Search Results for author: Yuxiao Dong

Found 45 papers, 34 papers with code

P-INT: A Path-based Interaction Model for Few-shot Knowledge Graph Completion

no code implementations Findings (EMNLP) 2021 Jingwen Xu, Jing Zhang, Xirui Ke, Yuxiao Dong, Hong Chen, Cuiping Li, Yongbin Liu

Its general process is to first encode the implicit relation of an entity pair and then match the relation of a query entity pair with the relations of the reference entity pairs.

Knowledge Graph Completion

Revisiting Parallel Context Windows: A Frustratingly Simple Alternative and Chain-of-Thought Deterioration

no code implementations24 May 2023 Kejuan Yang, Xiao Liu, Kaiwen Men, Aohan Zeng, Yuxiao Dong, Jie Tang

We identify two crucial limitations in the evaluation of recent parallel-integrated method Parallel Context Windows (PCW), which extends the maximum context lengths of language models, e. g., 2048 for LLaMA, by harnessing window-wise attention and positional embedding techniques.

ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation

2 code implementations12 Apr 2023 Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, Yuxiao Dong

We present ImageReward -- the first general-purpose text-to-image human preference reward model -- to address various prevalent issues in generative models and align them with human values and preferences.

Text-to-Image Generation

GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner

2 code implementations10 Apr 2023 Zhenyu Hou, Yufei He, Yukuo Cen, Xiao Liu, Yuxiao Dong, Evgeny Kharlamov, Jie Tang

Graph self-supervised learning (SSL), including contrastive and generative approaches, offers great potential to address the fundamental challenge of label scarcity in real-world graph data.

Self-Supervised Learning

CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Evaluations on HumanEval-X

1 code implementation30 Mar 2023 Qinkai Zheng, Xiao Xia, Xu Zou, Yuxiao Dong, Shan Wang, Yufei Xue, Zihan Wang, Lei Shen, Andi Wang, Yang Li, Teng Su, Zhilin Yang, Jie Tang

Large pre-trained code generation models, such as OpenAI Codex, can generate syntax- and function-correct code, making the coding of programmers more productive and our pursuit of artificial general intelligence closer.

Code Generation

GOAL: A Challenging Knowledge-grounded Video Captioning Benchmark for Real-time Soccer Commentary Generation

no code implementations26 Mar 2023 Ji Qi, Jifan Yu, Teng Tu, Kunyu Gao, Yifan Xu, Xinyu Guan, Xiaozhi Wang, Yuxiao Dong, Bin Xu, Lei Hou, Juanzi Li, Jie Tang, Weidong Guo, Hui Liu, Yu Xu

Despite the recent emergence of video captioning models, how to generate vivid, fine-grained video descriptions based on the background knowledge (i. e., long and informative commentary about the domain-specific scenes with appropriate reasoning) is still far from being solved, which however has great applications such as automatic sports narrative.

Video Captioning

Web-Scale Academic Name Disambiguation: the WhoIsWho Benchmark, Leaderboard, and Toolkit

no code implementations23 Feb 2023 Bo Chen, Jing Zhang, Fanjin Zhang, Tianyi Han, Yuqing Cheng, Xiaoyan Li, Yuxiao Dong, Jie Tang

Name disambiguation -- a fundamental problem in online academic systems -- is now facing greater challenges with the increasing growth of research papers.

Data Integration

Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries

1 code implementation16 Aug 2022 Xiao Liu, Shiyu Zhao, Kai Su, Yukuo Cen, Jiezhong Qiu, Mengdi Zhang, Wei Wu, Yuxiao Dong, Jie Tang

In this work, we present the Knowledge Graph Transformer (kgTransformer) with masked pre-training and fine-tuning strategies.

Parameter-Efficient Prompt Tuning Makes Generalized and Calibrated Neural Text Retrievers

2 code implementations14 Jul 2022 Weng Lam Tam, Xiao Liu, Kaixuan Ji, Lilong Xue, Xingjian Zhang, Yuxiao Dong, Jiahua Liu, Maodi Hu, Jie Tang

By updating only 0. 1% of the model parameters, the prompt tuning strategy can help retrieval models achieve better generalization performance than traditional methods in which all parameters are updated.

Retrieval Text Retrieval

GraphMAE: Self-Supervised Masked Graph Autoencoders

3 code implementations22 May 2022 Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, Jie Tang

Despite this, contrastive learning-which heavily relies on structural data augmentation and complicated training strategies-has been the dominant approach in graph SSL, while the progress of generative SSL on graphs, especially graph autoencoders (GAEs), has thus far not reached the potential as promised in other fields.

Contrastive Learning Graph Classification +4

IDPG: An Instance-Dependent Prompt Generation Method

no code implementations NAACL 2022 Zhuofeng Wu, Sinong Wang, Jiatao Gu, Rui Hou, Yuxiao Dong, V. G. Vinod Vydiswaran, Hao Ma

Prompt tuning is a new, efficient NLP transfer learning paradigm that adds a task-specific prompt in each input instance during the model training stage.

Language Modelling Natural Language Understanding +1

GRAND+: Scalable Graph Random Neural Networks

1 code implementation12 Mar 2022 Wenzheng Feng, Yuxiao Dong, Tinglin Huang, Ziqi Yin, Xu Cheng, Evgeny Kharlamov, Jie Tang

In this work, we present a scalable and high-performance GNN framework GRAND+ for semi-supervised graph learning.

Data Augmentation Graph Learning +2

SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs

1 code implementation2 Mar 2022 Xiao Liu, Haoyun Hong, Xinghao Wang, Zeyi Chen, Evgeny Kharlamov, Yuxiao Dong, Jie Tang

We present SelfKG with efficient strategies to optimize this objective for aligning entities without label supervision.

Entity Alignment Knowledge Graphs +1

Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks

1 code implementation30 Dec 2021 Qingsong Lv, Ming Ding, Qiang Liu, Yuxiang Chen, Wenzheng Feng, Siming He, Chang Zhou, Jianguo Jiang, Yuxiao Dong, Jie Tang

Heterogeneous graph neural networks (HGNNs) have been blossoming in recent years, but the unique data processing and evaluation setups used by each work obstruct a full understanding of their advancements.


SCR: Training Graph Neural Networks with Consistency Regularization

4 code implementations8 Dec 2021 Chenhui Zhang, Yufei He, Yukuo Cen, Zhenyu Hou, Wenzheng Feng, Yuxiao Dong, Xu Cheng, Hongyun Cai, Feng He, Jie Tang

However, it is unclear how to best design the generalization strategies in GNNs, as it works in a semi-supervised setting for graph data.

Node Classification

Adaptive Diffusion in Graph Neural Networks

no code implementations NeurIPS 2021 Jialin Zhao, Yuxiao Dong, Ming Ding, Evgeny Kharlamov, Jie Tang

Notably, message passing based GNNs, e. g., graph convolutional networks, leverage the immediate neighbors of each node during the aggregation process, and recently, graph diffusion convolution (GDC) is proposed to expand the propagation neighborhood by leveraging generalized graph diffusion.

Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning

1 code implementation8 Nov 2021 Qinkai Zheng, Xu Zou, Yuxiao Dong, Yukuo Cen, Da Yin, Jiarong Xu, Yang Yang, Jie Tang

To bridge this gap, we present the Graph Robustness Benchmark (GRB) with the goal of providing a scalable, unified, modular, and reproducible evaluation for the adversarial robustness of GML models.

Adversarial Robustness Benchmarking +1

Graph Contrastive Learning for Anomaly Detection

2 code implementations17 Aug 2021 Bo Chen, Jing Zhang, Xiaokang Zhang, Yuxiao Dong, Jian Song, Peng Zhang, Kaibo Xu, Evgeny Kharlamov, Jie Tang

To achieve the contrastive objective, we design a graph neural network encoder that can infer and further remove suspicious links during message passing, as well as learn the global context of the input graph.

Anomaly Detection Binary Classification +2

A Self-supervised Method for Entity Alignment

1 code implementation17 Jun 2021 Xiao Liu, Haoyun Hong, Xinghao Wang, Zeyi Chen, Evgeny Kharlamov, Yuxiao Dong, Jie Tang

We present SelfKG by leveraging this discovery to design a contrastive learning strategy across two KGs.

Contrastive Learning Entity Alignment +2

TDGIA:Effective Injection Attacks on Graph Neural Networks

1 code implementation12 Jun 2021 Xu Zou, Qinkai Zheng, Yuxiao Dong, Xinyu Guan, Evgeny Kharlamov, Jialiang Lu, Jie Tang

In the GIA scenario, the adversary is not able to modify the existing link structure and node attributes of the input graph, instead the attack is performed by injecting adversarial nodes into it.

Adversarial Attack

OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs

5 code implementations17 Mar 2021 Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, Jure Leskovec

Enabling effective and efficient machine learning (ML) over large-scale graph data (e. g., graphs with billions of edges) can have a great impact on both industrial and scientific applications.

BIG-bench Machine Learning Graph Learning +4

Understanding WeChat User Preferences and "Wow" Diffusion

1 code implementation4 Mar 2021 Fanjin Zhang, Jie Tang, Xueyi Liu, Zhenyu Hou, Yuxiao Dong, Jing Zhang, Xiao Liu, Ruobing Xie, Kai Zhuang, Xu Zhang, Leyu Lin, Philip S. Yu

"Top Stories" is a novel friend-enhanced recommendation engine in WeChat, in which users can read articles based on preferences of both their own and their friends.

Graph Representation Learning Social and Information Networks

OAG-BERT: Towards A Unified Backbone Language Model For Academic Knowledge Services

1 code implementation3 Mar 2021 Xiao Liu, Da Yin, Jingnan Zheng, Xingjian Zhang, Peng Zhang, Hongxia Yang, Yuxiao Dong, Jie Tang

Academic knowledge services have substantially facilitated the development of the science enterprise by providing a plenitude of efficient research tools.

Language Modelling Link Prediction

CogDL: A Comprehensive Library for Graph Deep Learning

1 code implementation1 Mar 2021 Yukuo Cen, Zhenyu Hou, Yan Wang, Qibin Chen, Yizhen Luo, Zhongming Yu, Hengrui Zhang, Xingcheng Yao, Aohan Zeng, Shiguang Guo, Yuxiao Dong, Yang Yang, Peng Zhang, Guohao Dai, Yu Wang, Chang Zhou, Hongxia Yang, Jie Tang

In CogDL, we propose a unified design for the training and evaluation of GNN models for various graph tasks, making it unique among existing graph learning libraries.

Graph Classification Graph Embedding +5

MATCH: Metadata-Aware Text Classification in A Large Hierarchy

1 code implementation15 Feb 2021 Yu Zhang, Zhihong Shen, Yuxiao Dong, Kuansan Wang, Jiawei Han

Multi-label text classification refers to the problem of assigning each given document its most relevant labels from the label set.

General Classification Multi Label Text Classification +2

Generalizing Graph Convolutional Networks

1 code implementation1 Jan 2021 Jialin Zhao, Yuxiao Dong, Jie Tang, Ming Ding, Kuansan Wang

Graph convolutional networks (GCNs) have emerged as a powerful framework for mining and learning with graphs.

A Large-Scale Database for Graph Representation Learning

2 code implementations16 Nov 2020 Scott Freitas, Yuxiao Dong, Joshua Neil, Duen Horng Chau

With the rapid emergence of graph representation learning, the construction of new large-scale datasets is necessary to distinguish model capabilities and accurately assess the strengths and weaknesses of each technique.

Graph Representation Learning imbalanced classification

GPT-GNN: Generative Pre-Training of Graph Neural Networks

3 code implementations27 Jun 2020 Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang, Yizhou Sun

Graph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-structured data.

Graph Generation

Open Graph Benchmark: Datasets for Machine Learning on Graphs

16 code implementations NeurIPS 2020 Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, Jure Leskovec

We present the Open Graph Benchmark (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research.

Knowledge Graphs

Heterogeneous Graph Transformer

4 code implementations3 Mar 2020 Ziniu Hu, Yuxiao Dong, Kuansan Wang, Yizhou Sun

Recent years have witnessed the emerging success of graph neural networks (GNNs) for modeling structured data.

Graph Sampling Node Property Prediction

Diagonal Graph Convolutional Networks with Adaptive Neighborhood Aggregation

no code implementations25 Sep 2019 Jie Zhang, Yuxiao Dong, Jie Tang

In this paper, we revisit the mathematical foundation of GCNs and study how to extend their representation capacity.

Graph Attention Graph Classification +1

NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization

1 code implementation26 Jun 2019 Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Chi Wang, Kuansan Wang, Jie Tang

Previous research shows that 1) popular network embedding benchmarks, such as DeepWalk, are in essence implicitly factorizing a matrix with a closed form, and 2)the explicit factorization of such matrix generates more powerful embeddings than existing methods.

Network Embedding

DeepInf: Social Influence Prediction with Deep Learning

1 code implementation15 Jul 2018 Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, Jie Tang

Inspired by the recent success of deep neural networks in a wide range of computing applications, we design an end-to-end framework, DeepInf, to learn users' latent feature representation for predicting social influence.

Feature Engineering Representation Learning

Neural Tensor Factorization

no code implementations13 Feb 2018 Xian Wu, Baoxu Shi, Yuxiao Dong, Chao Huang, Nitesh Chawla

Neural collaborative filtering (NCF) and recurrent recommender systems (RRN) have been successful in modeling user-item relational data.

Collaborative Filtering Link Prediction +1

Revisiting Knowledge Base Embedding as Tensor Decomposition

no code implementations ICLR 2018 Jiezhong Qiu, Hao Ma, Yuxiao Dong, Kuansan Wang, Jie Tang

We study the problem of knowledge base (KB) embedding, which is usually addressed through two frameworks---neural KB embedding and tensor decomposition.

Link Prediction Tensor Decomposition

Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec

4 code implementations9 Oct 2017 Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, Jie Tang

This work lays the theoretical foundation for skip-gram based network embedding methods, leading to a better understanding of latent network representation learning.

Network Embedding

A Century of Science: Globalization of Scientific Collaborations, Citations, and Innovations

no code implementations17 Apr 2017 Yuxiao Dong, Hao Ma, Zhihong Shen, Kuansan Wang

We find that science has benefited from the shift from individual work to collaborative effort, with over 90% of the world-leading innovations generated by collaborations in this century, nearly four times higher than they were in the 1900s.

Digital Libraries Social and Information Networks Physics and Society

Will This Paper Increase Your h-index? Scientific Impact Prediction

2 code implementations15 Dec 2014 Yuxiao Dong, Reid A. Johnson, Nitesh V. Chawla

The effectiveness of such predictions, however, is fundamentally limited by the power-law distribution of citations, whereby publications with few citations are extremely common and publications with many citations are relatively rare.

Social and Information Networks Digital Libraries Physics and Society H.2.8; H.3.7

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