no code implementations • 2 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.
no code implementations • 26 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.
1 code implementation • 26 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.
1 code implementation • 15 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.
no code implementations • 15 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.
1 code implementation • 2 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.
1 code implementation • 12 Aug 2024 • Jiahui Jin, YiFan Song, Dong Kan, Haojia Zhu, Xiangguo Sun, Zhicheng Li, Xigang Sun, Jinghui Zhang
Urban region representation is crucial for various urban downstream tasks.
no code implementations • 4 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.
1 code implementation • 8 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.
no code implementations • 27 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.
1 code implementation • 23 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.
no code implementations • 11 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.
1 code implementation • 18 Feb 2024 • Yingying Wang, Yun Xiong, Xixi Wu, Xiangguo Sun, Jiawei Zhang
Drug combinations can cause adverse drug-drug interactions(DDIs).
2 code implementations • 15 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.
1 code implementation • 9 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.
no code implementations • 11 Jan 2024 • Yicong Li, Xiangguo Sun, Hongxu Chen, Sixiao Zhang, Yu Yang, Guandong Xu
Unfortunately, these attention weights are intentionally designed for model accuracy but not explainability.
2 code implementations • 28 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.
3 code implementations • 21 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.
1 code implementation • 9 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.
1 code implementation • 4 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.
no code implementations • 1 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.
1 code implementation • 17 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.
1 code implementation • 20 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.
1 code implementation • 5 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
no code implementations • 2 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.