no code implementations • 23 Apr 2024 • Peiwen Li, Xin Wang, Zeyang Zhang, Yuan Meng, Fang Shen, Yue Li, Jialong Wang, Yang Li, Wenweu Zhu
In the field of Artificial Intelligence for Information Technology Operations, causal discovery is pivotal for operation and maintenance of graph construction, facilitating downstream industrial tasks such as root cause analysis.
no code implementations • 21 Mar 2024 • Yang Yao, Xin Wang, Zeyang Zhang, Yijian Qin, Ziwei Zhang, Xu Chu, Yuekui Yang, Wenwu Zhu, Hong Mei
In this paper, we propose LLM4GraphGen to explore the ability of LLMs for graph generation with systematical task designs and extensive experiments.
no code implementations • NeurIPS 2023 • Zeyang Zhang, Xin Wang, Ziwei Zhang, Guangyao Shen, Shiqi Shen, Wenwu Zhu
To address the challenge, we propose a novel Disentangled Self-supervised Graph Neural Architecture Search (DSGAS) model, which is able to discover the optimal architectures capturing various latent graph factors in a self-supervised fashion based on unlabeled graph data.
1 code implementation • NeurIPS 2023 • Zeyang Zhang, Xin Wang, Ziwei Zhang, Zhou Qin, Weigao Wen, Hui Xue, Haoyang Li, Wenwu Zhu
In this paper, we discover that there exist cases with distribution shifts unobservable in the time domain while observable in the spectral domain, and propose to study distribution shifts on dynamic graphs in the spectral domain for the first time.
no code implementations • 30 Dec 2023 • Zeyang Zhang, Hui Li, Tianyang Xu, XiaoJun Wu, Josef Kittler
We focus on Infrared-Visible image registration and fusion task (IVRF).
no code implementations • 21 Dec 2023 • Wei Feng, Xin Wang, Hong Chen, Zeyang Zhang, Zihan Song, Yuwei Zhou, Wenwu Zhu
Recently, researchers have attempted to investigate the capability of LLMs in handling videos and proposed several video LLM models.
no code implementations • 27 Nov 2023 • Zeyang Zhang, Xingwang Li, Fei Teng, Ning Lin, Xueling Zhu, Xin Wang, Wenwu Zhu
We first model human albumin prediction as a dynamic graph regression problem to model the dynamics and patient relationship.
no code implementations • 24 Nov 2023 • Zeyang Zhang, Xin Wang, Ziwei Zhang, Haoyang Li, Wenwu Zhu
In this paper, we propose Disentangled Intervention-based Dynamic graph Attention networks with Invariance Promotion (I-DIDA) to handle spatio-temporal distribution shifts in dynamic graphs by discovering and utilizing invariant patterns, i. e., structures and features whose predictive abilities are stable across distribution shifts.
no code implementations • 26 Oct 2023 • Zeyang Zhang, Xin Wang, Ziwei Zhang, Haoyang Li, Yijian Qin, Wenwu Zhu
Our main observations are: 1) LLMs have preliminary spatial-temporal understanding abilities on dynamic graphs, 2) Dynamic graph tasks show increasing difficulties for LLMs as the graph size and density increase, while not sensitive to the time span and data generation mechanism, 3) the proposed DST2 prompting method can help to improve LLMs' spatial-temporal understanding abilities on dynamic graphs for most tasks.
1 code implementation • 28 Aug 2023 • Ziwei Zhang, Haoyang Li, Zeyang Zhang, Yijian Qin, Xin Wang, Wenwu Zhu
In order to promote applying large models for graphs forward, we present a perspective paper to discuss the challenges and opportunities associated with developing large graph models.
1 code implementation • 18 Jun 2022 • Yijian Qin, Ziwei Zhang, Xin Wang, Zeyang Zhang, Wenwu Zhu
To the best of our knowledge, our work is the first benchmark for graph neural architecture search.
1 code implementation • 7 Apr 2022 • Zeyang Zhang, Ziwei Zhang, Xin Wang, Wenwu Zhu
To solve these challenges, we first propose a principled hardness measurement to quantify the hardness of TSP instances.
no code implementations • 23 Dec 2021 • Ziwei Zhang, Xin Wang, Zeyang Zhang, Peng Cui, Wenwu Zhu
Based on the experimental results, we advocate that TinvNN should be considered a new starting point and an essential baseline for further studies of transformation-invariant geometric deep learning.
no code implementations • NeurIPS 2021 • Yijian Qin, Xin Wang, Zeyang Zhang, Wenwu Zhu
Extensive experiments on real-world graph datasets demonstrate that our proposed GASSO model is able to achieve state-of-the-art performance compared with existing baselines.
2 code implementations • ICLR Workshop GTRL 2021 • Ziwei Zhang, Yijian Qin, Zeyang Zhang, Chaoyu Guan, Jie Cai, Heng Chang, Jiyan Jiang, Haoyang Li, Zixin Sun, Beini Xie, Yang Yao, YiPeng Zhang, Xin Wang, Wenwu Zhu
To fill this gap, we present Automated Graph Learning (AutoGL), the first dedicated library for automated machine learning on graphs.