215 citations · 498 across the 15 of their papers we have counts for
16 papers
Urban Computing in the Era of Large Language Models
Zhonghang Li, Lianghao Xia, Xubin Ren +4
Urban computing has emerged as a multidisciplinary field that harnesses data-driven technologies to address challenges and improve urban living. Traditional approaches, while benef…
EasyST: A Simple Framework for Spatio-Temporal Prediction
Jiabin Tang, Wei Wei, Lianghao Xia +1
Spatio-temporal prediction is a crucial research area in data-driven urban computing, with implications for transportation, public safety, and environmental monitoring. However, sc…
AnyGraph: Graph Foundation Model in the Wild
Lianghao Xia, Chao Huang
The growing ubiquity of relational data structured as graphs has underscored the need for graph learning models with exceptional generalization capabilities. However, current appro…
OpenCity: Open Spatio-Temporal Foundation Models for Traffic Prediction
Zhonghang Li, Long Xia, Lei Shi +3
Accurate traffic forecasting is crucial for effective urban planning and transportation management, enabling efficient resource allocation and enhanced travel experiences. However,…
DiffMM: Multi-Modal Diffusion Model for Recommendation
Yangqin Jiang, Lianghao Xia, Wei Wei +3
The rise of online multi-modal sharing platforms like TikTok and YouTube has enabled personalized recommender systems to incorporate multiple modalities (such as visual, textual, a…
A Survey of Large Language Models for Graphs
Xubin Ren, Jiabin Tang, Dawei Yin +2
Graphs are an essential data structure utilized to represent relationships in real-world scenarios. Prior research has established that Graph Neural Networks (GNNs) deliver impress…
A Comprehensive Survey on Self-Supervised Learning for Recommendation
Xubin Ren, Wei Wei, Lianghao Xia +1
Recommender systems play a crucial role in tackling the challenge of information overload by delivering personalized recommendations based on individual user preferences. Deep lear…
PromptMM: Multi-Modal Knowledge Distillation for Recommendation with Prompt-Tuning
Wei Wei, Jiabin Tang, Yangqin Jiang +2
Multimedia online platforms (e.g., Amazon, TikTok) have greatly benefited from the incorporation of multimedia (e.g., visual, textual, and acoustic) content into their personal rec…
OpenGraph: Towards Open Graph Foundation Models
Lianghao Xia, Ben Kao, Chao Huang
Graph learning has become essential in various domains, including recommendation systems and social network analysis. Graph Neural Networks (GNNs) have emerged as promising techniq…
UrbanGPT: Spatio-Temporal Large Language Models
Zhonghang Li, Lianghao Xia, Jiabin Tang +5
Spatio-temporal prediction aims to forecast and gain insights into the ever-changing dynamics of urban environments across both time and space. Its purpose is to anticipate future…
HiGPT: Heterogeneous Graph Language Model
Jiabin Tang, Yuhao Yang, Wei Wei +4
Heterogeneous graph learning aims to capture complex relationships and diverse relational semantics among entities in a heterogeneous graph to obtain meaningful representations for…
GraphEdit: Large Language Models for Graph Structure Learning
Zirui Guo, Lianghao Xia, Yanhua Yu +4
Graph Structure Learning (GSL) focuses on capturing intrinsic dependencies and interactions among nodes in graph-structured data by generating novel graph structures. Graph Neural…
LLMRec: Large Language Models with Graph Augmentation for Recommendation
Wei Wei, Xubin Ren, Jiabin Tang +6
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. Howev…
Representation Learning with Large Language Models for Recommendation
Xubin Ren, Wei Wei, Lianghao Xia +5
Recommender systems have seen significant advancements with the influence of deep learning and graph neural networks, particularly in capturing complex user-item relationships. How…
GraphGPT: Graph Instruction Tuning for Large Language Models
Jiabin Tang, Yuhao Yang, Wei Wei +5
Graph Neural Networks (GNNs) have evolved to understand graph structures through recursive exchanges and aggregations among nodes. To enhance robustness, self-supervised learning (…
Automated Self-Supervised Learning for Recommendation
Lianghao Xia, Chao Huang, Chunzhen Huang +3
Graph neural networks (GNNs) have emerged as the state-of-the-art paradigm for collaborative filtering (CF). To improve the representation quality over limited labeled data, contra…