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Xiaoran Yan

Publications
12
Citations
80
Publications by Year
20162025
Co-Authors:
Show All Co-Authors (59)
Image of author Xiaoran Yan

Xiaoran Yan

Affiliation

Research Institute of Artificial Intelligence
Zhejiang Lab
Hangzhou, China

Publication Topics

Biography

Xiaoran Yan (Member, IEEE) received the BS degree from Zhejiang University, China, in 2007 and the PhD degree from the University of New Mexico, Albuquerque, NM, USA, in 2013, all in computer science. From 2013 to 2015, he was a postdoctoral research associate with the University of Southern California Information Sciences Institute. From 2015 to 2021, he was an assistant research scientist with the Indiana University Network Science Institute. In 2021, he joined the Research Institute of Artificial Intelligence, Zhejiang Lab. His research interests include network science, statistical machine learning, data privacy and their trans-disciplinary applications in social, communications and bio-medical networks. He has co-directed NIH and IMLS ...
Publications
12
Citations
80
Publications by Year
20162025
Co-Authors:
Show All Co-Authors (59)
Author's Published Works

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Oh-FedRec: One-Shot and Heterogeneous Vertical Federated Recommendation System

Xiangjie Kong;Xiaohua He;Xulin Ma;Xiaoran Yan;Lingyun Wang;Guojiang Shen;Zhi Liu

IEEE Transactions on Consumer Electronics
Year: 2025 | Early Access Article |
Federated learning has a wide range of applications in recommendation systems, but most federated recommendation systems can only achieve federated communication between users and servers. Only a few are vertical federated recommendation systems, achieving federated server communication. In addition, the current federated recommendation frameworks require that each participant have the same model....Show More

Oh-FedRec: One-Shot and Heterogeneous Vertical Federated Recommendation System

Xiangjie Kong;Xiaohua He;Xulin Ma;Xiaoran Yan;Lingyun Wang;Guojiang Shen;Zhi Liu

Year: 2025 | Early Access Article |

Enhancing Human Action Recognition with Fine-grained Body Movement Attention

Rui Zhang;Junxiao Xue;Feng Lin;Qing Zhang;Pavel Smirnov;Xiao Ma;Xiaoran Yan

2024 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2024 | Conference Paper |
In the field of vision-language models (VLMs), human action recognition models, while effective, always rely on large pre-trained models or high-resolution inputs, leading to computational challenges. To address this, we propose a novel VLM approach with fine-grained attention to body movements. Unlike methods relying on coarse video-text matching, we guide the model to infer actions from fine-gra...Show More

Enhancing Human Action Recognition with Fine-grained Body Movement Attention

Rui Zhang;Junxiao Xue;Feng Lin;Qing Zhang;Pavel Smirnov;Xiao Ma;Xiaoran Yan

Year: 2024 | Conference Paper |

Point-Correlate Adversarial Transformer for Unsupervised Multivariate Time Series Anomaly Detection

Huan Li;Xiangjie Kong;Guojiang Shen;Xiaoran Yan;Yao Yang;Mario Collotta

2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)
Year: 2024 | Conference Paper |
Multivariate time series anomaly detection plays a crucial role in industrial production. However, the inherent complexity and randomness of time series pose significant challenges. Furthermore, existing detection methods struggle to provide reliable explanations for outliers. To address these issues, this paper presents an unsupervised multivariate time series anomaly detection model named Point-...Show More

Point-Correlate Adversarial Transformer for Unsupervised Multivariate Time Series Anomaly Detection

Huan Li;Xiangjie Kong;Guojiang Shen;Xiaoran Yan;Yao Yang;Mario Collotta

Year: 2024 | Conference Paper |

Federated Graph Anomaly Detection via Contrastive Self-Supervised Learning

Xiangjie Kong;Wenyi Zhang;Hui Wang;Mingliang Hou;Xin Chen;Xiaoran Yan;Sajal K. Das

IEEE Transactions on Neural Networks and Learning Systems
Year: 2024 | Early Access Article |
Cited by: Papers (5)
Attribute graph anomaly detection aims to identify nodes that significantly deviate from the majority of normal nodes, and has received increasing attention due to the ubiquity and complexity of graph-structured data in various real-world scenarios. However, current mainstream anomaly detection methods are primarily designed for centralized settings, which may pose privacy leakage risks in certain...Show More

Federated Graph Anomaly Detection via Contrastive Self-Supervised Learning

Xiangjie Kong;Wenyi Zhang;Hui Wang;Mingliang Hou;Xin Chen;Xiaoran Yan;Sajal K. Das

Year: 2024 | Early Access Article |

FVFL: A Flexible and Verifiable Privacy-Preserving Federated Learning Scheme

Gang Wang;Li Zhou;Qingming Li;Xiaoran Yan;Ximeng Liu;Yuncheng Wu

IEEE Internet of Things Journal
Year: 2024 | Volume: 11, Issue: 13 | Journal Article |
Cited by: Papers (2)
With the development of deep learning, people are more and more concerned about the security of data. Federated learning can solve the problem of data island, but it also brings more serious data privacy problems. Furthermore, in the process of multisource data collaboration, the efficiency of the whole federated learning system is usually not high. In this article, we introduce a scheme named FVF...Show More

FVFL: A Flexible and Verifiable Privacy-Preserving Federated Learning Scheme

Gang Wang;Li Zhou;Qingming Li;Xiaoran Yan;Ximeng Liu;Yuncheng Wu

Year: 2024 | Volume: 11, Issue: 13 | Journal Article |

Z-Laplacian Matrix Factorization: Network Embedding With Interpretable Graph Signals

Liangtian Wan;Zhengqiang Fu;Yi Ling;Yuchen Sun;Xiaona Li;Lu Sun;Feng Xia;Xiaoran Yan;Charu C. Aggarwal

IEEE Transactions on Knowledge and Data Engineering
Year: 2024 | Volume: 36, Issue: 8 | Journal Article |
Network embedding aims to represent nodes with low dimensional vectors while preserving structural information. It has been recently shown that many popular network embedding methods can be transformed into matrix factorization problems. In this paper, we propose the unifying framework “Z-NetMF,” which generalizes random walk samplers to Z-Laplacian graph filters, leading to embedding algorithms w...Show More

Z-Laplacian Matrix Factorization: Network Embedding With Interpretable Graph Signals

Liangtian Wan;Zhengqiang Fu;Yi Ling;Yuchen Sun;Xiaona Li;Lu Sun;Feng Xia;Xiaoran Yan;Charu C. Aggarwal

Year: 2024 | Volume: 36, Issue: 8 | Journal Article |

Investigations on Scientific Literature Meta Information Extraction Using Large Language Models

Menghao Guo;Fan Wu;Jinling Jiang;Xiaoran Yan;Guangyong Chen;Wenhui Li;Yunhong Zhao;Zeyi Sun

2023 IEEE International Conference on Knowledge Graph (ICKG)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
The meta information in scientific literature including article title, author, institutions, year, journal, etc., plays a critical role in providing useful information to research peers. Traditional meta information extraction methods usually rely on rules and templates. Recently, due to the booming of Large Language Models (LLMs), its application in scientific literature meta-information extracti...Show More

Investigations on Scientific Literature Meta Information Extraction Using Large Language Models

Menghao Guo;Fan Wu;Jinling Jiang;Xiaoran Yan;Guangyong Chen;Wenhui Li;Yunhong Zhao;Zeyi Sun

Year: 2023 | Conference Paper |

DPVisCreator: Incorporating Pattern Constraints to Privacy-preserving Visualizations via Differential Privacy

Jiehui Zhou;Xumeng Wang;Jason K. Wong;Huanliang Wang;Zhongwei Wang;Xiaoyu Yang;Xiaoran Yan;Haozhe Feng;Huamin Qu;Haochao Ying;Wei Chen

IEEE Transactions on Visualization and Computer Graphics
Year: 2023 | Volume: 29, Issue: 1 | Journal Article |
Cited by: Papers (9)
Data privacy is an essential issue in publishing data visualizations. However, it is challenging to represent multiple data patterns in privacy-preserving visualizations. The prior approaches target specific chart types or perform an anonymization model uniformly without considering the importance of data patterns in visualizations. In this paper, we propose a visual analytics approach that facili...Show More

DPVisCreator: Incorporating Pattern Constraints to Privacy-preserving Visualizations via Differential Privacy

Jiehui Zhou;Xumeng Wang;Jason K. Wong;Huanliang Wang;Zhongwei Wang;Xiaoyu Yang;Xiaoran Yan;Haozhe Feng;Huamin Qu;Haochao Ying;Wei Chen

Year: 2023 | Volume: 29, Issue: 1 | Journal Article |

Detecting Outlier Patterns With Query-Based Artificially Generated Searching Conditions

Shuo Yu;Feng Xia;Yuchen Sun;Tao Tang;Xiaoran Yan;Ivan Lee

IEEE Transactions on Computational Social Systems
Year: 2021 | Volume: 8, Issue: 1 | Journal Article |
Cited by: Papers (25)
In the age of social computing, finding interesting network patterns or motifs is significant and critical for various areas, such as decision intelligence, intrusion detection, medical diagnosis, social network analysis, fake news identification, and national security. However, subgraph matching remains a computationally challenging problem, let alone identifying special motifs among them. This i...Show More

Detecting Outlier Patterns With Query-Based Artificially Generated Searching Conditions

Shuo Yu;Feng Xia;Yuchen Sun;Tao Tang;Xiaoran Yan;Ivan Lee

Year: 2021 | Volume: 8, Issue: 1 | Journal Article |

Efficient network navigation with partial information

Xiaoran Yan;Olaf Sporns;Andrea Avena-Koenigsberger

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)
Year: 2020 | Conference Paper |
We propose a information theoretical framework to capture transition and information costs of network navigation models. Based on the minimum description length principle and the Markov decision process, we demonstrate that efficient global navigation can be achieved with only partial information. Additionally, we derived a scalable algorithm for optimal solutions under certain conditions. The pro...Show More

Efficient network navigation with partial information

Xiaoran Yan;Olaf Sporns;Andrea Avena-Koenigsberger

Year: 2020 | Conference Paper |

Graph Filters and the Z-Laplacian

Xiaoran Yan;Brian M. Sadler;Robert J. Drost;Paul L. Yu;Kristina Lerman

IEEE Journal of Selected Topics in Signal Processing
Year: 2017 | Volume: 11, Issue: 6 | Journal Article |
Cited by: Papers (15)
In network science, the interplay between dynamical processes and the underlying topologies of complex systems has led to a diverse family of models with different interpretations. In graph signal processing, this is manifested in the form of different graph shifts and their induced algebraic systems. In this paper, we propose the unifying Z-Laplacian framework, whose instances can act as graph sh...Show More

Graph Filters and the Z-Laplacian

Xiaoran Yan;Brian M. Sadler;Robert J. Drost;Paul L. Yu;Kristina Lerman

Year: 2017 | Volume: 11, Issue: 6 | Journal Article |

Bayesian model selection of stochastic block models

Xiaoran Yan

2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
Year: 2016 | Conference Paper |
Cited by: Papers (22)
A central problem in analyzing networks is partitioning them into modules or communities. One of the best tools for this is the stochastic block model, which clusters vertices into blocks with statistically homogeneous pattern of links. Despite its flexibility and popularity, there has been a lack of principled statistical model selection criteria for the stochastic block model. Here we propose a ...Show More

Bayesian model selection of stochastic block models

Xiaoran Yan

Year: 2016 | Conference Paper |

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