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Huawei Shen - IEEE Xplore Author Profile

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Author details

Huawei Shen

Also published under: Hua-Wei Shen

Publications

24

Citations

202

Publications by Year

20112024

Co-Authors:

Liang BaiPeng BaoQi CaoKeting CenXue-Qi Cheng

Show All Co-Authors (68)

Huawei Shen

Also published under: Hua-Wei Shen

Affiliation

CAS Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences

University of Chinese Academy of Sciences

Publication Topics

Generative Adversarial Networks,
Graph Convolutional Network,
Graph Neural Network Model,
Graph Neural Networks,
Loss Function,
Node Classification,
Node Representations,
Objective Function,
Pair Of Nodes,
Recurrent Neural Network,
Textual Features,
Validation Set

Biography

Huawei Shen received the PhD degree from the Institute of Computing Technology, in 2010. He is a professor with the Institute of Computing Technology, Chinese Academy of Sciences (CAS). His major research interests include network science, social media analytics, and recommendation. He has published more than 80 papers in prestigious journals and top international conferences, including in Science, PNAS,Physical Review E, WWW, AAAI, IJCAI, SIGIR, CIKM, and WSDM. He is an outstanding member of the Association of Innovation Promotion for Youth of CAS. He received the Top 100 Doctoral Thesis Award of CAS, in 2011 and the Grand Scholarship of the President of CAS, in 2010.(Based on document published on 4 January 2022).

Publications

24

Citations

202

Publications by Year

20112024

Co-Authors:

Liang Bai
Peng Bao
Qi Cao
Keting Cen
Xue-Qi Cheng

Show All Co-Authors (68)

Author's Published Works

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Showing 1-24 of 24 results

Conferences (16)

Journals (7)

Magazines (1)

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Huawei Shen(23)
Xueqi Cheng(16)
Bingbing Xu(4)
Yongqing Wang(3)
Chao Yang(2)
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China(5)
University of Chinese Academy of Sciences, Beijing, China(5)
CAS Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China(4)
Data Intelligence System Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China(2)
University of Chinese Academy of Sciences(2)
IEEE Transactions on Knowledge and Data Engineering(4)
2012 Second International Conference on Cloud and Green Computing(2)
2021 IEEE International Conference on Multimedia and Expo (ICME)(2)
2022 International Joint Conference on Neural Networks (IJCNN)(2)
IEEE Access(2)
IEEE(24)
Padua, Italy(2)
Shenzhen, China(2)
Xiangtan, China(2)
Beijing, China(1)
Busan, Korea (South)(1)
Objective Function(5)
Social Networks(5)
Convolutional Neural Network(3)
Embedding Learning(3)
Generative Adversarial Networks(3)

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TEA: Test-Time Energy Adaptation

Yige Yuan;Bingbing Xu;Liang Hou;Fei Sun;Huawei Shen;Xueqi Cheng

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

Cited by: Papers (1)

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Test Time Adaptation (TTA) aims to improve model generalizability when test data diverges from training distribution, with the distinct advantage of not requiring access to training data and processes, especially valuable in the context of pre-trained models. However, current TTA methods fail to address the fundamental issue: covariate shift, i. e., the decreased generalizability can be attributed...Show More

TEA: Test-Time Energy Adaptation

Yige Yuan;Bingbing Xu;Liang Hou;Fei Sun;Huawei Shen;Xueqi Cheng

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

SACH: Significant-Attributed Community Search in Heterogeneous Information Networks

Yanghao Liu;Fangda Guo;Bingbing Xu;Peng Bao;Huawei Shen;Xueqi Cheng

2024 IEEE 40th International Conference on Data Engineering (ICDE)

Year: 2024 | Conference Paper |

Cited by: Papers (1)

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Community search is a personalized community discovery problem aimed at finding densely-connected subgraphs containing the query vertex. In particular, the search for com-munities with high-importance vertices has recently received a great deal of attention. However, existing works mainly focus on conventional homogeneous networks where vertices are of the same type, but are not applicable to hete...Show More

SACH: Significant-Attributed Community Search in Heterogeneous Information Networks

Yanghao Liu;Fangda Guo;Bingbing Xu;Peng Bao;Huawei Shen;Xueqi Cheng

2024 IEEE 40th International Conference on Data Engineering (ICDE)

Year: 2024 | Conference Paper |

BCC: Bidirectional Consistency Constraint Method for Hierarchical Text Classification

Yinghan Shen;Yu Yan;Dechun Yin;Huawei Shen

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2024 | Conference Paper |

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Hierarchical Text Classification (HTC) is a useful tool for document categorization based on the taxonomic hierarchy. However, current HTC methods treat labels under each category separately, which makes it difficult to model multiple inheritance labels. To solve this problem, we propose the Bidirectional Consistency Constraint (BCC) method. BCC aims to better handle multiple inheritance and class...Show More

BCC: Bidirectional Consistency Constraint Method for Hierarchical Text Classification

Yinghan Shen;Yu Yan;Dechun Yin;Huawei Shen

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2024 | Conference Paper |

Graph Adversarial Immunization for Certifiable Robustness

Shuchang Tao;Qi Cao;Huawei Shen;Yunfan Wu;Liang Hou;Xueqi Cheng

IEEE Transactions on Knowledge and Data Engineering

Year: 2024 | Volume: 36, Issue: 4 | Journal Article |

Cited by: Papers (3)

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Despite achieving great success, graph neural networks (GNNs) are vulnerable to adversarial attacks. Existing defenses focus on developing adversarial training or model modification. In this paper, we propose and formulate graph adversarial immunization, i.e., vaccinating part of graph structure to improve certifiable robustness of graph against any admissible adversarial attack. We first propose ...Show More

Graph Adversarial Immunization for Certifiable Robustness

Shuchang Tao;Qi Cao;Huawei Shen;Yunfan Wu;Liang Hou;Xueqi Cheng

IEEE Transactions on Knowledge and Data Engineering

Year: 2024 | Volume: 36, Issue: 4 | Journal Article |

Time Series Anomaly Detection With Adversarial Reconstruction Networks

Shenghua Liu;Bin Zhou;Quan Ding;Bryan Hooi;Zhengbo Zhang;Huawei Shen;Xueqi Cheng

IEEE Transactions on Knowledge and Data Engineering

Year: 2023 | Volume: 35, Issue: 4 | Journal Article |

Cited by: Papers (43)

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Time series data naturally exist in many domains including medical data analysis, infrastructure sensor monitoring, and motion tracking. However, a very small portion of anomalous time series can be observed, comparing to the whole data. Most existing approaches are based on the supervised classification model requiring representative labels for anomaly class(es), which is challenging in real-worl...Show More

Time Series Anomaly Detection With Adversarial Reconstruction Networks

Shenghua Liu;Bin Zhou;Quan Ding;Bryan Hooi;Zhengbo Zhang;Huawei Shen;Xueqi Cheng

IEEE Transactions on Knowledge and Data Engineering

Year: 2023 | Volume: 35, Issue: 4 | Journal Article |

Coordinate Mapping Method of Crowdsourced Data for Indoor Fingerprint Localization

Zhengyi Li;Wen Li;Dongyan Wei;Hong Yuan;Bingbing Xu;Huawei Shen

2022 IEEE 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN)

Year: 2022 | Conference Paper |

Cited by: Papers (3)

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For the problem that GNSS can't be used for positioning indoors and with the development of computing technology and the deployment widespread of Wi-Fi, Wi-Fi based indoor crowdsourced positioning methods have gradually become a hot spot for research and application. However, compared with professional fingerprint acquisition, crowdsourced data lacks accurate indoor labeled locations, which create...Show More

Coordinate Mapping Method of Crowdsourced Data for Indoor Fingerprint Localization

Zhengyi Li;Wen Li;Dongyan Wei;Hong Yuan;Bingbing Xu;Huawei Shen

2022 IEEE 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN)

Year: 2022 | Conference Paper |

Towards Powerful Graph Contrastive Learning without Negative Examples

Keting Cen;Huawei Shen;Qi Cao;Bingbing Xu;Xueqi Cheng

2022 International Joint Conference on Neural Networks (IJCNN)

Year: 2022 | Conference Paper |

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Contrastive learning gains remarkable success in graph representation learning through imposing two properties to node representations, i.e., aligning representations of similar nodes in different augmented graphs and uniformly distributing node representations on the hypersphere. Existing graph contrastive learning methods capture these two key properties by extensive negative examples, resulting...Show More

Towards Powerful Graph Contrastive Learning without Negative Examples

Keting Cen;Huawei Shen;Qi Cao;Bingbing Xu;Xueqi Cheng

2022 International Joint Conference on Neural Networks (IJCNN)

Year: 2022 | Conference Paper |

Region-based Cross-modal Retrieval

Danyang Hou;Liang Pang;Yanyan Lan;Huawei Shen;Xueqi Cheng

2022 International Joint Conference on Neural Networks (IJCNN)

Year: 2022 | Conference Paper |

Cited by: Papers (1)

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Cross-modal retrieval aims to identify relevant information from different modalities, such as image and text. Existing works build a coarse relationship between whole image and whole text. However, the retrieval in fine-grained elements is more necessary in research and has many practical applications, such as explainable and interactive retrieval. In this paper, we propose a region-based cross-m...Show More

Region-based Cross-modal Retrieval

Danyang Hou;Liang Pang;Yanyan Lan;Huawei Shen;Xueqi Cheng

2022 International Joint Conference on Neural Networks (IJCNN)

Year: 2022 | Conference Paper |

On the Cybernetics of Crowdsourcing Innovation: A Process Model

Lei Lin;Huawei Shen;Shenghua Liu;Li Xu;Xueqi Cheng

Year: 2022 | Volume: 10 | Journal Article |

Cited by: Papers (5)

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For small and medium-sized enterprises and large enterprises alike, crowdsourcing innovation has become an important element of a product’s whole life cycle. It is the open call process of soliciting consumers to harvest and evaluate ideas or other intellectual assets. The previous proposed taxonomic framework for charactering this process is mainly for general crowdsourcing process and summarized...Show More

On the Cybernetics of Crowdsourcing Innovation: A Process Model

Lei Lin;Huawei Shen;Shenghua Liu;Li Xu;Xueqi Cheng

Year: 2022 | Volume: 10 | Journal Article |

MiSTR: A Multiview Structural-Temporal Learning Framework for Rumor Detection

Jianian Li;Peng Bao;Huawei Shen;Xuanya Li

IEEE Transactions on Big Data

Year: 2022 | Volume: 8, Issue: 4 | Journal Article |

Cited by: Papers (7)

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With the rapid development of web technology, social media platforms have become a breeding ground for rumors. These rumors can threaten people’s health, endanger the economy, and affect the stability of a country. In recent years, to mitigate the problem of rumors, computational detection of rumors has been studied, producing some promising early results. However, how to effectively capture the t...Show More

MiSTR: A Multiview Structural-Temporal Learning Framework for Rumor Detection

Jianian Li;Peng Bao;Huawei Shen;Xuanya Li

IEEE Transactions on Big Data

Year: 2022 | Volume: 8, Issue: 4 | Journal Article |

UGCLink: User Identity Linkage by Modeling User Generated Contents with Knowledge Distillation

Hao Gao;Yongqing Wang;Jiangli Shao;Huawei Shen;Xueqi Cheng

2021 IEEE International Conference on Big Data (Big Data)

Year: 2021 | Conference Paper |

Cited by: Papers (5)

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User identity linkage aims to link users with the same identities across different social networks. Recently, re- searchers model the similarities of users’ behaviors such as Point of Interests(PoIs) or User Generated Contents(UGCs) to predict the identities of users. However, it is non-trivial to solve the problem due to the following challenges: 1) PoIs are always sparse in the non-location-base...Show More

UGCLink: User Identity Linkage by Modeling User Generated Contents with Knowledge Distillation

Hao Gao;Yongqing Wang;Jiangli Shao;Huawei Shen;Xueqi Cheng

2021 IEEE International Conference on Big Data (Big Data)

Year: 2021 | Conference Paper |

Learning Content and Context with Language Bias for Visual Question Answering

Chao Yang;Su Feng;Dongsheng Li;Huawei Shen;Guoqing Wang;Bin Jiang

2021 IEEE International Conference on Multimedia and Expo (ICME)

Year: 2021 | Conference Paper |

Cited by: Papers (12)

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Visual Question Answering (VQA) is a challenging multi-modal task to answer questions about an image. Many works concentrate on how to reduce language bias which makes models answer questions ignoring visual content and language context. However, reducing language bias also weakens the ability of VQA models to learn context prior. To address this issue, we propose a novel learning strategy named C...Show More

Learning Content and Context with Language Bias for Visual Question Answering

Chao Yang;Su Feng;Dongsheng Li;Huawei Shen;Guoqing Wang;Bin Jiang

2021 IEEE International Conference on Multimedia and Expo (ICME)

Year: 2021 | Conference Paper |

Multi-Modality Image Manipulation Detection

Chao Yang;Zhiyu Wang;Huawei Shen;Huizhou Li;Bin Jiang

2021 IEEE International Conference on Multimedia and Expo (ICME)

Year: 2021 | Conference Paper |

Cited by: Papers (9)

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State-of-the-art multi-stream methods for image manipulation detection suffer from gaps between features. Moreover, the rapid development of GANs makes it an emerging method of image tampering. However, existing natural scene image tampered datasets are limited to manual tampering. In this paper, we address these two issues. Firstly, we propose a novel two-stream multi-modality image manipulation ...Show More

Multi-Modality Image Manipulation Detection

Chao Yang;Zhiyu Wang;Huawei Shen;Huizhou Li;Bin Jiang

2021 IEEE International Conference on Multimedia and Expo (ICME)

Year: 2021 | Conference Paper |

Truth Discovery by Claim and Source Embedding

Shanshan Lyu;Wentao Ouyang;Yongqing Wang;Huawei Shen;Xueqi Cheng

IEEE Transactions on Knowledge and Data Engineering

Year: 2021 | Volume: 33, Issue: 3 | Journal Article |

Cited by: Papers (17)

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Information gathered from multiple sources on the Web often exhibits conflicts. This phenomenon motivates the need of truth discovery, which aims to automatically find the true claim among multiple conflicting claims. Existing truth discovery methods are mainly based on iterative updates, optimization or probabilistic models. Although these methods have shown their own effectiveness, they have a c...Show More

Truth Discovery by Claim and Source Embedding

Shanshan Lyu;Wentao Ouyang;Yongqing Wang;Huawei Shen;Xueqi Cheng

IEEE Transactions on Knowledge and Data Engineering

Year: 2021 | Volume: 33, Issue: 3 | Journal Article |

GCN-ALP: Addressing Matching Collisions in Anchor Link Prediction

Hao Gao;Yongqing Wang;Shanshan Lyu;Huawei Shen;Xueqi Cheng

2020 IEEE International Conference on Knowledge Graph (ICKG)

Year: 2020 | Conference Paper |

Cited by: Papers (8)

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Nowadays online users prefer to join multiple social media for the purpose of socialized online service. The problem anchor link prediction is formalized to link user data with the common ground on user profile, content and network structure across social networks. Most of the traditional works concentrated on learning matching function with explicit or implicit features on observed user data. How...Show More

GCN-ALP: Addressing Matching Collisions in Anchor Link Prediction

Hao Gao;Yongqing Wang;Shanshan Lyu;Huawei Shen;Xueqi Cheng

2020 IEEE International Conference on Knowledge Graph (ICKG)

Year: 2020 | Conference Paper |

GraphWGAN: Graph Representation Learning with Wasserstein Generative Adversarial Networks

Rong Yan;Huawei Shen;Cao Qi;Keting Cen;Li Wang

2020 IEEE International Conference on Big Data and Smart Computing (BigComp)

Year: 2020 | Conference Paper |

Cited by: Papers (1)

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Graph representation learning aims to represent vertices as low-dimensional and real-valued vectors to facilitate subsequent downstream tasks, i.e., node classification, link predictions. Recently, some novel graph representation learning frameworks, which try to approximate the underlying true connectivity distribution of the vertices, show their superiority. These methods characterize the distan...Show More

GraphWGAN: Graph Representation Learning with Wasserstein Generative Adversarial Networks

Rong Yan;Huawei Shen;Cao Qi;Keting Cen;Li Wang

2020 IEEE International Conference on Big Data and Smart Computing (BigComp)

Year: 2020 | Conference Paper |

ULE: Learning User and Location Embeddings for POI Recommendation

Hao Wang;Wentao Ouyang;Huawei Shen;Xueqi Cheng

2018 IEEE Third International Conference on Data Science in Cyberspace (DSC)

Year: 2018 | Conference Paper |

Cited by: Papers (6)

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Point-of Interest recommendation has become an important application in Location-Based Social Networks. It recommends interesting but unvisited locations for users based on users' historical check-ins and other auxiliary information. To cope with check-ins, existing methods convert them into users' scores on Points-of-interest and fit the scores by users' and locations' latent factors. However, th...Show More

ULE: Learning User and Location Embeddings for POI Recommendation

Hao Wang;Wentao Ouyang;Huawei Shen;Xueqi Cheng

2018 IEEE Third International Conference on Data Science in Cyberspace (DSC)

Year: 2018 | Conference Paper |

Self-Learning and Embedding Based Entity Alignment

Saiping Guan;Xiaolong Jin;Yantao Jia;Yuanzhuo Wang;Huawei Shen;Xueqi Cheng

2017 IEEE International Conference on Big Knowledge (ICBK)

Year: 2017 | Conference Paper |

Cited by: Papers (8)

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Entity alignment aims to identify semantical matchings between entities from different groups. Traditional methods (e.g., attribute comparison based methods, clustering based methods, and active learning methods) are usually supervised by labelled data as prior knowledge. Since it is not trivial to label data for training, researchers have recently turned to unsupervised methods, and have thus dev...Show More

Self-Learning and Embedding Based Entity Alignment

Saiping Guan;Xiaolong Jin;Yantao Jia;Yuanzhuo Wang;Huawei Shen;Xueqi Cheng

2017 IEEE International Conference on Big Knowledge (ICBK)

Year: 2017 | Conference Paper |

An Optimization Model for Clustering Categorical Data Streams with Drifting Concepts

Liang Bai;Xueqi Cheng;Jiye Liang;Huawei Shen

IEEE Transactions on Knowledge and Data Engineering

Year: 2016 | Volume: 28, Issue: 11 | Journal Article |

Cited by: Papers (13)

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There is always a lack of a cluster validity function and optimization strategy to find out clusters and catch the evolution trend of cluster structures on a categorical data stream. Therefore, this paper presents an optimization model for clustering categorical data streams. In the model, a cluster validity function is proposed as the objective function to evaluate the effectiveness of the cluste...Show More

An Optimization Model for Clustering Categorical Data Streams with Drifting Concepts

Liang Bai;Xueqi Cheng;Jiye Liang;Huawei Shen

IEEE Transactions on Knowledge and Data Engineering

Year: 2016 | Volume: 28, Issue: 11 | Journal Article |

Aspect-level opinion mining of online customer reviews

Xueke Xu;Xueqi Cheng;Songbo Tan;Yue Liu;Huawei Shen

China Communications

Year: 2013 | Volume: 10, Issue: 3 | Magazine Article |

Cited by: Papers (48)

This paper focuses on how to improve aspect-level opinion mining for online customer reviews. We first propose a novel generative topic model, the Joint Aspect/Sentiment (JAS) model, to jointly extract aspects and aspect-dependent sentiment lexicons from online customer reviews. An aspect-dependent sentiment lexicon refers to the aspect-specific opinion words along with their aspect-aware sentimen...Show More

Aspect-level opinion mining of online customer reviews

Xueke Xu;Xueqi Cheng;Songbo Tan;Yue Liu;Huawei Shen

China Communications

Year: 2013 | Volume: 10, Issue: 3 | Magazine Article |

Analysis of Competitive Information Dissemination in Social Network Based on Evolutionary Game Model

Jianye Yu;Yuanzhuo Wang;Jingyuan Li;Huawei Shen;Xueqi Cheng

2012 Second International Conference on Cloud and Green Computing

Year: 2012 | Conference Paper |

Cited by: Papers (6)

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We proposed a game-theoretic model for competitive information dissemination in social network. A model is presented to understand human impacts on competitive information dissemination that is described as a utility function that involves several factors shaping human behaviors, such as knowledge, interest, memory, and willingness to learn. The dissemination of information is modeled as the dynam...Show More

Analysis of Competitive Information Dissemination in Social Network Based on Evolutionary Game Model

Jianye Yu;Yuanzhuo Wang;Jingyuan Li;Huawei Shen;Xueqi Cheng

2012 Second International Conference on Cloud and Green Computing

Year: 2012 | Conference Paper |

The Untold Story Behind the Recommendation in Micro-blogging Network

Tong Man;Hua-Wei Shen;Xue-Qi Cheng

2012 Second International Conference on Cloud and Green Computing

Year: 2012 | Conference Paper |

Cited by: Papers (1)

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Celebrity recommendation is widely-adopted by many micro-blogging services as an important way to enhance users' experience and the visibility of contents submitted. It is critically important for celebrity recommendation to understand which factors and how these factors affect the probability that the recommended celebrities will be accepted. In this paper, taking the Tecent Weibo as a case, we t...Show More

The Untold Story Behind the Recommendation in Micro-blogging Network

Tong Man;Hua-Wei Shen;Xue-Qi Cheng

2012 Second International Conference on Cloud and Green Computing

Year: 2012 | Conference Paper |

A White-Box Empirical Study of P2P-VoD Systems: Several Unconventional New Findings

Tieying Zhang;Zhenhua Li;Huawei Shen;Yan Huang;Xueqi Cheng

2011 Proceedings of 20th International Conference on Computer Communications and Networks (ICCCN)

Year: 2011 | Conference Paper |

Cited by: Papers (2)

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P2P-VoD systems have gained tremendous popularity in recent years. While existing research is mostly based on theoretical or conventional assumptions, it is particularly valuable to understand and examine how these assumptions work in realistic environments, so as to set up a solid foundation for mechanism design and optimization possibilities. In this paper, we present a comprehensive measurement...Show More

A White-Box Empirical Study of P2P-VoD Systems: Several Unconventional New Findings

Tieying Zhang;Zhenhua Li;Huawei Shen;Yan Huang;Xueqi Cheng

2011 Proceedings of 20th International Conference on Computer Communications and Networks (ICCCN)

Year: 2011 | Conference Paper |

IEEE Access Special Section Editorial: Advanced Data Mining Methods for Social Computing

Yongqiang Zhao;Shirui Pan;Jia Wu;Huaiyu Wan;Huizhi Liang;Haishuai Wang;Huawei Shen

Year: 2020 | Volume: 8 | Journal Article |

Cited by: Papers (2)

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Various kinds of social networks develop explosively, such as online social networks, scientific cooperation networks, athlete networks, airport passage networks, and so on. With the large number of participants and real-time property, social networks increasingly demonstrate their strength in information dissemination. Social computing has become a promising research area and attracts lots of att...Show More

IEEE Access Special Section Editorial: Advanced Data Mining Methods for Social Computing

Yongqiang Zhao;Shirui Pan;Jia Wu;Huaiyu Wan;Huizhi Liang;Haishuai Wang;Huawei Shen

Year: 2020 | Volume: 8 | Journal Article |

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