Search Results for author: Xiangguo Sun

Found 25 papers, 16 papers with code

A Comprehensive Analysis on LLM-based Node Classification Algorithms

no code implementations2 Feb 2025 Xixi Wu, Yifei Shen, Fangzhou Ge, Caihua Shan, Yizhu Jiao, Xiangguo Sun, Hong Cheng

Node classification is a fundamental task in graph analysis, with broad applications across various fields.

Node Classification

Efficient Multi-modal Large Language Models via Visual Token Grouping

no code implementations26 Nov 2024 Minbin Huang, Runhui Huang, Han Shi, Yimeng Chen, Chuanyang Zheng, Xiangguo Sun, Xin Jiang, Zhenguo Li, Hong Cheng

The development of Multi-modal Large Language Models (MLLMs) enhances Large Language Models (LLMs) with the ability to perceive data formats beyond text, significantly advancing a range of downstream applications, such as visual question answering and image captioning.

Image Captioning Question Answering +2

Personality Analysis from Online Short Video Platforms with Multi-domain Adaptation

1 code implementation26 Oct 2024 Sixu An, Xiangguo Sun, Yicong Li, Yu Yang, Guandong Xu

Personality analysis from online short videos has gained prominence due to its applications in personalized recommendation systems, sentiment analysis, and human-computer interaction.

Domain Adaptation Recommendation Systems +1

Adaptive Coordinators and Prompts on Heterogeneous Graphs for Cross-Domain Recommendations

1 code implementation15 Oct 2024 Hengyu Zhang, Chunxu Shen, Xiangguo Sun, Jie Tan, Yu Rong, Chengzhi Piao, Hong Cheng, Lingling Yi

However, integrating multi-domain knowledge for the cross-domain recommendation is very hard due to inherent disparities in user behavior and item characteristics and the risk of negative transfer, where irrelevant or conflicting information from the source domains adversely impacts the target domain's performance.

Recommendation Systems

G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

no code implementations15 Oct 2024 Guibin Zhang, Yanwei Yue, Xiangguo Sun, Guancheng Wan, Miao Yu, Junfeng Fang, Kun Wang, Tianlong Chen, Dawei Cheng

Recent advancements in large language model (LLM)-based agents have demonstrated that collective intelligence can significantly surpass the capabilities of individual agents, primarily due to well-crafted inter-agent communication topologies.

HumanEval Language Modelling +2

Does Graph Prompt Work? A Data Operation Perspective with Theoretical Analysis

1 code implementation2 Oct 2024 Qunzhong Wang, Xiangguo Sun, Hong Cheng

In recent years, graph prompting has emerged as a promising research direction, enabling the learning of additional tokens or subgraphs appended to the original graphs without requiring retraining of pre-trained graph models across various applications.

Recommendation Systems

When LLM Meets Hypergraph: A Sociological Analysis on Personality via Online Social Networks

no code implementations4 Jul 2024 Zhiyao Shu, Xiangguo Sun, Hong Cheng

By employing the framework on this dataset, we can effectively capture the nuances of individual personalities and their online behaviors, leading to a deeper understanding of human interactions in the digital world.

ProG: A Graph Prompt Learning Benchmark

1 code implementation8 Jun 2024 Chenyi Zi, Haihong Zhao, Xiangguo Sun, Yiqing Lin, Hong Cheng, Jia Li

Artificial general intelligence on graphs has shown significant advancements across various applications, yet the traditional 'Pre-train & Fine-tune' paradigm faces inefficiencies and negative transfer issues, particularly in complex and few-shot settings.

Graph Condensation for Open-World Graph Learning

no code implementations27 May 2024 Xinyi Gao, Tong Chen, Wentao Zhang, Yayong Li, Xiangguo Sun, Hongzhi Yin

Consequently, due to the limited generalization capacity of condensed graphs, applications that employ GC for efficient GNN training end up with sub-optimal GNNs when confronted with evolving graph structures and distributions in dynamic real-world situations.

Graph Learning

Graph Sparsification via Mixture of Graphs

1 code implementation23 May 2024 Guibin Zhang, Xiangguo Sun, Yanwei Yue, Chonghe Jiang, Kun Wang, Tianlong Chen, Shirui Pan

Specifically, MoG incorporates multiple sparsifier experts, each characterized by unique sparsity levels and pruning criteria, and selects the appropriate experts for each node.

Graph Learning

All in One: Multi-Task Prompting for Graph Neural Networks (Extended Abstract)

no code implementations11 Mar 2024 Xiangguo Sun, Hong Cheng, Jia Li, Bo Liu, Jihong Guan

This paper is an extended abstract of our original work published in KDD23, where we won the best research paper award (Xiangguo Sun, Hong Cheng, Jia Li, Bo Liu, and Jihong Guan.

Meta-Learning

All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretraining

2 code implementations15 Feb 2024 Haihong Zhao, Aochuan Chen, Xiangguo Sun, Hong Cheng, Jia Li

In response to this challenge, we propose a novel approach called Graph COordinators for PrEtraining (GCOPE), that harnesses the underlying commonalities across diverse graph datasets to enhance few-shot learning.

Few-Shot Learning

Prompt Learning on Temporal Interaction Graphs

1 code implementation9 Feb 2024 Xi Chen, Siwei Zhang, Yun Xiong, Xixi Wu, Jiawei Zhang, Xiangguo Sun, Yao Zhang, Feng Zhao, Yulin kang

In detail, we propose a temporal prompt generator to offer temporally-aware prompts for different tasks.

Representation Learning

Graph Prompt Learning: A Comprehensive Survey and Beyond

2 code implementations28 Nov 2023 Xiangguo Sun, Jiawen Zhang, Xixi Wu, Hong Cheng, Yun Xiong, Jia Li

This paper presents a pioneering survey on the emerging domain of graph prompts in AGI, addressing key challenges and opportunities in harnessing graph data for AGI applications.

Survey

A Survey of Graph Meets Large Language Model: Progress and Future Directions

3 code implementations21 Nov 2023 Yuhan Li, ZHIXUN LI, Peisong Wang, Jia Li, Xiangguo Sun, Hong Cheng, Jeffrey Xu Yu

First of all, we propose a new taxonomy, which organizes existing methods into three categories based on the role (i. e., enhancer, predictor, and alignment component) played by LLMs in graph-related tasks.

Language Modeling Language Modelling +2

Counter-Empirical Attacking based on Adversarial Reinforcement Learning for Time-Relevant Scoring System

1 code implementation9 Nov 2023 Xiangguo Sun, Hong Cheng, Hang Dong, Bo Qiao, Si Qin, QIngwei Lin

To establish such scoring systems, several "empirical criteria" are firstly determined, followed by dedicated top-down design for each factor of the score, which usually requires enormous effort to adjust and tune the scoring function in the new application scenario.

All in One: Multi-task Prompting for Graph Neural Networks

1 code implementation4 Jul 2023 Xiangguo Sun, Hong Cheng, Jia Li, Bo Liu, Jihong Guan

Inspired by the prompt learning in natural language processing (NLP), which has presented significant effectiveness in leveraging prior knowledge for various NLP tasks, we study the prompting topic for graphs with the motivation of filling the gap between pre-trained models and various graph tasks.

Meta-Learning

Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning

no code implementations1 Jul 2022 Haoran Yang, Hongxu Chen, Sixiao Zhang, Xiangguo Sun, Qian Li, Xiangyu Zhao, Guandong Xu

In this paper, we propose a novel method to utilize \textbf{C}ounterfactual mechanism to generate artificial hard negative samples for \textbf{G}raph \textbf{C}ontrastive learning, namely \textbf{CGC}, which has a different perspective compared to those sampling-based strategies.

Contrastive Learning counterfactual +2

Graph Masked Autoencoders with Transformers

1 code implementation17 Feb 2022 Sixiao Zhang, Hongxu Chen, Haoran Yang, Xiangguo Sun, Philip S. Yu, Guandong Xu

In this paper, we propose Graph Masked Autoencoders (GMAEs), a self-supervised transformer-based model for learning graph representations.

Decoder Graph Classification +1

Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation

1 code implementation20 Jan 2022 Sixiao Zhang, Hongxu Chen, Xiangguo Sun, Yicong Li, Guandong Xu

Extensive experiments show that our attack outperforms unsupervised baseline attacks and has comparable performance with supervised attacks in multiple downstream tasks including node classification and link prediction.

Adversarial Attack Contrastive Learning +3

Temporal Meta-path Guided Explainable Recommendation

1 code implementation5 Jan 2021 Hongxu Chen, Yicong Li, Xiangguo Sun, Guandong Xu, Hongzhi Yin

This paper utilizes well-designed item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable recommendations.

Social and Information Networks

Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction

no code implementations2 Jun 2020 Hongxu Chen, Hongzhi Yin, Xiangguo Sun, Tong Chen, Bogdan Gabrys, Katarzyna Musial

Moreover, to adapt the proposed method to be capable of handling large-scale social networks, we propose a two-phase space reconciliation mechanism to align the embedding spaces in both network partitioning based parallel training and account matching across different social networks.

Anchor link prediction Model Selection

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