no code implementations • 22 Aug 2024 • Xinyu Yuan, Zhihao Zhan, Zuobai Zhang, Manqi Zhou, Jianan Zhao, Boyu Han, Yue Li, Jian Tang
The novel loss component guide scCello to learn the cell-type-specific representation and the structural relation between cell types from the cell ontology graph, respectively.
1 code implementation • 30 May 2024 • Jianan Zhao, Zhaocheng Zhu, Mikhail Galkin, Hesham Mostafa, Michael Bronstein, Jian Tang
Many existing methods following the inductive setup can generalize to test graphs with new structures, but assuming the feature and label spaces remain the same as the training ones.
1 code implementation • 10 Apr 2024 • Hongru Du, Jianan Zhao, Yang Zhao, Shaochong Xu, Xihong Lin, Yiran Chen, Lauren M. Gardner, Hao, Yang
Forecasting the short-term spread of an ongoing disease outbreak is a formidable challenge due to the complexity of contributing factors, some of which can be characterized through interlinked, multi-modality variables such as epidemiological time series data, viral biology, population demographics, and the intersection of public policy and human behavior.
1 code implementation • 3 Feb 2024 • Haitao Mao, Zhikai Chen, Wenzhuo Tang, Jianan Zhao, Yao Ma, Tong Zhao, Neil Shah, Mikhail Galkin, Jiliang Tang
Graph Foundation Models (GFMs) are emerging as a significant research topic in the graph domain, aiming to develop graph models trained on extensive and diverse data to enhance their applicability across various tasks and domains.
no code implementations • 9 Jan 2024 • Xuzheng Yu, Chen Jiang, Wei zhang, Tian Gan, Linlin Chao, Jianan Zhao, Yuan Cheng, Qingpei Guo, Wei Chu
With the explosive growth of video data in real-world applications, a comprehensive representation of videos becomes increasingly important.
1 code implementation • 2 Oct 2023 • Jianan Zhao, Le Zhuo, Yikang Shen, Meng Qu, Kai Liu, Michael Bronstein, Zhaocheng Zhu, Jian Tang
Furthermore, GraphText paves the way for interactive graph reasoning, allowing both humans and LLMs to communicate with the model seamlessly using natural language.
1 code implementation • 23 May 2023 • Rui Li, Xu Chen, Chaozhuo Li, Yanming Shen, Jianan Zhao, Yujing Wang, Weihao Han, Hao Sun, Weiwei Deng, Qi Zhang, Xing Xie
Embedding models have shown great power in knowledge graph completion (KGC) task.
1 code implementation • 28 Feb 2023 • Wen Li, Cheng Zou, Meng Wang, Furong Xu, Jianan Zhao, Ruobing Zheng, Yuan Cheng, Wei Chu
In this paper, we propose a Diverse and Compact Transformer (DC-Former) that can achieve a similar effect by splitting embedding space into multiple diverse and compact subspaces.
1 code implementation • 12 Nov 2022 • Jianan Zhao, Qianlong Wen, Mingxuan Ju, Chuxu Zhang, Yanfang Ye
Specifically, The pre-training phase aims to comprehensively estimate the underlying graph structure by a multi-view contrastive learning framework with both intra- and inter-view link prediction tasks.
2 code implementations • 26 Oct 2022 • Jianan Zhao, Meng Qu, Chaozhuo Li, Hao Yan, Qian Liu, Rui Li, Xing Xie, Jian Tang
In this paper, we propose an efficient and effective solution to learning on large text-attributed graphs by fusing graph structure and language learning with a variational Expectation-Maximization (EM) framework, called GLEM.
Ranked #1 on
Node Property Prediction
on ogbn-papers100M
no code implementations • 17 Oct 2022 • Yiqi Wang, Chaozhuo Li, Wei Jin, Rui Li, Jianan Zhao, Jiliang Tang, Xing Xie
To bridge such gap, in this work we introduce the first test-time training framework for GNNs to enhance the model generalization capacity for the graph classification task.
1 code implementation • 16 Feb 2022 • Rui Li, Jianan Zhao, Chaozhuo Li, Di He, Yiqi Wang, Yuming Liu, Hao Sun, Senzhang Wang, Weiwei Deng, Yanming Shen, Xing Xie, Qi Zhang
The effectiveness of knowledge graph embedding (KGE) largely depends on the ability to model intrinsic relation patterns and mapping properties.
no code implementations • 9 Dec 2021 • Wen Li, Furong Xu, Jianan Zhao, Ruobing Zheng, Cheng Zou, Meng Wang, Yuan Cheng
Triplet loss is a widely adopted loss function in ReID task which pulls the hardest positive pairs close and pushes the hardest negative pairs far away.
1 code implementation • 8 Dec 2021 • Mingxuan Ju, Shifu Hou, Yujie Fan, Jianan Zhao, Liang Zhao, Yanfang Ye
To solve this problem, in this paper, we propose a novel framework - i. e., namely Adaptive Kernel Graph Neural Network (AKGNN) - which learns to adapt to the optimal graph kernel in a unified manner at the first attempt.
no code implementations • 25 Oct 2021 • Jianan Zhao, Chaozhuo Li, Qianlong Wen, Yiqi Wang, Yuming Liu, Hao Sun, Xing Xie, Yanfang Ye
Existing graph transformer models typically adopt fully-connected attention mechanism on the whole input graph and thus suffer from severe scalability issues and are intractable to train in data insufficient cases.
1 code implementation • CVPR 2021 • Jianan Zhao, Fengliang Qi, Guangyu Ren, Lin Xu
Vehicle re-identification (re-ID) is of great significance to urban operation, management, security and has gained more attention in recent years.
no code implementations • 23 Nov 2020 • Ziliang Zhong, Muhang Zheng, Huafeng Mai, Jianan Zhao, Xinyi Liu
Computer-aided diagnosis establishes methods for robust assessment of medical image-based examination.