Login | Cas Login
English| 简体中文| 繁体中文

Home>Scholars

  • Complex
  • Title
  • Keyword
  • Abstract
  • Scholars
  • Journal
  • ISSN
  • Conference
成果搜索

Advanced Search

吴伶

副教授（高校）

计算机与大数据学院、软件学院

0000-0001-5293-8701

AAH-8238-2020

吴伶，福州大学副教授，硕士生导师。CCF会员，CCF协同计算专委，中国优选法统筹法与经济数学研究会灰色系统专业委员会理事、CCF YOCSEF福州学术AC。以第一作者或通讯作者在Applied Intelligence、Computer Communications、Electronics和Journal of Grey System等国内外重要刊物和国际会议共计发表了15篇论文，主要研究方向为数据挖掘和智能信息处理、机器学习、灰色方法和算法设计与分析。主持了1项国家自然科学基金、1项福建省自然科学基金项目、1项福建省教育厅中青年项目和1项福州大学科研启动项目。

展开

Total Results： 46

High Impact Results & Cited Count Trend for Year Keyword Cloud and Partner Relationship

Query：

所有字段：(空)

Refining：

Year

Submit Unfold

Type

Submit

Indexed by

Submit Unfold

Source

Submit Unfold

Complex

Submit Unfold

Former Name

Submit

Co-

Submit Unfold

Language

Submit

Clean All

Sort by：
Default
  • Default
  • Title
  • Year
  • WOS Cited Count
  • Impact factor
  • Ascending
  • Descending
< Page ，Total 4 >
Tracking dynamic community evolution based on Social Relevance and Strong Events SCIE
期刊论文 | 2025 | KNOWLEDGE AND INFORMATION SYSTEMS
Wu, Ling | Xie, Xiaohua | Chen, Chengkai | Yang, Yingjie | Guo, Kun Unfold
Abstract&Keyword Cite

Abstract ：

Although incremental methods are widely used in community detection, their error accumulation problem remains unresolved. Additionally, current methods typically identify events only after community detection has been completed for all time snapshots, lacking consideration of the impact of events on community structure during evolution. Therefore, this paper proposes a framework called Tracking dynamic community evolution based on Social Relevance and Strong Events(TranSiEnt). For the first time, TranSiEnt integrates evolution event identification with dynamic community updating, classifying evolution events into ordinary events and Strong Events based on the influence of the relevant communities. During dynamic community updating, TranSiEnt employs a path diffusion strategy to determine core nodes for community detection, establishing the initial community structure. Using an incremental approach, the framework expands the influence range of incremental nodes in communities experiencing Strong Events. It again conducts precise community detection on all affected nodes to reduce error accumulation, ultimately optimizing community partitioning. TranSiEnt was subjected to objective accuracy experiments on real and synthetic datasets, using modularity, NMI, and EMA as performance evaluation metrics. T-tests were used to verify the significance of the performance improvement of the TranSiEnt algorithm. The experimental results show that TranSiEnt performs better in dynamic community detection and evolution event tracking, significantly improving over existing methods.

Keyword ：

Dynamic community detection Dynamic community detection Social Relevance Social Relevance Strong Event Strong Event

Cite：

Copy from the list or Export to your reference management。

GB/T 7714 Wu, Ling , Xie, Xiaohua , Chen, Chengkai et al. Tracking dynamic community evolution based on Social Relevance and Strong Events [J]. | KNOWLEDGE AND INFORMATION SYSTEMS , 2025 .
MLA Wu, Ling et al. "Tracking dynamic community evolution based on Social Relevance and Strong Events" . | KNOWLEDGE AND INFORMATION SYSTEMS (2025) .
APA Wu, Ling , Xie, Xiaohua , Chen, Chengkai , Yang, Yingjie , Guo, Kun . Tracking dynamic community evolution based on Social Relevance and Strong Events . | KNOWLEDGE AND INFORMATION SYSTEMS , 2025 .
Export to NoteExpress RIS BibTex

Version ：

UGCM-LU: A Unified Stream and Batch Graph Computing Model with Local Update for Community Detection EI
会议论文 | 2025 , 2343 CCIS , 266-280 | 19th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024
Li, Hong | Wu, Ling | Guo, Kun
Abstract&Keyword Cite Version(1)

Abstract ：

Unified stream and batch computing (USBC) aims to incorporate stream and batch computation into a unified framework, thereby enabling the development of a one-stop solution for stream and batch data processing and enhancing the generalization of the framework. However, research on unified graph computing models (UGCMs) faces several challenges. First, existing UGCMs need to consider all graph information in the cache during the incremental update phase, thus leading to decreased execution efficiency. Second, existing UGCMs use fixed bytes to store nodes without considering the actual space occupied by nodes resulting in wasted memory when dealing with large graphs. This paper proposes a UGCM with Local Updates for community detection (UGCM-LU). We first implement a local update strategy to consider partial information of the graph to achieve incremental updates. Secondly, we also designed a byte-compression-based module to store graph data according to the space occupied by nodes. The experimental results show the effectiveness and efficiency of the model in real-world and artificial networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Keyword ：

Batch data processing Batch data processing Lutetium alloys Lutetium alloys

Cite：

Copy from the list or Export to your reference management。

GB/T 7714 Li, Hong , Wu, Ling , Guo, Kun . UGCM-LU: A Unified Stream and Batch Graph Computing Model with Local Update for Community Detection [C] . 2025 : 266-280 .
MLA Li, Hong et al. "UGCM-LU: A Unified Stream and Batch Graph Computing Model with Local Update for Community Detection" . (2025) : 266-280 .
APA Li, Hong , Wu, Ling , Guo, Kun . UGCM-LU: A Unified Stream and Batch Graph Computing Model with Local Update for Community Detection . (2025) : 266-280 .
Export to NoteExpress RIS BibTex

Version ：

UGCM-LU: A Unified Stream and Batch Graph Computing Model with Local Update for Community Detection Scopus
其他 | 2025 , 2343 CCIS , 266-280 | Communications in Computer and Information Science
Li, H. | Wu, L. | Guo, K.
Community-Aware Heterogeneous Graph Contrastive Learning EI
会议论文 | 2025 , 2343 CCIS , 251-265 | 19th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024
Li, Xinying | Wu, Ling | Guo, Kun
Abstract&Keyword Cite Version(1)

Abstract ：

Recently, heterogeneous graph contrastive learning, which can mine supervision signals from the data, has attracted widespread attention. However, most existing methods employ random data augmentation strategies to construct contrastive views, which may destroy the semantic information in heterogeneous graphs. Moreover, they often select positive and negative samples based solely on node-level proximity and overlook hard samples that are difficult to distinguish from anchors. To solve the above problems, we propose a Community-Aware Heterogeneous Graph Contrastive Learning model called CAHGCL. In particular, we design an adaptive data augmentation strategy to construct views, including feature augmentation and topology augmentation. To improve the quality of samples, we propose a dynamic sample weighting strategy based on node similarity and community information, capable of identifying both hard positive samples and hard negative samples. Finally, we introduce community-level contrast to improve community cohesion. Extensive experiments and analyses demonstrate that CAHGCL consistently outperforms state-of-the-art baselines on three datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Keyword ：

Adversarial machine learning Adversarial machine learning Contrastive Learning Contrastive Learning Federated learning Federated learning Knowledge graph Knowledge graph Self-supervised learning Self-supervised learning

Cite：

Copy from the list or Export to your reference management。

GB/T 7714 Li, Xinying , Wu, Ling , Guo, Kun . Community-Aware Heterogeneous Graph Contrastive Learning [C] . 2025 : 251-265 .
MLA Li, Xinying et al. "Community-Aware Heterogeneous Graph Contrastive Learning" . (2025) : 251-265 .
APA Li, Xinying , Wu, Ling , Guo, Kun . Community-Aware Heterogeneous Graph Contrastive Learning . (2025) : 251-265 .
Export to NoteExpress RIS BibTex

Version ：

Community-Aware Heterogeneous Graph Contrastive Learning Scopus
其他 | 2025 , 2343 CCIS , 251-265 | Communications in Computer and Information Science
Li, X. | Wu, L. | Guo, K.
Heterogeneous Information Network Embedding Based on Adaptive Meta-Schema Considering Relation Distinction and Semantic Preservation EI
会议论文 | 2025 , 2343 CCIS , 47-63 | 19th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024
Wu, Ling | Gao, Pingping | Lu, Jinlu | Guo, Kun | Zhang, Qishan Unfold
Abstract&Keyword Cite Version(1)

Abstract ：

Many real-world networks can be treated as heterogeneous information networks (HINs) that consist of various types of nodes, like different proteins and molecules in biological networks and different authors and papers in citation networks. Multiple network data mining tasks can be conducted on HINs to capture the complex relationships between multi-type nodes. In recent years, random walk based HIN embedding has drawn increasing attention. Furthermore, the meta-path or meta-graph guided random walk is one of the most widely used techniques in HIN embedding methods. However, existing HIN embedding methods still face several difficulties. Firstly, the meta-paths or meta-graphs often need to be predefined, which relies heavily on domain knowledge and incomplete information coverage. Secondly, these methods treat all relations without distinction, which inevitably limits the capability of HIN embedding. Thirdly, they do not focus on preserving finer-grained meta-graph semantics. In this paper, a HIN embedding algorithm based on adaptive meta-schema considering relation distinction and semantic preservation (HINEAS) is proposed. In order to avoid the selection of meta-paths or meta-graphs, an adaptive meta-schema extraction is designed. In heterogeneous node sequence generation, a biased random walk strategy based on the adaptive meta-schema is presented to embed the different relationships’ influence. Finally, an enhanced embedding strategy based on semantic preservation of the adaptive meta-schema is proposed to effectively extract topology and preserve the meta-graph’s fine-grained semantics. Experiments on real-world datasets show that HINEAS significantly outperforms state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Keyword ：

Graph embeddings Graph embeddings Graph theory Graph theory Network embeddings Network embeddings

Cite：

Copy from the list or Export to your reference management。

GB/T 7714 Wu, Ling , Gao, Pingping , Lu, Jinlu et al. Heterogeneous Information Network Embedding Based on Adaptive Meta-Schema Considering Relation Distinction and Semantic Preservation [C] . 2025 : 47-63 .
MLA Wu, Ling et al. "Heterogeneous Information Network Embedding Based on Adaptive Meta-Schema Considering Relation Distinction and Semantic Preservation" . (2025) : 47-63 .
APA Wu, Ling , Gao, Pingping , Lu, Jinlu , Guo, Kun , Zhang, Qishan . Heterogeneous Information Network Embedding Based on Adaptive Meta-Schema Considering Relation Distinction and Semantic Preservation . (2025) : 47-63 .
Export to NoteExpress RIS BibTex

Version ：

Heterogeneous Information Network Embedding Based on Adaptive Meta-Schema Considering Relation Distinction and Semantic Preservation Scopus
其他 | 2025 , 2343 CCIS , 47-63 | Communications in Computer and Information Science
Wu, L. | Gao, P. | Lu, J. | Guo, K. | Zhang, Q. Unfold
ABCD-HN: An Artificial Network Benchmark for Community Detection on Heterogeneous Networks EI
会议论文 | 2024 , 2012 , 182-194 | 18th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023
Liu, Junjie | Guo, Kun | Wu, Ling
Abstract&Keyword Cite Version(1)

Abstract ：

Community detection is essential for identifying cohesive groups in complex networks. Artificial benchmarks are critical for evaluating community detection algorithms, offering controlled environments with known community structures. However, existing benchmarks mainly focus on homogeneous networks and overlook the unique characteristics of heterogeneous networks. This paper proposes a novel artificial benchmark, called ABCD-HN (Artificial Network Benchmark for Community Detection on Heterogeneous Networks), for community detection in heterogeneous networks. This benchmark enables the generation of artificial heterogeneous networks with controllable community quantity, node quantity, and community complexity. Additionally, an evaluation framework for artificial heterogeneous networks is proposed to assess their effectiveness. Experimental results demonstrate the effectiveness and usability of ABCD-HN as a benchmark for artificial heterogeneous networks. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Keyword ：

Complex networks Complex networks Heterogeneous networks Heterogeneous networks Population dynamics Population dynamics

Cite：

Copy from the list or Export to your reference management。

GB/T 7714 Liu, Junjie , Guo, Kun , Wu, Ling . ABCD-HN: An Artificial Network Benchmark for Community Detection on Heterogeneous Networks [C] . 2024 : 182-194 .
MLA Liu, Junjie et al. "ABCD-HN: An Artificial Network Benchmark for Community Detection on Heterogeneous Networks" . (2024) : 182-194 .
APA Liu, Junjie , Guo, Kun , Wu, Ling . ABCD-HN: An Artificial Network Benchmark for Community Detection on Heterogeneous Networks . (2024) : 182-194 .
Export to NoteExpress RIS BibTex

Version ：

ABCD-HN: An Artificial Network Benchmark for Community Detection on Heterogeneous Networks Scopus
其他 | 2024 , 2012 , 182-194 | Communications in Computer and Information Science
Liu, J. | Guo, K. | Wu, L.
人工智能与增强现实应用于本科教育
期刊论文 | 2024 , (4) , 131-134 | 科学咨询
吴伶 | 李小燕 | 陈志华 | 钟展良 Unfold
Abstract&Keyword Cite Version(1)

Abstract ：

云计算与物联网安全课程是信息安全专业本科生的必修课,培养学生运用所学的云计算与物联网技术分析和解决问题.本教学创新成果报告围绕3个课堂教学真实问题:一是学生多学科交叉基础知识不足;二是学生解决实际问题和实践能力不足;三是存在产学落差,学生所学技术无法符合产业需求.并且分别提出3个教学方案解决对应的课堂教学真实问题:一是开发"AI助教"APP,以增强现实(AR)和人工智能(AI)语音问答协助学生的学习过程,结合创新性;二是引入心率带、脑波仪、机器人等设备,强化学生的自主学习动机和学习习惯,培养学生解决问题的思维能力,提升高阶性;三是结合"码云"分享开源代码,由企业下载和评价,增加挑战度.

Keyword ：

人工智能 人工智能 信息教育 信息教育 物联网 物联网

Cite：

Copy from the list or Export to your reference management。

GB/T 7714 吴伶 , 李小燕 , 陈志华 et al. 人工智能与增强现实应用于本科教育 [J]. | 科学咨询 , 2024 , (4) : 131-134 .
MLA 吴伶 et al. "人工智能与增强现实应用于本科教育" . | 科学咨询 4 (2024) : 131-134 .
APA 吴伶 , 李小燕 , 陈志华 , 钟展良 . 人工智能与增强现实应用于本科教育 . | 科学咨询 , 2024 , (4) , 131-134 .
Export to NoteExpress RIS BibTex

Version ：

人工智能与增强现实应用于本科教育
期刊论文 | 2024 , 4 (02) , 131-134 | 科学咨询
吴伶 | 李小燕 | 陈志华 | 钟展良 Unfold
Community Detection with Deep Attention Autoencoder Based on Subspace Constraints EI
会议论文 | 2024 , 95-98 | 4th International Conference on Neural Networks, Information and Communication Engineering, NNICE 2024
Cai, Ziqi | Chen, Jianguo | Wu, Ling
Abstract&Keyword Cite Version(1)

Abstract ：

Subspace clustering, known for its effectiveness in handling high-dimensional data, has attracted attention. And the autoencoder can discover hidden features within a large dataset, yet it faces challenges in utilizing attribute information for reconstruction and capturing complex spatial structural information. To tackle these issues, we propose a community detection algorithm named Deep Attention Autoencoder Based on Subspace Constraints (DAASC). First, we design an attribute topology fusion strategy to integrate attribute information into the reconstruction of the decoder. Then, we design a subspace autoencoder strategy, using the concept of subspaces to construct the loss function, to capture the spatial structural information of the data. Experiments conducted on both real-world and synthetic networks to compare DAASC with several state-of-the-art community detection algorithms demonstrate its exceptional accuracy and robustness. © 2024 IEEE.

Keyword ：

Clustering algorithms Clustering algorithms Complex networks Complex networks Deep learning Deep learning Large datasets Large datasets Learning systems Learning systems Population dynamics Population dynamics Signal detection Signal detection

Cite：

Copy from the list or Export to your reference management。

GB/T 7714 Cai, Ziqi , Chen, Jianguo , Wu, Ling . Community Detection with Deep Attention Autoencoder Based on Subspace Constraints [C] . 2024 : 95-98 .
MLA Cai, Ziqi et al. "Community Detection with Deep Attention Autoencoder Based on Subspace Constraints" . (2024) : 95-98 .
APA Cai, Ziqi , Chen, Jianguo , Wu, Ling . Community Detection with Deep Attention Autoencoder Based on Subspace Constraints . (2024) : 95-98 .
Export to NoteExpress RIS BibTex

Version ：

Community Detection with Deep Attention Autoencoder Based on Subspace Constraints Scopus
其他 | 2024 , 95-98 | 2024 4th International Conference on Neural Networks, Information and Communication Engineering, NNICE 2024
Cai, Z. | Chen, J. | Wu, L.
Game Theory-Based Fastest Route Plan Method for Transportation Network Applications EI
会议论文 | 2024 | 6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024
Liu, Zheng-Tan | Wu, Ling | Chen, Chi-Hua
Abstract&Keyword Cite Version(1)

Abstract ：

This paper presents a vehicle route planning method based on game theory principles and innovative utility functions. By addressing the complexities of real-Time traffic congestion, the proposed framework offers a dynamic allocation strategy for rational decision-making. The utility function, which inte-grates traffic flow volume, road capacity, and congestion effects, provides accurate travel time estimations. Mathematical analysis and validation, including genetic algorithms, underscore the framework's robustness. Equilibrium solutions reveal allocation strategies responsive to varying road conditions. Comparative scenarios demonstrate the utility function's effectiveness in guiding enterprises' decisions. This research extends beyond static models, envisioning a future of data integration, multi-objective optimization, adaptive learning, and eco-friendly navigation. By converging these routes, the paper sets the stage for a smarter, more sustainable transportation landscape. © 2024 IEEE.

Cite：

Copy from the list or Export to your reference management。

GB/T 7714 Liu, Zheng-Tan , Wu, Ling , Chen, Chi-Hua . Game Theory-Based Fastest Route Plan Method for Transportation Network Applications [C] . 2024 .
MLA Liu, Zheng-Tan et al. "Game Theory-Based Fastest Route Plan Method for Transportation Network Applications" . (2024) .
APA Liu, Zheng-Tan , Wu, Ling , Chen, Chi-Hua . Game Theory-Based Fastest Route Plan Method for Transportation Network Applications . (2024) .
Export to NoteExpress RIS BibTex

Version ：

Game Theory-Based Fastest Route Plan Method for Transportation Network Applications Scopus
其他 | 2024 | 2024 International Conference on Emerging Smart Computing and Informatics, ESCI 2024
Liu, Z.-T. | Wu, L. | Chen, C.-H.
Unsupervised Multi-population Evolutionary Algorithm for Community Detection in Attributed Networks EI
会议论文 | 2024 , 2012 , 152-166 | 18th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023
Wu, Junjie | Wu, Lin | Guo, Kun
Abstract&Keyword Cite Version(1)

Abstract ：

Community detection on attributed networks is a method to discover community structures within attributed networks. By applying community detection on attribute networks, we can better understand the relationships between nodes in real-world networks. However, current algorithms for community detection on attribute networks rely on hyper-parameters, and it is difficult to obtain an ideal result when facing networks with inconsistent attributes and topology. Consequently, we propose an Unsupervised Multi-population Evolutionary Algorithm (UMEA) for community detection in attributed networks. This algorithm adds edges between nodes based on attribute similarity, allowing it to combine attribute information during the process of community detection. In addition, this algorithm determines the optimal number of added edges autonomously through communication and learning between multiple populations. Furthermore, we propose a series of strategies to accelerate population convergence for the locus-based encoding. Experiments have demonstrated that our algorithm outperforms the benchmark algorithms in both real and artificial networks. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Keyword ：

Data mining Data mining Evolutionary algorithms Evolutionary algorithms Population dynamics Population dynamics

Cite：

Copy from the list or Export to your reference management。

GB/T 7714 Wu, Junjie , Wu, Lin , Guo, Kun . Unsupervised Multi-population Evolutionary Algorithm for Community Detection in Attributed Networks [C] . 2024 : 152-166 .
MLA Wu, Junjie et al. "Unsupervised Multi-population Evolutionary Algorithm for Community Detection in Attributed Networks" . (2024) : 152-166 .
APA Wu, Junjie , Wu, Lin , Guo, Kun . Unsupervised Multi-population Evolutionary Algorithm for Community Detection in Attributed Networks . (2024) : 152-166 .
Export to NoteExpress RIS BibTex

Version ：

Unsupervised Multi-population Evolutionary Algorithm for Community Detection in Attributed Networks Scopus
其他 | 2024 , 2012 , 152-166 | Communications in Computer and Information Science
Wu, J. | Wu, L. | Guo, K.
Incremental Inductive Dynamic Network Community Detection EI
会议论文 | 2024 , 2012 , 93-107 | 18th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023
Wu, Ling | Zhuang, Jiangming | Guo, Kun
Abstract&Keyword Cite Version(1)

Abstract ：

In order to address the problem of reconstruction and retraining time overhead in representation learning processing dynamic networks, this paper proposes an incremental inductive dynamic network community detection algorithm (IINDCD). First, the algorithm uses an attention mechanism to capture node neighborhood information and learn node representations by neighborhood aggregation induction while enhancing low-order structural representations. Second, the design uses random walking to capture high-order information and use it to construct node initial features for input into the attentional autoencoder, which effectively fuses high- and low-order structural features. Finally, the algorithm introduces the ideas of incremental update and model reuse for dynamic representation learning, constructs incremental node sets for updating the model, reduces training overhead, and quickly obtains node representation vectors for new moments of the network, then completing dynamic network community detection. IINDCD runs without reconstruction and with low retraining overhead. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Keyword ：

Learning algorithms Learning algorithms Learning systems Learning systems Population dynamics Population dynamics

Cite：

Copy from the list or Export to your reference management。

GB/T 7714 Wu, Ling , Zhuang, Jiangming , Guo, Kun . Incremental Inductive Dynamic Network Community Detection [C] . 2024 : 93-107 .
MLA Wu, Ling et al. "Incremental Inductive Dynamic Network Community Detection" . (2024) : 93-107 .
APA Wu, Ling , Zhuang, Jiangming , Guo, Kun . Incremental Inductive Dynamic Network Community Detection . (2024) : 93-107 .
Export to NoteExpress RIS BibTex

Version ：

Incremental Inductive Dynamic Network Community Detection Scopus
其他 | 2024 , 2012 , 93-107 | Communications in Computer and Information Science
Wu, L. | Zhuang, J. | Guo, K.
10| 20| 50 per page
< Page ，Total 4 >

Export

Results：

Selected

to

Format：
Online/Total：76/10077874
Address：FZU Library(No.2 Xuyuan Road, Fuzhou, Fujian, PRC Post Code：350116) Contact Us：0591-22865326
Copyright：FZU Library Technical Support：Beijing Aegean Software Co., Ltd. 闽ICP备05005463号-1