PromptMM: Multi-Modal Knowledge Distillation for Recommendation with Prompt-Tuning

1 code implementation • 27 Feb 2024 • Wei Wei, Jiabin Tang, Yangqin Jiang, Lianghao Xia, Chao Huang

Additionally, to adjust the impact of inaccuracies in multimedia data, a disentangled multi-modal list-wise distillation is developed with modality-aware re-weighting mechanism.

Knowledge Distillation Model Compression +1

HiGPT: Heterogeneous Graph Language Model

1 code implementation • 25 Feb 2024 • Jiabin Tang, Yuhao Yang, Wei Wei, Lei Shi, Long Xia, Dawei Yin, Chao Huang

However, existing frameworks for heterogeneous graph learning have limitations in generalizing across diverse heterogeneous graph datasets.

Graph Learning Language Modelling +1

UrbanGPT: Spatio-Temporal Large Language Models

1 code implementation • 25 Feb 2024 • Zhonghang Li, Lianghao Xia, Jiabin Tang, Yong Xu, Lei Shi, Long Xia, Dawei Yin, Chao Huang

These findings highlight the potential of building large language models for spatio-temporal learning, particularly in zero-shot scenarios where labeled data is scarce.

LLMRec: Large Language Models with Graph Augmentation for Recommendation

1 code implementation • 1 Nov 2023 • Wei Wei, Xubin Ren, Jiabin Tang, Qinyong Wang, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, Chao Huang

By employing these strategies, we address the challenges posed by sparse implicit feedback and low-quality side information in recommenders.

Model Optimization Recommendation Systems

Spatio-Temporal Meta Contrastive Learning

1 code implementation • 26 Oct 2023 • Jiabin Tang, Lianghao Xia, Jie Hu, Chao Huang

Although recent STGNN models with contrastive learning aim to address these challenges, most of them use pre-defined augmentation strategies that heavily depend on manual design and cannot be customized for different Spatio-Temporal Graph (STG) scenarios.

Contrastive Learning Crime Prediction +1

Explainable Spatio-Temporal Graph Neural Networks

1 code implementation • 26 Oct 2023 • Jiabin Tang, Lianghao Xia, Chao Huang

Furthermore, we propose a structure distillation approach based on the Graph Information Bottleneck (GIB) principle with an explainable objective, which is instantiated by the STG encoder and decoder.

Crime Prediction Decoder +1

GraphGPT: Graph Instruction Tuning for Large Language Models

1 code implementation • 19 Oct 2023 • Jiabin Tang, Yuhao Yang, Wei Wei, Lei Shi, Lixin Su, Suqi Cheng, Dawei Yin, Chao Huang

In this work, we present the GraphGPT framework that aligns LLMs with graph structural knowledge with a graph instruction tuning paradigm.

Data Augmentation Graph Learning +2

Spatio-Temporal Latent Graph Structure Learning for Traffic Forecasting

no code implementations • 25 Feb 2022 • Jiabin Tang, Tang Qian, Shijing Liu, Shengdong Du, Jie Hu, Tianrui Li

Accurate traffic forecasting, the foundation of intelligent transportation systems (ITS), has never been more significant than nowadays due to the prosperity of smart cities and urban computing.

Benchmarking Graph structure learning

Neurological Consequences of COVID-19 Infection

no code implementations • 9 Jun 2021 • Jiabin Tang, Shivani Patel, Steve Gentleman, Paul Matthews

COVID-19 infections have well described systemic manifestations, especially respiratory problems.