no code implementations • 15 Oct 2024 • Jiajun Zhou, Xuanze Chen, Chenxuan Xie, Yu Shanqing, Qi Xuan, Xiaoniu Yang
Graph Transformer (GT), as a special type of Graph Neural Networks (GNNs), utilizes multi-head attention to facilitate high-order message passing.
no code implementations • 12 Sep 2024 • Song Hao, Wentao Fu, Xuanze Chen, Chengxiang Jin, Jiajun Zhou, Shanqing Yu, Qi Xuan
MuFF models the temporal and interactive relationships of packets in network traffic based on the temporal and interactive viewpoints respectively.
no code implementations • 1 Aug 2024 • Chenxiang Jin, Jiajun Zhou, Chenxuan Xie, Shanqing Yu, Qi Xuan, Xiaoniu Yang
The rampant fraudulent activities on Ethereum hinder the healthy development of the blockchain ecosystem, necessitating the reinforcement of regulations.
no code implementations • 29 Jun 2024 • Jiacheng Yao, Maolin Wang, Wanqi Chen, Chengxiang Jin, Jiajun Zhou, Shanqing Yu, Qi Xuan
The wide application of Ethereum technology has brought technological innovation to traditional industries.
no code implementations • 12 Jun 2024 • Yao Lu, Yutao Zhu, Yuqi Li, Dongwei Xu, Yun Lin, Qi Xuan, Xiaoniu Yang
With the successful application of deep learning in communications systems, deep neural networks are becoming the preferred method for signal classification.
1 code implementation • 24 May 2024 • Zeyu Wang, Tianyi Jiang, Yao Lu, Xiaoze Bao, Shanqing Yu, Bin Wei, Qi Xuan
The knowledge-enhanced relation graph module constructs the molecule-property multi-relation graph (MPMRG) to capture the many-to-many relationships between molecules and properties.
1 code implementation • 24 Mar 2024 • Yao Lu, Jianyang Gu, Xuguang Chen, Saeed Vahidian, Qi Xuan
Given that there are no suitable biased datasets for DD, we first construct two biased datasets, CMNIST-DD and CCIFAR10-DD, to establish a foundation for subsequent analysis.
no code implementations • 24 Mar 2024 • Hao Song, Jiacheng Yao, Zhengxi Li, Shaocong Xu, Shibo Jin, Jiajun Zhou, Chenbo Fu, Qi Xuan, Shanqing Yu
Additionally, for the privacy security of FLGNN, this paper designs membership inference attack experiments and differential privacy defense experiments.
1 code implementation • 7 Jan 2024 • Zeyu Wang, Tianyi Jiang, Jinhuan Wang, Qi Xuan
Molecular property prediction refers to the task of labeling molecules with some biochemical properties, playing a pivotal role in the drug discovery and design process.
no code implementations • 8 Nov 2023 • Tao Chen, Shilian Zheng, Jiawei Zhu, Qi Xuan, Xiaoniu Yang
In wireless communication systems, the asynchronization of the oscillators in the transmitter and the receiver along with the Doppler shift due to relative movement may lead to the presence of carrier frequency offset (CFO) in the received signals.
no code implementations • 7 Nov 2023 • Tao Chen, Shilian Zheng, Kunfeng Qiu, Luxin Zhang, Qi Xuan, Xiaoniu Yang
The use of deep learning for radio modulation recognition has become prevalent in recent years.
1 code implementation • CIKM 2023 • Shengbo Gong, Jiajun Zhou, Chenxuan Xie, Qi Xuan
Graph neural networks (GNNs) have been proved powerful in graph-oriented tasks.
1 code implementation • 10 Oct 2023 • Yao Lu, Yutian Huang, Jiaqi Nie, Zuohui Chen, Qi Xuan
Across several benchmark datasets, we find that samples with low coreness values appear less representative of their respective categories, and conversely, those with high coreness values exhibit greater representativeness.
1 code implementation • 5 Oct 2023 • Yao Lu, Xuguang Chen, Yuchen Zhang, Jianyang Gu, Tianle Zhang, Yifan Zhang, Xiaoniu Yang, Qi Xuan, Kai Wang, Yang You
Dataset Distillation (DD) is a prominent technique that encapsulates knowledge from a large-scale original dataset into a small synthetic dataset for efficient training.
1 code implementation • 23 Jun 2023 • Jiajun Zhou, Chenxuan Xie, Shengbo Gong, Jiaxu Qian, Shanqing Yu, Qi Xuan, Xiaoniu Yang
However, common practices in GNNs to acquire high-order information mainly through increasing model depth and altering message-passing mechanisms, which, albeit effective to a certain extent, suffer from three shortcomings: 1) over-smoothing due to excessive model depth and propagation times; 2) high-order information is not fully utilized; 3) low computational efficiency.
no code implementations • 6 Jun 2023 • Jinhuan Wang, Jiafei Shao, Zeyu Wang, Shanqing Yu, Qi Xuan, Xiaoniu Yang
In addition, we also investigate the impact of the second-order subgraph augmentation on mining graph structure interactions, and further, propose a contrastive objective that fuses the first-order and second-order subgraph information.
no code implementations • 4 Jun 2023 • Jiajun Zhou, Shengbo Gong, Xuanze Chen, Chenxuan Xie, Shanqing Yu, Qi Xuan, Xiaoniu Yang
A minority of studies attempt to train different node groups separately but suffer from inappropriate separation metrics and low efficiency.
1 code implementation • 17 May 2023 • Yunzhe Zhang, Yao Lu, Qi Xuan
Contrastive learning, a dominant self-supervised technique, emphasizes similarity in representations between augmentations of the same input and dissimilarity for different ones.
no code implementations • 4 May 2023 • Dayuan Chen, Jian Zhang, Yuqian Lv, Jinhuan Wang, Hongjie Ni, Shanqing Yu, Zhen Wang, Qi Xuan
Furthermore, most methods concentrate on a single attack goal and lack a generalizable adversary to develop distinct attack strategies for diverse goals, thus limiting precise control over victim model behavior in real-world scenarios.
1 code implementation • 14 Mar 2023 • Hui Tang, Yao Lu, Qi Xuan
Our SR-init method is inspired by the discovery that the accuracy drop due to stochastic re-initialization of layer parameters differs in various layers.
1 code implementation • 24 Jan 2023 • Shengbo Gong, Jiajun Zhou, Chenxuan Xie, Qi Xuan
Graph neural networks (GNNs) have been proved powerful in graph-oriented tasks.
1 code implementation • 20 Dec 2022 • Jiajun Zhou, Chenxuan Xie, Shengbo Gong, Zhenyu Wen, Xiangyu Zhao, Qi Xuan, Xiaoniu Yang
To advance research in this emerging direction, this survey provides a comprehensive review and summary of existing graph data augmentation (GDAug) techniques.
no code implementations • 2 Nov 2022 • Shanqing Yu, Yijun Wu, Ran Gan, Jiajun Zhou, Ziwan Zheng, Qi Xuan
Most of the existing knowledge graphs are not usually complete and can be complemented by some reasoning algorithms.
no code implementations • 30 Oct 2022 • Chengxiang Jin, Jiajun Zhou, Jie Jin, Jiajing Wu, Qi Xuan
With the development of Web 3. 0 which emphasizes decentralization, blockchain technology ushers in its revolution and also brings numerous challenges, particularly in the field of cryptocurrency.
1 code implementation • 23 Oct 2022 • Junyuan Fang, Haixian Wen, Jiajing Wu, Qi Xuan, Zibin Zheng, Chi K. Tse
Specifically, to make the node injections as imperceptible and effective as possible, we propose a sampling operation to determine the degree of the newly injected nodes, and then generate features and select neighbors for these injected nodes based on the statistical information of features and evolutionary perturbations obtained from a genetic algorithm, respectively.
no code implementations • 7 May 2022 • Shanqing Yu, Shihan Zhang, Jianlin Zhang, Jiajun Zhou, Qi Xuan, Bing Li, Xiaojuan Hu
Cross-lingual knowledge graph entity alignment aims to discover the cross-lingual links in the multi-language KGs, which is of great significance to the NLP applications and multi-language KGs fusion.
no code implementations • 28 Apr 2022 • Panpan Li, Shengbo Gong, Shaocong Xu, Jiajun Zhou, Yu Shanqing, Qi Xuan
In this work, we propose a generic Cross-Cryptocurrency Relationship Mining module, named C2RM, which can effectively capture the synchronous and asynchronous impact factors between Bitcoin and related Altcoins.
no code implementations • Findings (ACL) 2022 • Bin Zhu, Zhaoquan Gu, Le Wang, Jinyin Chen, Qi Xuan
On top of FADA, we propose geometry-aware adversarial training (GAT) to perform adversarial training on friendly adversarial data so that we can save a large number of search steps.
no code implementations • 5 Apr 2022 • Xinjie Xu, Zhuangzhi Chen, Dongwei Xu, Huaji Zhou, Shanqing Yu, Shilian Zheng, Qi Xuan, Xiaoniu Yang
Data augmentation, as the strategy of expanding dataset, can improve the generalization of the deep learning models and thus improve the accuracy of the models to a certain extent.
no code implementations • 10 Mar 2022 • Yun Xiang, Qijun Chen, Zhongjin Su, Lu Zhang, Zuohui Chen, Guozhi Zhou, Zhuping Yao, Qi Xuan, Yuan Cheng
Cherry tomato (Solanum Lycopersicum) is popular with consumers over the world due to its special flavor.
no code implementations • 23 Dec 2021 • Songtao Peng, Jiaqi Nie, Xincheng Shu, Zhongyuan Ruan, Lei Wang, Yunxuan Sheng, Qi Xuan
As the default protocol for exchanging routing reachability information on the Internet, the abnormal behavior in traffic of Border Gateway Protocols (BGP) is closely related to Internet anomaly events.
1 code implementation • 24 Nov 2021 • Yao Lu, Wen Yang, Yunzhe Zhang, Zuohui Chen, Jinyin Chen, Qi Xuan, Zhen Wang, Xiaoniu Yang
Specifically, we model the process of class separation of intermediate representations in pre-trained DNNs as the evolution of communities in dynamic graphs.
1 code implementation • 22 Nov 2021 • Zuohui Chen, Yao Lu, Jinxuan Hu, Wen Yang, Qi Xuan, Zhen Wang, Xiaoniu Yang
Understanding the black-box representations in Deep Neural Networks (DNN) is an essential problem in deep learning.
no code implementations • 28 Oct 2021 • Zhuangzhi Chen, Jingyang Xiang, Yao Lu, Qi Xuan, Xiaoniu Yang
In this paper, we study the graph structure of the neural network, and propose regular graph based pruning (RGP) to perform a one-shot neural network pruning.
no code implementations • 26 Jul 2021 • Qi Xuan, Xiaohui Li, Zhuangzhi Chen, Dongwei Xu, Shilian Zheng, Xiaoniu Yang
In this letter, we turn to the more challenging problem: can we cluster the modulation types just based on a large number of unlabeled radio signals?
no code implementations • 9 Jul 2021 • Zuohui Chen, Renxuan Wang, Jingyang Xiang, Yue Yu, Xin Xia, Shouling Ji, Qi Xuan, Xiaoniu Yang
Deep Neural Networks (DNN) are known to be vulnerable to adversarial samples, the detection of which is crucial for the wide application of these DNN models.
no code implementations • ICML Workshop AML 2021 • Zuohui Chen, Renxuan Wang, Yao Lu, Jingyang Xiang, Qi Xuan
Experiments on CIFAR10 and SVHN show that the FLOPs and size of our generated model are only 24. 46\% and 4. 86\% of the original model.
no code implementations • 16 Jun 2021 • Qi Xuan, Kunfeng Qiu, Jinchao Zhou, Zhuangzhi Chen, Dongwei Xu, Shilian Zheng, Xiaoniu Yang
In this paper, we propose an Adaptive Visibility Graph (AVG) algorithm that can adaptively map time series into graphs, based on which we further establish an end-to-end classification framework AVGNet, by utilizing GNN model DiffPool as the classifier.
no code implementations • 18 Apr 2021 • Jinhuan Wang, Pengtao Chen, Shanqing Yu, Qi Xuan
In this paper, we propose a Transaction SubGraph Network (TSGN) based classification model to identify phishing accounts in Ethereum.
1 code implementation • 14 Apr 2021 • Jie Shen, Jiajun Zhou, Yunyi Xie, Shanqing Yu, Qi Xuan
In this paper, we present a novel approach to analyze user's behavior from the perspective of the transaction subgraph, which naturally transforms the identity inference task into a graph classification pattern and effectively avoids computation in large-scale graph.
no code implementations • 1 Mar 2021 • Qi Xuan, Jinchao Zhou, Kunfeng Qiu, Dongwei Xu, Shilian Zheng, Xiaoniu Yang
Visibility Graph (VG) transforms time series into graphs, facilitating signal processing by advanced graph data mining algorithms.
1 code implementation • 18 Feb 2021 • Zuohui Chen, Tony Zhang, Zhuangzhi Chen, Yun Xiang, Qi Xuan, Robert P. Dick
The main contribution of this paper is that to the best of our knowledge, it is the first publicly available, high temporal and spatial resolution air quality dataset containing simultaneous point sensor measurements and corresponding images.
no code implementations • 16 Feb 2021 • Yunyi Xie, Jie Jin, Jian Zhang, Shanqing Yu, Qi Xuan
With the wide application of blockchain in the financial field, the rise of various types of cybercrimes has brought great challenges to the security of blockchain.
no code implementations • 16 Feb 2021 • Zuohui Chen, Qing Yuan, Xujie Song, Cheng Chen, Dan Zhang, Yun Xiang, Ruigang Liu, Qi Xuan
Magnetic induction tomography (MIT) is an efficient solution for long-term brain disease monitoring, which focuses on reconstructing bio-impedance distribution inside the human brain using non-intrusive electromagnetic fields.
no code implementations • 18 Nov 2020 • Jinyin Chen, Yunyi Xie, Jian Zhang, Xincheng Shu, Qi Xuan
In this paper, we introduce time-series snapshot network (TSSN) which is a mixture network to model the interactions among users and developers.
Social and Information Networks
no code implementations • 28 Oct 2020 • Zhuangzhi Chen, Hui Cui, Jingyang Xiang, Kunfeng Qiu, Liang Huang, Shilian Zheng, Shichuan Chen, Qi Xuan, Xiaoniu Yang
More interestingly, our proposed models behave extremely well in small-sample learning when only a small training dataset is provided.
no code implementations • 11 Jul 2020 • Jiajun Zhou, Jie Shen, Shanqing Yu, Guanrong Chen, Qi Xuan
Graph classification, which aims to identify the category labels of graphs, plays a significant role in drug classification, toxicity detection, protein analysis etc.
no code implementations • 26 Feb 2020 • Jinyin Chen, Yixian Chen, Haibin Zheng, Shijing Shen, Shanqing Yu, Dan Zhang, Qi Xuan
The adversarial attack methods based on gradient information can adequately find the perturbations, that is, the combinations of rewired links, thereby reducing the effectiveness of the deep learning model based graph embedding algorithms, but it is also easy to fall into a local optimum.
Social and Information Networks
no code implementations • 4 Feb 2020 • Qi Xuan, Yalu Shan, Jinhuan Wang, Zhongyuan Ruan, Guanrong Chen
It is found that both DALR and DILR are more effective than RLR, in the sense that rewiring a smaller number of links can succeed in the same attack.
Social and Information Networks Physics and Society
no code implementations • 24 Nov 2019 • Jinyin Chen, Jian Zhang, Zhi Chen, Min Du, Qi Xuan
In this work, we present the first study of adversarial attack on dynamic network link prediction (DNLP).
no code implementations • 22 Oct 2019 • Jinyin Chen, Yixian Chen, Lihong Chen, Minghao Zhao, Qi Xuan
In this paper, we formalize this community detection attack problem in three scales, including global attack (macroscale), target community attack (mesoscale) and target node attack (microscale).
Social and Information Networks Physics and Society
no code implementations • 21 Jul 2019 • Yun Xiang, Zhuangzhi Chen, Zuohui Chen, Zebin Fang, Haiyang Hao, Jinyin Chen, Yi Liu, Zhefu Wu, Qi Xuan, Xiaoniu Yang
However, recent studies indicate that they are also vulnerable to adversarial attacks.
no code implementations • 15 Jun 2019 • Qi Xuan, Fuxian Li, Yi Liu, Yun Xiang
Experimental results on ModelNet10 and ModelNet40 datasets show that our MV-C3D technique can achieve outstanding performance with multi-view images which are captured from partial angles with less range.
no code implementations • 27 May 2019 • Qi Xuan, Jun Zheng, Lihong Chen, Shanqing Yu, Jinyin Chen, Dan Zhang, Qingpeng Zhang Member
Since a large number of downstream network algorithms, such as community detection and node classification, rely on the Euclidean distance between nodes to evaluate the similarity between them in the embedding space, EDA can be considered as a universal attack on a variety of network algorithms.
Social and Information Networks Physics and Society
no code implementations • 12 Apr 2019 • Jinyin Chen, Yangyang Wu, Lu Fan, Xiang Lin, Haibin Zheng, Shanqing Yu, Qi Xuan
In particular, we use a bipartite network to construct the user-item network, and represent the interactions among users (or items) by the corresponding one-mode projection network.
no code implementations • 21 Mar 2019 • Qi Xuan, Jinhuan Wang, Minghao Zhao, Junkun Yuan, Chenbo Fu, Zhongyuan Ruan, Guanrong Chen
In other words, the structural features of SGNs can complement that of the original network for better network classification, regardless of the feature extraction method used, such as the handcrafted, network embedding and kernel-based methods.
Ranked #10 on Graph Classification on MUTAG
no code implementations • 11 Mar 2019 • Jinyin Chen, Yangyang Wu, Xiang Lin, Qi Xuan
In this paper, we are interested in the possibility of defense against adversarial attack on network, and propose defense strategies for GNNs against attacks.
Social and Information Networks Physics and Society
1 code implementation • 22 Feb 2019 • Jinyin Chen, Jian Zhang, Xuanheng Xu, Chengbo Fu, Dan Zhang, Qingpeng Zhang, Qi Xuan
Predicting the potential relations between nodes in networks, known as link prediction, has long been a challenge in network science.
no code implementations • 1 Nov 2018 • Jinyin Chen, Lihong Chen, Yixian Chen, Minghao Zhao, Shanqing Yu, Qi Xuan, Xiaoniu Yang
In particular, we first give two heuristic attack strategies, i. e., Community Detection Attack (CDA) and Degree Based Attack (DBA), as baselines, utilizing the information of detected community structure and node degree, respectively.
Social and Information Networks
no code implementations • 16 Sep 2018 • Shanqing Yu, Minghao Zhao, Chenbo Fu, Huimin Huang, Xincheng Shu, Qi Xuan, Guanrong Chen
This is the first time to study privacy protection on targeted links against link-prediction-based attacks.
Social and Information Networks Physics and Society
no code implementations • 8 Sep 2018 • Jinyin Chen, Yangyang Wu, Xuanheng Xu, Yixian Chen, Haibin Zheng, Qi Xuan
Network embedding maps a network into a low-dimensional Euclidean space, and thus facilitate many network analysis tasks, such as node classification, link prediction and community detection etc, by utilizing machine learning methods.
Physics and Society Social and Information Networks