Search Results for author: Qiaoyu Tan

Found 31 papers, 22 papers with code

Gradient Rewiring for Editable Graph Neural Network Training

1 code implementation21 Oct 2024 Zhimeng Jiang, Zirui Liu, Xiaotian Han, Qizhang Feng, Hongye Jin, Qiaoyu Tan, Kaixiong Zhou, Na Zou, Xia Hu

In this paper, we first observe the gradient of cross-entropy loss for the target node and training nodes with significant inconsistency, which indicates that directly fine-tuning the base model using the loss on the target node deteriorates the performance on training nodes.

Graph Neural Network Model Editing

Reasoning Like a Doctor: Improving Medical Dialogue Systems via Diagnostic Reasoning Process Alignment

1 code implementation20 Jun 2024 Kaishuai Xu, Yi Cheng, Wenjun Hou, Qiaoyu Tan, Wenjie Li

We propose a novel framework, Emulation, designed to generate an appropriate response that relies on abductive and deductive diagnostic reasoning analyses and aligns with clinician preferences through thought process modeling.

GAugLLM: Improving Graph Contrastive Learning for Text-Attributed Graphs with Large Language Models

1 code implementation17 Jun 2024 Yi Fang, Dongzhe Fan, Daochen Zha, Qiaoyu Tan

This work studies self-supervised graph learning for text-attributed graphs (TAGs) where nodes are represented by textual attributes.

Contrastive Learning Graph Learning +1

UniGLM: Training One Unified Language Model for Text-Attributed Graphs

1 code implementation17 Jun 2024 Yi Fang, Dongzhe Fan, Sirui Ding, Ninghao Liu, Qiaoyu Tan

Representation learning on text-attributed graphs (TAGs), where nodes are represented by textual descriptions, is crucial for textual and relational knowledge systems and recommendation systems.

Contrastive Learning Graph Embedding +5

GraphFM: A Comprehensive Benchmark for Graph Foundation Model

no code implementations12 Jun 2024 Yuhao Xu, Xinqi Liu, Keyu Duan, Yi Fang, Yu-Neng Chuang, Daochen Zha, Qiaoyu Tan

To address these questions, we have constructed a rigorous benchmark that thoroughly analyzes and studies the generalization and scalability of self-supervised Graph Neural Network (GNN) models.

Graph Neural Network Link Prediction +3

Better Late Than Never: Formulating and Benchmarking Recommendation Editing

1 code implementation6 Jun 2024 Chengyu Lai, Sheng Zhou, Zhimeng Jiang, Qiaoyu Tan, Yuanchen Bei, Jiawei Chen, Ningyu Zhang, Jiajun Bu

This paper introduces a novel and significant task termed recommendation editing, which focuses on modifying known and unsuitable recommendation behaviors.

Benchmarking Recommendation Systems

E2GNN: Efficient Graph Neural Network Ensembles for Semi-Supervised Classification

1 code implementation6 May 2024 Xin Zhang, Daochen Zha, Qiaoyu Tan

Next, instead of directly combing their outputs for label inference, we train a simple multi-layer perceptron--MLP model to mimic their predictions on both labeled and unlabeled nodes.

Ensemble Learning Graph Neural Network

Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection

no code implementations9 Dec 2023 Yuanchen Bei, Sheng Zhou, Qiaoyu Tan, Hao Xu, Hao Chen, Zhao Li, Jiajun Bu

To address these issues, we utilize the advantages of reinforcement learning in adaptively learning in complex environments and propose a novel method that incorporates Reinforcement neighborhood selection for unsupervised graph ANomaly Detection (RAND).

Graph Anomaly Detection Representation Learning

GiGaMAE: Generalizable Graph Masked Autoencoder via Collaborative Latent Space Reconstruction

1 code implementation18 Aug 2023 Yucheng Shi, Yushun Dong, Qiaoyu Tan, Jundong Li, Ninghao Liu

By considering embeddings encompassing graph topology and attribute information as reconstruction targets, our model could capture more generalized and comprehensive knowledge.

Attribute Self-Supervised Learning

Homophily-enhanced Structure Learning for Graph Clustering

1 code implementation10 Aug 2023 Ming Gu, Gaoming Yang, Sheng Zhou, Ning Ma, Jiawei Chen, Qiaoyu Tan, Meihan Liu, Jiajun Bu

Graph clustering is a fundamental task in graph analysis, and recent advances in utilizing graph neural networks (GNNs) have shown impressive results.

Clustering Graph Clustering +1

Collaborative Graph Neural Networks for Attributed Network Embedding

1 code implementation22 Jul 2023 Qiaoyu Tan, Xin Zhang, Xiao Huang, Hao Chen, Jundong Li, Xia Hu

Graph neural networks (GNNs) have shown prominent performance on attributed network embedding.

Attribute Network Embedding

OpenGSL: A Comprehensive Benchmark for Graph Structure Learning

1 code implementation NeurIPS 2023 Zhiyao Zhou, Sheng Zhou, Bochao Mao, Xuanyi Zhou, Jiawei Chen, Qiaoyu Tan, Daochen Zha, Yan Feng, Chun Chen, Can Wang

Moreover, we observe that the learned graph structure demonstrates a strong generalization ability across different GNN models, despite the high computational and space consumption.

Graph structure learning Representation Learning

ChatGraph: Interpretable Text Classification by Converting ChatGPT Knowledge to Graphs

1 code implementation3 May 2023 Yucheng Shi, Hehuan Ma, Wenliang Zhong, Qiaoyu Tan, Gengchen Mai, Xiang Li, Tianming Liu, Junzhou Huang

To tackle these limitations, we propose a novel framework that leverages the power of ChatGPT for specific tasks, such as text classification, while improving its interpretability.

Decision Making Language Modelling +3

Towards Personalized Preprocessing Pipeline Search

no code implementations28 Feb 2023 Diego Martinez, Daochen Zha, Qiaoyu Tan, Xia Hu

However, the existing systems often have a very small search space for feature preprocessing with the same preprocessing pipeline applied to all the numerical features.

AutoML Clustering +1

Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection

1 code implementation23 Dec 2022 Qiaoyu Tan, Xin Zhang, Ninghao Liu, Daochen Zha, Li Li, Rui Chen, Soo-Hyun Choi, Xia Hu

To bridge the gap, we introduce a Personalized Subgraph Selector (PS2) as a plug-and-play framework to automatically, personally, and inductively identify optimal subgraphs for different edges when performing GNNLP.

Link Prediction

DreamShard: Generalizable Embedding Table Placement for Recommender Systems

1 code implementation5 Oct 2022 Daochen Zha, Louis Feng, Qiaoyu Tan, Zirui Liu, Kwei-Herng Lai, Bhargav Bhushanam, Yuandong Tian, Arun Kejariwal, Xia Hu

Although prior work has explored learning-based approaches for the device placement of computational graphs, embedding table placement remains to be a challenging problem because of 1) the operation fusion of embedding tables, and 2) the generalizability requirement on unseen placement tasks with different numbers of tables and/or devices.

Recommendation Systems Reinforcement Learning (RL)

Graph Contrastive Learning with Personalized Augmentation

no code implementations14 Sep 2022 Xin Zhang, Qiaoyu Tan, Xiao Huang, Bo Li

Thus, blindly augmenting all graphs without considering their individual characteristics may undermine the performance of GCL arts. To deal with this, we propose the first principled framework, termed as \textit{G}raph contrastive learning with \textit{P}ersonalized \textit{A}ugmentation (GPA), to advance conventional GCL by allowing each graph to choose its own suitable augmentation operations. In essence, GPA infers tailored augmentation strategies for each graph based on its topology and node attributes via a learnable augmentation selector, which is a plug-and-play module and can be effectively trained with downstream GCL models end-to-end.

Contrastive Learning Data Augmentation

Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning

2 code implementations26 Aug 2022 Daochen Zha, Kwei-Herng Lai, Qiaoyu Tan, Sirui Ding, Na Zou, Xia Hu

Motivated by this, we investigate developing a learning-based over-sampling algorithm to optimize the classification performance, which is a challenging task because of the huge and hierarchical decision space.

Hierarchical Reinforcement Learning reinforcement-learning +2

MGAE: Masked Autoencoders for Self-Supervised Learning on Graphs

1 code implementation7 Jan 2022 Qiaoyu Tan, Ninghao Liu, Xiao Huang, Rui Chen, Soo-Hyun Choi, Xia Hu

We introduce a novel masked graph autoencoder (MGAE) framework to perform effective learning on graph structure data.

Decoder Graph Neural Network +3

Sparse-Interest Network for Sequential Recommendation

1 code implementation18 Feb 2021 Qiaoyu Tan, Jianwei Zhang, Jiangchao Yao, Ninghao Liu, Jingren Zhou, Hongxia Yang, Xia Hu

Our sparse-interest module can adaptively infer a sparse set of concepts for each user from the large concept pool and output multiple embeddings accordingly.

Sequential Recommendation

Dynamic Memory based Attention Network for Sequential Recommendation

1 code implementation18 Feb 2021 Qiaoyu Tan, Jianwei Zhang, Ninghao Liu, Xiao Huang, Hongxia Yang, Jingren Zhou, Xia Hu

It segments the overall long behavior sequence into a series of sub-sequences, then trains the model and maintains a set of memory blocks to preserve long-term interests of users.

Sequential Recommendation

Learning to Hash with Graph Neural Networks for Recommender Systems

no code implementations4 Mar 2020 Qiaoyu Tan, Ninghao Liu, Xing Zhao, Hongxia Yang, Jingren Zhou, Xia Hu

In this work, we investigate the problem of hashing with graph neural networks (GNNs) for high quality retrieval, and propose a simple yet effective discrete representation learning framework to jointly learn continuous and discrete codes.

Deep Hashing Graph Representation Learning +1

Is a Single Vector Enough? Exploring Node Polysemy for Network Embedding

1 code implementation25 May 2019 Ninghao Liu, Qiaoyu Tan, Yuening Li, Hongxia Yang, Jingren Zhou, Xia Hu

Network embedding models are powerful tools in mapping nodes in a network into continuous vector-space representations in order to facilitate subsequent tasks such as classification and link prediction.

General Classification Language Modelling +3

Deep Representation Learning for Social Network Analysis

no code implementations18 Apr 2019 Qiaoyu Tan, Ninghao Liu, Xia Hu

First, we introduce the basic models for learning node representations in homogeneous networks.

Anomaly Detection Attribute +3

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