1 code implementation • 14 Feb 2024 • Juanhui Li, Haoyu Han, Zhikai Chen, Harry Shomer, Wei Jin, Amin Javari, Jiliang Tang
To integrate text information, various methods have been introduced, mostly following a naive fusion framework.
no code implementations • 13 Feb 2024 • Li Ma, Haoyu Han, Juanhui Li, Harry Shomer, Hui Liu, Xiaofeng Gao, Jiliang Tang
Link prediction, which aims to forecast unseen connections in graphs, is a fundamental task in graph machine learning.
no code implementations • 5 Dec 2023 • Yao Teng, Enze Xie, Yue Wu, Haoyu Han, Zhenguo Li, Xihui Liu
In this paper, we propose a new diffusion-based method for interactive point-based video manipulation, called Drag-A-Video.
no code implementations • 20 Oct 2023 • Kaiqi Yang, Haoyu Han, Wei Jin, Hui Liu
Existing augmentation views with perturbed graph structures are usually based on random topology corruption in the spatial domain; however, from perspectives of the spectral domain, this approach may be ineffective as it fails to pose tailored impacts on the information of different frequencies, thus weakening the agreement between the augmentation views.
1 code implementation • 7 Oct 2023 • Zhikai Chen, Haitao Mao, Hongzhi Wen, Haoyu Han, Wei Jin, Haiyang Zhang, Hui Liu, Jiliang Tang
In light of these observations, this work introduces a label-free node classification on graphs with LLMs pipeline, LLM-GNN.
1 code implementation • NeurIPS 2023 • Wei Jin, Haitao Mao, Zheng Li, Haoming Jiang, Chen Luo, Hongzhi Wen, Haoyu Han, Hanqing Lu, Zhengyang Wang, Ruirui Li, Zhen Li, Monica Xiao Cheng, Rahul Goutam, Haiyang Zhang, Karthik Subbian, Suhang Wang, Yizhou Sun, Jiliang Tang, Bing Yin, Xianfeng Tang
To test the potential of the dataset, we introduce three tasks in this work: (1) next-product recommendation, (2) next-product recommendation with domain shifts, and (3) next-product title generation.
1 code implementation • NeurIPS 2023 • Haitao Mao, Zhikai Chen, Wei Jin, Haoyu Han, Yao Ma, Tong Zhao, Neil Shah, Jiliang Tang
Recent studies on Graph Neural Networks(GNNs) provide both empirical and theoretical evidence supporting their effectiveness in capturing structural patterns on both homophilic and certain heterophilic graphs.
1 code implementation • 3 Feb 2023 • Rui Xue, Haoyu Han, MohamadAli Torkamani, Jian Pei, Xiaorui Liu
Recent works have demonstrated the benefits of capturing long-distance dependency in graphs by deeper graph neural networks (GNNs).
no code implementations • 8 Jun 2022 • Haoyu Han, Xiaorui Liu, Haitao Mao, MohamadAli Torkamani, Feng Shi, Victor Lee, Jiliang Tang
Extensive experiments demonstrate that the proposed method can achieve comparable or better performance with state-of-the-art baselines while it has significantly better computation and memory efficiency.