1 code implementation • 27 Nov 2017 • Ziwei Zhang, Peng Cui, Jian Pei, Xiao Wang, Wenwu Zhu
By setting a maximum tolerated error as a threshold, we can trigger SVD restart automatically when the margin exceeds this threshold. We prove that the time complexity of our method is linear with respect to the number of local dynamic changes, and our method is general across different types of dynamic networks.
Social and Information Networks
no code implementations • 12 Apr 2018 • Cong Ma, Changshui Yang, Fan Yang, Yueqing Zhuang, Ziwei Zhang, Huizhu Jia, Xiaodong Xie
In this paper, we propose a novel tracklet processing method to cleave and re-connect tracklets on crowd or long-term occlusion by Siamese Bi-Gated Recurrent Unit (GRU).
Ranked #20 on Multi-Object Tracking on MOT16
2 code implementations • 7 May 2018 • Ziwei Zhang, Peng Cui, Haoyang Li, Xiao Wang, Wenwu Zhu
Network embedding, which learns low-dimensional vector representation for nodes in the network, has attracted considerable research attention recently.
no code implementations • 27 Sep 2018 • Ziwei Zhang
Spectral clustering and Singular Value Decomposition (SVD) are both widely used technique for analyzing graph data.
1 code implementation • 11 Dec 2018 • Ziwei Zhang, Peng Cui, Wenwu Zhu
Recently, substantial research efforts have been devoted to applying deep learning methods to graphs, resulting in beneficial advances in graph analysis techniques.
1 code implementation • CVPR 2020 • Ziwei Zhang, Chi Su, Liang Zheng, Xiaodong Xie
Compared with the existing practice of feature concatenation, we find that uncovering the correlation among the three factors is a superior way of leveraging the pivotal contextual cues provided by edges and poses.
1 code implementation • 2020 • Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, Wenwu Zhu
In particular, we develop a novel graph embedding algorithm, High-Order Proximity preserved Embedding (HOPE for short), which is scalable to preserve high-order proximities of large scale graphs and capable of capturing the asymmetric transitivity.
no code implementations • 8 Jun 2020 • Ziwei Zhang, Peng Cui, Jian Pei, Xin Wang, Wenwu Zhu
Graph Neural Networks (GNNs) are emerging machine learning models on graphs.
1 code implementation • 5 Sep 2020 • Ziwei Zhang, Chenhao Niu, Peng Cui, Jian Pei, Bo Zhang, Wenwu Zhu
Graph neural networks (GNNs) are emerging machine learning models on graphs.
no code implementations • 7 Feb 2021 • Ruobing Xie, Qi Liu, Shukai Liu, Ziwei Zhang, Peng Cui, Bo Zhang, Leyu Lin
In this paper, we propose a novel Heterogeneous graph neural network framework for diversified recommendation (GraphDR) in matching to improve both recommendation accuracy and diversity.
2 code implementations • 1 Mar 2021 • Ziwei Zhang, Xin Wang, Wenwu Zhu
Machine learning on graphs has been extensively studied in both academic and industry.
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.
no code implementations • 29 Sep 2021 • Xuguang Duan, Xin Wang, Ziwei Zhang, Wenwu Zhu
Program induction serves as one way to analog the ability of human thinking.
no code implementations • NeurIPS 2021 • Haoyang Li, Xin Wang, Ziwei Zhang, Zehuan Yuan, Hang Li, Wenwu Zhu
Then we propose a novel factor-wise discrimination objective in a contrastive learning manner, which can force the factorized representations to independently reflect the expressive information from different latent factors.
no code implementations • 7 Dec 2021 • Haoyang Li, Xin Wang, Ziwei Zhang, Wenwu Zhu
Our proposed OOD-GNN employs a novel nonlinear graph representation decorrelation method utilizing random Fourier features, which encourages the model to eliminate the statistical dependence between relevant and irrelevant graph representations through iteratively optimizing the sample graph weights and graph encoder.
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.
1 code implementation • 4 Jan 2022 • Xin Wang, Ziwei Zhang, Wenwu Zhu
Graph machine learning has been extensively studied in both academic and industry.
1 code implementation • 16 Feb 2022 • Haoyang Li, Xin Wang, Ziwei Zhang, Wenwu Zhu
This paper is the first systematic and comprehensive review of OOD generalization on graphs, to the best of our knowledge.
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.
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.
no code implementations • CVPR 2023 • Beini Xie, Heng Chang, Ziwei Zhang, Xin Wang, Daixin Wang, Zhiqiang Zhang, Rex Ying, Wenwu Zhu
To tackle these challenges, we propose a novel Robust Neural Architecture search framework for GNNs (G-RNA).
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.
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.
no code implementations • 27 Oct 2023 • Yijian Qin, Xin Wang, Ziwei Zhang, Wenwu Zhu
Text-attributed graphs (TAGs) are prevalent on the web and research over TAGs such as citation networks, e-commerce networks and social networks has attracted considerable attention in the web community.
1 code implementation • NeurIPS 2023 • Haonan Yuan, Qingyun Sun, Xingcheng Fu, Ziwei Zhang, Cheng Ji, Hao Peng, JianXin Li
To the best of our knowledge, we are the first to study OOD generalization on dynamic graphs from the environment learning perspective.
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 • 20 Dec 2023 • Ziwei Zhang, Mengtao Zhu, Jiabin Liu, Yunjie Li, Shafei Wang
To enhance distinguishability, we design Class Conditional Vectors (CCVs) to match the latent representations extracted from input samples, achieving perfect reconstruction for known samples while yielding poor results for unknown ones.
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 • 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.