y Xiong… - arXiv preprint arXiv …, 2023 - arxiv.org
Artificial General Intelligence (AGI) has revolutionized numerous fields, yet its integration
with graph data, a cornerstone in our interconnected world, remains nascent. This paper …

Llmrec: Large language models with graph augmentation for recommendation

W Wei, X Ren, J Tang, Q Wang, L Su, S Cheng… - Proceedings of the 17th …, 2024 - dl.acm.org
The problem of data sparsity has long been a challenge in recommendation systems, and
previous studies have attempted to address this issue by incorporating side information …

A survey of large language models for graphs

X Ren, J Tang, D Yin, N Chawla, C Huang - Proceedings of the 30th …, 2024 - dl.acm.org
Graphs are an essential data structure utilized to represent relationships in real-world
scenarios. Prior research has established that Graph Neural Networks (GNNs) deliver …

G-retriever: Retrieval-augmented generation for textual graph understanding and question answering

X He, Y Tian, Y Sun, NV Chawla, T Laurent… - arXiv preprint arXiv …, 2024 - arxiv.org
Given a graph with textual attributes, we enable users tochat with their graph': that is, to ask
questions about the graph using a conversational interface. In response to a user's …

Graph chain-of-thought: Augmenting large language models by reasoning on graphs

B Jin, C Xie, J Zhang, KK Roy, Y Zhang, Z Li… - arXiv preprint arXiv …, 2024 - arxiv.org
Large language models (LLMs), while exhibiting exceptional performance, suffer from
hallucinations, especially on knowledge-intensive tasks. Existing works propose to augment …

Knowledgeable preference alignment for llms in domain-specific question answering

Y Zhang, Z Chen, Y Fang, Y Lu, F Li, W Zhang… - arXiv preprint arXiv …, 2023 - arxiv.org
Deploying large language models (LLMs) to real scenarios for domain-specific question
answering (QA) is a key thrust for LLM applications, which poses numerous challenges …

Breaking the trilemma of privacy, utility, and efficiency via controllable machine unlearning

Z Liu, G Dou, E Chien, C Zhang, Y Tian… - Proceedings of the ACM …, 2024 - dl.acm.org
Machine Unlearning (MU) algorithms have become increasingly critical due to the
imperative adherence to data privacy regulations. The primary objective of MU is to erase …

Fairness-aware graph neural networks: A survey

A Chen, RA Rossi, N Park, P Trivedi, Y Wang… - ACM Transactions on …, 2024 - dl.acm.org
Graph Neural Networks (GNNs) have become increasingly important due to their
representational power and state-of-the-art predictive performance on many fundamental …

Promptmm: Multi-modal knowledge distillation for recommendation with prompt-tuning

W Wei, J Tang, L Xia, Y Jiang, C Huang - Proceedings of the ACM on …, 2024 - dl.acm.org
Multimedia online platforms (eg, Amazon, TikTok) have greatly benefited from the
incorporation of multimedia (eg, visual, textual, and acoustic) content into their personal …