Search Results for author: Jiarong Xu

Found 18 papers, 9 papers with code

Unleashing the Power of Emojis in Texts via Self-supervised Graph Pre-Training

no code implementations22 Sep 2024 Zhou Zhang, Dongzeng Tan, Jiaan Wang, Yilong Chen, Jiarong Xu

Emojis have gained immense popularity on social platforms, serving as a common means to supplement or replace text.

Contrastive Learning

Can Modifying Data Address Graph Domain Adaptation?

1 code implementation27 Jul 2024 Renhong Huang, Jiarong Xu, Xin Jiang, Ruichuan An, Yang Yang

UGDA aims to facilitate knowledge transfer from a labeled source graph to an unlabeled target graph.

Domain Adaptation GRAPH DOMAIN ADAPTATION +1

Unveiling Privacy Vulnerabilities: Investigating the Role of Structure in Graph Data

no code implementations26 Jul 2024 Hanyang Yuan, Jiarong Xu, Cong Wang, Ziqi Yang, Chunping Wang, Keting Yin, Yang Yang

The public sharing of user information opens the door for adversaries to infer private data, leading to privacy breaches and facilitating malicious activities.

Attribute Graph Sampling +2

LOGIN: A Large Language Model Consulted Graph Neural Network Training Framework

1 code implementation22 May 2024 Yiran Qiao, Xiang Ao, Yang Liu, Jiarong Xu, Xiaoqian Sun, Qing He

In this paper, we aim to streamline the GNN design process and leverage the advantages of Large Language Models (LLMs) to improve the performance of GNNs on downstream tasks.

Graph Neural Network Language Modelling +2

CT-Eval: Benchmarking Chinese Text-to-Table Performance in Large Language Models

no code implementations20 May 2024 Haoxiang Shi, Jiaan Wang, Jiarong Xu, Cen Wang, Tetsuya Sakai

Our preliminary analysis of English text-to-table datasets highlights two key factors for dataset construction: data diversity and data hallucination.

Benchmarking Diversity +1

Towards Fair Graph Federated Learning via Incentive Mechanisms

1 code implementation20 Dec 2023 Chenglu Pan, Jiarong Xu, Yue Yu, Ziqi Yang, Qingbiao Wu, Chunping Wang, Lei Chen, Yang Yang

Extensive experiments show that our model achieves the best trade-off between accuracy and the fairness of model gradient, as well as superior payoff fairness.

Fairness Federated Learning +1

Better with Less: A Data-Active Perspective on Pre-Training Graph Neural Networks

1 code implementation NeurIPS 2023 Jiarong Xu, Renhong Huang, Xin Jiang, Yuxuan Cao, Carl Yang, Chunping Wang, Yang Yang

The proposed pre-training pipeline is called the data-active graph pre-training (APT) framework, and is composed of a graph selector and a pre-training model.

Cross-Lingual Knowledge Editing in Large Language Models

2 code implementations16 Sep 2023 Jiaan Wang, Yunlong Liang, Zengkui Sun, Yuxuan Cao, Jiarong Xu, Fandong Meng

With the recent advancements in large language models (LLMs), knowledge editing has been shown as a promising technique to adapt LLMs to new knowledge without retraining from scratch.

knowledge editing

When to Pre-Train Graph Neural Networks? From Data Generation Perspective!

1 code implementation29 Mar 2023 Yuxuan Cao, Jiarong Xu, Carl Yang, Jiaan Wang, Yunchao Zhang, Chunping Wang, Lei Chen, Yang Yang

All convex combinations of graphon bases give rise to a generator space, from which graphs generated form the solution space for those downstream data that can benefit from pre-training.

Unifying Structure Reasoning and Language Model Pre-training for Complex Reasoning

no code implementations21 Jan 2023 Siyuan Wang, Zhongyu Wei, Jiarong Xu, Taishan Li, Zhihao Fan

Recent pre-trained language models (PLMs) equipped with foundation reasoning skills have shown remarkable performance on downstream complex tasks.

Language Modelling Logical Reasoning

Understanding Translationese in Cross-Lingual Summarization

no code implementations14 Dec 2022 Jiaan Wang, Fandong Meng, Yunlong Liang, Tingyi Zhang, Jiarong Xu, Zhixu Li, Jie zhou

In detail, we find that (1) the translationese in documents or summaries of test sets might lead to the discrepancy between human judgment and automatic evaluation; (2) the translationese in training sets would harm model performance in real-world applications; (3) though machine-translated documents involve translationese, they are very useful for building CLS systems on low-resource languages under specific training strategies.

DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection

no code implementations30 Jun 2022 Xuanwen Huang, Yang Yang, Yang Wang, Chunping Wang, Zhisheng Zhang, Jiarong Xu, Lei Chen, Michalis Vazirgiannis

Since GAD emphasizes the application and the rarity of anomalous samples, enriching the varieties of its datasets is fundamental work.

Graph Anomaly Detection

DropMessage: Unifying Random Dropping for Graph Neural Networks

2 code implementations21 Apr 2022 Taoran Fang, Zhiqing Xiao, Chunping Wang, Jiarong Xu, Xuan Yang, Yang Yang

First, it is challenging to find a universal method that are suitable for all cases considering the divergence of different datasets and models.

Graph Representation Learning

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

Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs

no code implementations12 Dec 2020 Jiarong Xu, Yizhou Sun, Xin Jiang, Yanhao Wang, Yang Yang, Chunping Wang, Jiangang Lu

To bridge the gap between theoretical graph attacks and real-world scenarios, in this work, we propose a novel and more realistic setting: strict black-box graph attack, in which the attacker has no knowledge about the victim model at all and is not allowed to send any queries.

Adversarial Attack Graph Classification +1

Unsupervised Adversarially-Robust Representation Learning on Graphs

no code implementations4 Dec 2020 Jiarong Xu, Yang Yang, Junru Chen, Chunping Wang, Xin Jiang, Jiangang Lu, Yizhou Sun

Additionally, we explore a provable connection between the robustness of the unsupervised graph encoder and that of models on downstream tasks.

Adversarial Robustness Community Detection +4

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