1 code implementation • 17 Mar 2023 • Dongcheng Zou, Hao Peng, Xiang Huang, Renyu Yang, JianXin Li, Jia Wu, Chunyang Liu, Philip S. Yu
Graph Neural Networks (GNNs) are de facto solutions to structural data learning.
no code implementations • 22 Feb 2023 • Zhizhi Yu, Di Jin, Cuiying Huo, Zhiqiang Wang, Xiulong Liu, Heng Qi, Jia Wu, Lingfei Wu
Graph neural networks for trust evaluation typically adopt a straightforward way such as one-hot or node2vec to comprehend node characteristics, which ignores the valuable semantic knowledge attached to nodes.
1 code implementation • 20 Feb 2023 • Zitai Qiu, Jia Wu, Jian Yang, Xing Su, Charu C. Aggarwal
This model addresses the heterogeneity of social media, and, with this graph, the information in social media can be used to capture structural information based on the properties of hyperbolic space.
no code implementations • 18 Feb 2023 • Ce Zhou, Qian Li, Chen Li, Jun Yu, Yixin Liu, Guangjing Wang, Kai Zhang, Cheng Ji, Qiben Yan, Lifang He, Hao Peng, JianXin Li, Jia Wu, Ziwei Liu, Pengtao Xie, Caiming Xiong, Jian Pei, Philip S. Yu, Lichao Sun
This study provides a comprehensive review of recent research advancements, current and future challenges, and opportunities for PFMs in text, image, graph, as well as other data modalities.
no code implementations • 13 Feb 2023 • Nasrin Shabani, Jia Wu, Amin Beheshti, Jin Foo, Ambreen Hanif, Maryam Shahabikargar
As large-scale graphs become more widespread today, it exposes computational challenges to extract, process, and interpret large graph data.
no code implementations • 10 Feb 2023 • Lingfeng Zhong, Jia Wu, Qian Li, Hao Peng, Xindong Wu
A knowledge graph is built in three steps: knowledge acquisition, knowledge refinement, and knowledge evolution.
1 code implementation • 28 Jan 2023 • Cheng Ji, JianXin Li, Hao Peng, Jia Wu, Xingcheng Fu, Qingyun Sun, Phillip S. Yu
Contrastive Learning (CL) has been proved to be a powerful self-supervised approach for a wide range of domains, including computer vision and graph representation learning.
1 code implementation • 14 Jan 2023 • Zhenyu Yang, Ge Zhang, Jia Wu, Jian Yang, Quan Z. Sheng, Shan Xue, Chuan Zhou, Charu Aggarwal, Hao Peng, Wenbin Hu, Edwin Hancock, Pietro Li`o
Traditional approaches to learning a set of graphs tend to rely on hand-crafted features, such as substructures.
1 code implementation • 21 Dec 2022 • Xing Su, Jian Yang, Jia Wu, Yuchen Zhang
In this paper, we construct a dual-layer graph (i. e., the news layer and the user layer) to extract multiple relations of news and users in social networks to derive rich information for detecting fake news.
1 code implementation • 4 Dec 2022 • Usama Tariq, Rizwan Qureshi, Anas Zafar, Danyal Aftab, Jia Wu, Tanvir Alam, Zubair Shah, Hazrat Ali
Furthermore, the model can generate high-quality synthetic brain MRI with a tumor that can limit the small sample size issues.
1 code implementation • 18 Oct 2022 • Fanzhen Liu, Xiaoxiao Ma, Jia Wu, Jian Yang, Shan Xue, Amin Beheshti, Chuan Zhou, Hao Peng, Quan Z. Sheng, Charu C. Aggarwal
To bridge the gaps, this paper devises a novel Data Augmentation-based Graph Anomaly Detection (DAGAD) framework for attributed graphs, equipped with three specially designed modules: 1) an information fusion module employing graph neural network encoders to learn representations, 2) a graph data augmentation module that fertilizes the training set with generated samples, and 3) an imbalance-tailored learning module to discriminate the distributions of the minority (anomalous) and majority (normal) classes.
1 code implementation • 2 Oct 2022 • Yuecen Wei, Xingcheng Fu, Qingyun Sun, Hao Peng, Jia Wu, Jinyan Wang, Xianxian Li
To address this issue, we propose a novel heterogeneous graph neural network privacy-preserving method based on a differential privacy mechanism named HeteDP, which provides a double guarantee on graph features and topology.
no code implementations • 4 Sep 2022 • Jiaqian Ren, Lei Jiang, Hao Peng, Lingjuan Lyu, Zhiwei Liu, Chaochao Chen, Jia Wu, Xu Bai, Philip S. Yu
Integrating multiple online social networks (OSNs) has important implications for many downstream social mining tasks, such as user preference modelling, recommendation, and link prediction.
2 code implementations • 9 Aug 2022 • Ruitong Zhang, Hao Peng, Yingtong Dou, Jia Wu, Qingyun Sun, Jingyi Zhang, Philip S. Yu
DBSCAN is widely used in many scientific and engineering fields because of its simplicity and practicality.
1 code implementation • 15 Jun 2022 • Sheng Zhou, Hongjia Xu, Zhuonan Zheng, Jiawei Chen, Zhao Li, Jiajun Bu, Jia Wu, Xin Wang, Wenwu Zhu, Martin Ester
Motivated by the tremendous success of deep learning in clustering, one of the most fundamental machine learning tasks, and the large number of recent advances in this direction, in this paper we conduct a comprehensive survey on deep clustering by proposing a new taxonomy of different state-of-the-art approaches.
no code implementations • 31 May 2022 • Ge Zhang, Jia Wu, Jian Yang, Shan Xue, Wenbin Hu, Chuan Zhou, Hao Peng, Quan Z. Sheng, Charu Aggarwal
To frame this survey, we propose a systematic taxonomy covering GLNNs upon deep neural networks, graph neural networks, and graph pooling.
no code implementations • 24 May 2022 • Jiaqian Ren, Lei Jiang, Hao Peng, Zhiwei Liu, Jia Wu, Philip S. Yu
To incorporate temporal information into the message passing scheme, we introduce a novel temporal-aware aggregator which assigns weights to neighbours according to an adaptive time exponential decay formula.
no code implementations • 24 Apr 2022 • Yutong Qu, Wei Emma Zhang, Jian Yang, Lingfei Wu, Jia Wu
Knowledge-aware methods have boosted a range of natural language processing applications over the last decades.
1 code implementation • 15 Apr 2022 • Chuang Liu, Yibing Zhan, Chang Li, Bo Du, Jia Wu, Wenbin Hu, Tongliang Liu, DaCheng Tao
Graph neural networks have emerged as a leading architecture for many graph-level tasks such as graph classification and graph generation with a notable improvement.
no code implementations • 18 Mar 2022 • Xusheng Zhao, Jia Wu, Hao Peng, Amin Beheshti, Jessica J. M. Monaghan, David Mcalpine, Heivet Hernandez-Perez, Mark Dras, Qiong Dai, Yangyang Li, Philip S. Yu, Lifang He
Modern neuroimaging techniques, such as diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), enable us to model the human brain as a brain network or connectome.
1 code implementation • 3 Mar 2022 • JianXin Li, Xingcheng Fu, Qingyun Sun, Cheng Ji, Jiajun Tan, Jia Wu, Hao Peng
In this paper, we proposed a novel Curvature Graph Generative Adversarial Networks method, named \textbf{\modelname}, which is the first GAN-based graph representation method in the Riemannian geometric manifold.
no code implementations • 14 Feb 2022 • Beihang Song, Jing Li, Shan Xue, Jun Chang, Jia Wu, Jun Wan, Tianpeng Liu
In this study, we developed a single-stage rotating object detector via two points with a solar corona heatmap (ROTP) to detect oriented objects.
1 code implementation • 16 Dec 2021 • Qingyun Sun, JianXin Li, Hao Peng, Jia Wu, Xingcheng Fu, Cheng Ji, Philip S. Yu
Graph Neural Networks (GNNs) have shown promising results on a broad spectrum of applications.
1 code implementation • 18 Nov 2021 • Xiang Bai, Hanchen Wang, Liya Ma, Yongchao Xu, Jiefeng Gan, Ziwei Fan, Fan Yang, Ke Ma, Jiehua Yang, Song Bai, Chang Shu, Xinyu Zou, Renhao Huang, Changzheng Zhang, Xiaowu Liu, Dandan Tu, Chuou Xu, Wenqing Zhang, Xi Wang, Anguo Chen, Yu Zeng, Dehua Yang, Ming-Wei Wang, Nagaraj Holalkere, Neil J. Halin, Ihab R. Kamel, Jia Wu, Xuehua Peng, Xiang Wang, Jianbo Shao, Pattanasak Mongkolwat, Jianjun Zhang, Weiyang Liu, Michael Roberts, Zhongzhao Teng, Lucian Beer, Lorena Escudero Sanchez, Evis Sala, Daniel Rubin, Adrian Weller, Joan Lasenby, Chuangsheng Zheng, Jianming Wang, Zhen Li, Carola-Bibiane Schönlieb, Tian Xia
Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses.
1 code implementation • 15 Oct 2021 • Xingcheng Fu, JianXin Li, Jia Wu, Qingyun Sun, Cheng Ji, Senzhang Wang, Jiajun Tan, Hao Peng, Philip S. Yu
Hyperbolic Graph Neural Networks(HGNNs) extend GNNs to hyperbolic space and thus are more effective to capture the hierarchical structures of graphs in node representation learning.
no code implementations • 12 Sep 2021 • Libing Wu, Min Wang, Dan Wu, Jia Wu
Then, to efficiently utilize the historical state information of the intersection, we design a sequence model with the temporal convolutional network (TCN) to capture the historical information and further merge it with the spatial information to improve its performance.
no code implementations • 23 Aug 2021 • Qian Li, Shu Guo, Jia Wu, JianXin Li, Jiawei Sheng, Lihong Wang, Xiaohan Dong, Hao Peng
It ignores meaningful associations among event types and argument roles, leading to relatively poor performance for less frequent types/roles.
1 code implementation • 6 Aug 2021 • Jiaqian Ren, Hao Peng, Lei Jiang, Jia Wu, Yongxin Tong, Lihong Wang, Xu Bai, Bo wang, Qiang Yang
Experiments on both synthetic and real-world datasets show the framework to be highly effective at detection in both multilingual data and in languages where training samples are scarce.
no code implementations • 5 Jul 2021 • Qian Li, JianXin Li, Jiawei Sheng, Shiyao Cui, Jia Wu, Yiming Hei, Hao Peng, Shu Guo, Lihong Wang, Amin Beheshti, Philip S. Yu
Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey.
1 code implementation • 30 Jun 2021 • Pingjun Chen, Muhammad Aminu, Siba El Hussein, Joseph D. Khoury, Jia Wu
In the end, we built global graphs to abstract spatial interaction patterns and extract features for disease diagnosis.
1 code implementation • 23 Jun 2021 • Qian Li, Hao Peng, JianXin Li, Jia Wu, Yuanxing Ning, Lihong Wang, Philip S. Yu, Zheng Wang
Our approach leverages knowledge of the already extracted arguments of the same sentence to determine the role of arguments that would be difficult to decide individually.
no code implementations • 22 Jun 2021 • Liwei Zhang, Yule Zhang, Jia Wu, Xiantao Xiao
We present a computable stochastic approximation type algorithm, namely the stochastic linearized proximal method of multipliers, to solve this convex stochastic optimization problem.
2 code implementations • 14 Jun 2021 • Xiaoxiao Ma, Jia Wu, Shan Xue, Jian Yang, Chuan Zhou, Quan Z. Sheng, Hui Xiong, Leman Akoglu
In this survey, we aim to provide a systematic and comprehensive review of the contemporary deep learning techniques for graph anomaly detection.
no code implementations • 10 Jun 2021 • Yang Guo, Tarique Anwar, Jian Yang, Jia Wu
As the process should be socially and economically profitable, the task of vehicle dispatching is highly challenging, specially due to the time-varying travel demands and traffic conditions.
no code implementations • 26 May 2021 • Xing Su, Shan Xue, Fanzhen Liu, Jia Wu, Jian Yang, Chuan Zhou, Wenbin Hu, Cecile Paris, Surya Nepal, Di Jin, Quan Z. Sheng, Philip S. Yu
A community reveals the features and connections of its members that are different from those in other communities in a network.
1 code implementation • 26 Feb 2021 • Xixun Lin, Jia Wu, Chuan Zhou, Shirui Pan, Yanan Cao, Bin Wang
In this paper, we develop a novel meta-learning recommender called task-adaptive neural process (TaNP).
2 code implementations • 21 Jan 2021 • Yuwei Cao, Hao Peng, Jia Wu, Yingtong Dou, JianXin Li, Philip S. Yu
The complexity and streaming nature of social messages make it appealing to address social event detection in an incremental learning setting, where acquiring, preserving, and extending knowledge are major concerns.
1 code implementation • 20 Jan 2021 • Qingyun Sun, JianXin Li, Hao Peng, Jia Wu, Yuanxing Ning, Phillip S. Yu, Lifang He
Graph representation learning has attracted increasing research attention.
no code implementations • 3 Jan 2021 • Di Jin, Zhizhi Yu, Pengfei Jiao, Shirui Pan, Dongxiao He, Jia Wu, Philip S. Yu, Weixiong Zhang
We conclude with discussions of the challenges of the field and suggestions of possible directions for future research.
1 code implementation • NeurIPS 2020 • Haibo Wang, Chuan Zhou, Xin Chen, Jia Wu, Shirui Pan, Jilong Wang
Graph Neural Networks (GNNs) have achieved remarkable performance in the task of the semi-supervised node classification.
no code implementations • 20 Nov 2020 • Chaochao Chen, Jamie Cui, Guanfeng Liu, Jia Wu, Li Wang
In this paper, to fill this gap, we summarize the open problems for privacy preserving KG in data isolation setting and propose possible solutions for them.
1 code implementation • NeurIPS 2020 • Shichao Zhu, Shirui Pan, Chuan Zhou, Jia Wu, Yanan Cao, Bin Wang
To utilize the strength of both Euclidean and hyperbolic geometries, we develop a novel Geometry Interaction Learning (GIL) method for graphs, a well-suited and efficient alternative for learning abundant geometric properties in graph.
no code implementations • 14 Sep 2020 • Feng Zhu, Yan Wang, Chaochao Chen, Guanfeng Liu, Mehmet Orgun, Jia Wu
Therefore, finding an accurate mapping of the latent factors across domains or systems is crucial to enhancing recommendation accuracy.
1 code implementation • 19 Jun 2020 • Pinghua Xu, Wenbin Hu, Jia Wu, Weiwei Liu
However, the practical significance of the existing studies on this subject is limited for two reasons.
Social and Information Networks Computer Science and Game Theory J.4
1 code implementation • 10 Jun 2020 • Qi Huang, Junshuai Yu, Jia Wu, Bin Wang
A meta-path based heterogeneous graph attention network framework is proposed to capture the global semantic relations of text contents, together with the global structure information of source tweet propagations for rumor detection.
no code implementations • 25 May 2020 • Chaochao Chen, Jun Zhou, Longfei Zheng, Huiwen Wu, Lingjuan Lyu, Jia Wu, Bingzhe Wu, Ziqi Liu, Li Wang, Xiaolin Zheng
Recently, Graph Neural Network (GNN) has achieved remarkable progresses in various real-world tasks on graph data, consisting of node features and the adjacent information between different nodes.
1 code implementation • 17 May 2020 • Fanzhen Liu, Shan Xue, Jia Wu, Chuan Zhou, Wenbin Hu, Cecile Paris, Surya Nepal, Jian Yang, Philip S. Yu
As communities represent similar opinions, similar functions, similar purposes, etc., community detection is an important and extremely useful tool in both scientific inquiry and data analytics.
1 code implementation • 18 Jun 2019 • Anfeng Cheng, Chuan Zhou, Hong Yang, Jia Wu, Lei LI, Jianlong Tan, Li Guo
Due to the expensive costs of labeling anchor users for training prediction models, we consider in this paper the problem of minimizing the number of user pairs across multiple networks for labeling as to improve the accuracy of the prediction.
1 code implementation • 26 Mar 2019 • Stephen Baek, Yusen He, Bryan G. Allen, John M. Buatti, Brian J. Smith, Ling Tong, Zhiyu Sun, Jia Wu, Maximilian Diehn, Billy W. Loo, Kristin A. Plichta, Steven N. Seyedin, Maggie Gannon, Katherine R. Cabel, Yusung Kim, Xiaodong Wu
Here we show that CNN trained to perform the tumor segmentation task, with no other information than physician contours, identify a rich set of survival-related image features with remarkable prognostic value.
no code implementations • 19 Aug 2018 • Pan Xiao, Bo Du, Jia Wu, Lefei Zhang, Ruimin Hu, Xuelong. Li
Many classic methods solve the domain adaptation problem by establishing a common latent space, which may cause the loss of many important properties across both domains.
no code implementations • 14 Jan 2018 • Chenglong Dai, Jia Wu, Dechang Pi, Lin Cui
Brain Electroencephalography (EEG) classification is widely applied to analyze cerebral diseases in recent years.
no code implementations • 5 Jan 2018 • Qinxue Meng, Jia Wu, John Ellisy, Paul J. Kennedy
One is that after a certain number of generations, different islands may retain quite similar, converged sub-populations thereby losing diversity and decreasing efficiency.
no code implementations • 5 Jan 2017 • Cheng-Jun Li, Jia Wu
In this paper, a scheme of setting the success rate of migration based on subpopulation diversity at each interval is proposed.
no code implementations • 20 Dec 2016 • Haishuai Wang, Jia Wu, Peng Zhang, Chengqi Zhang
For example, social network users are considered to be social sensors that continuously generate social signals (tweets) represented as a time series.
no code implementations • 12 Dec 2016 • Dongkuan Xu, Jia Wu, Wei zhang, Yingjie Tian
To the end, we propose a positive instance detection via graph updating for multiple instance learning, called PIGMIL, to detect TPI accurately.
no code implementations • PACLIC 2015 • Yiou Lin, Hang Lei, Jia Wu, Xiaoyu Li
In this article, how word embeddings can be used as features in Chinese sentiment classification is presented.