计算机科学 ›› 2022, Vol. 49 ›› Issue (1): 89-94.doi: 10.11896/jsjkx.210100023
所属专题： 大数据&数据科学 虚拟专题
蒲实, 赵卫东
PU Shi, ZHAO Wei-dong
摘要： 科研网络是一类动态变化的异构信息网络,科研网络上的社区检测能挖掘出学术主体的所属社区并发现蕴含于科研社区中的洞察。既有的社区检测算法忽略了科研网络的动态特征和科研主体间的特殊关系,未将科研社区内部的紧密程度和社区间的关系纳入社区检测算法中予以优化,对此提出了一种基于动态科研网络表示学习的社区检测算法DANE-CD。首先基于科研网络自编码器学习科研网络中学术主体的表示向量,然后创新性地在表示学习过程中融入了基于模块度和团队断裂带两个维度的聚类优化,最后基于堆栈自编码器构造了动态科研网络表示学习模型,同时完成了对科研网络的社区检测。在DBLP和HEP-TH两个真实科研数据集上进行了实验,实验结果显示算法在准确率、归一化互信息和模块度3个指标上优于既有科研社区检测算法,可以较好地完成动态科研网络下的社区检测任务。
中图分类号:
| [1]KONG X,SHI Y,YU S,et al.Academic social networks:Mode-ling,analysis,mining and applications[J].Journal of Network and Computer Applications,2019,132:86-103. [2]CHEN P,REDNER S.Community structure of the physical review citation network[J].Journal of Informetrics,2010,4(3):278-290. [3]GIRVAN M,NEWMAN M E J.Community structure in social and biological networks[J].Proceedings of the National Academy of Sciences of the United States of America,2002,99(12):7821-7826. [4]NEWMAN M E J.Fast algorithm for detecting communitystructure in networks[J].Physical Review E,2004,69(6):066133. [5]BLONDEL V D,GUILLAUME J L,LAMBIOTTE R,et al.Fast unfolding of communities in large networks[J].Journal of Statistical Mechanics:Theory and Experiment,2008,2008(10):P10008. [6]COMBE D,LARGERON C,EGYED-ZSIGMOND E,et al.Com-bining Relations and Text in Scientific Network Clustering[C]//Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.Istanbul,Turkey:IEEE,2012:1248-1253. [7]BARONI A,CONTE A,PATRIGNANI M,et al.EfficientlyClustering Very Large Attributed Graphs [C]//Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.Sydney,Australia:ACM,2017:369-376. [8]BHATT S,PADHEE S,SHETH A,et al.Knowledge GraphEnhanced Community Detection and Characterization [C]//Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining.Melbourne VIC,Australia:ACM,2019:51-59. [9]CHUNAEV P.Community detection in node-attributed socialnetworks:A survey[J].Computer Science Review,2020,37:100286. [10]YU P S,LIU F,XUE S,et al.Deep Learning for CommunityDetection:Progress,Challenges and Opportunities [C]//Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence.Yokohama,Japan:IJCAI Organization,2020,5:4981-4987. [11]CAVALLARI S,ZHENG V W,CAI H,et al.Learning Community Embedding with Community Detection and Node Embedding on Graphs [C]//Proceedings of the 26th ACM on International Conference on Information and Knowledge Management.Singapore:ACM,2017:377-386. [12]TU C,ZENG X,WANG H,et al.A Unified Framework forCommunity Detection and Network Representation Learning[J].IEEE Transactions on Knowledge and Data Engineering,2019,31(6):1051-1065. [13]LIU F,WU J,XUE S,et al.Detecting the evolving community structure in dynamic social networks[J].World Wide Web,2020,23(2):715-733. [14]GOYAL P,CHHETRI S R,CANEDO A.dyngraph2vec:Capturing network dynamics using dynamic graph representation learning[J].Knowledge-Based Systems,2020,187:104816. [15]DONG Y,CHAWLA N V,SWAMI A.Metapath2Vec:Scalable Representation Learning for Heterogeneous Networks [C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Halifax,NS,Canada:ACM,2017:135-144. [16]LE Q,MIKOLOV T.Distributed Representations of Sentences and Documents [C]//Proceedings of the 31st International Conference on Machine Learning.Beijing,China:PMLR,2014,32:1188-1196. [17]NEWMAN M E J.Modularity and community structure in networks[J].Proceedings of the National Academy of Sciences of the United States of America,2006,103(23):8577-8582. [18]YANG L,CAO X,HE D,et al.Modularity Based Community Detection with Deep Learning [C]//Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence.New York,USA:IJCAI/AAAI Press,2016:2252-2258. [19]THATCHER S M B,PATEL P C.Group Faultlines:A Review,Integration,and Guide to Future Research[J].Journal of Ma-nagement,2012,38(4):969-1009. [20]ZHAO W,PU S,JIANG D.A human resource allocation method for business processes using team faultlines[J].Applied Intelligence,2020,50(9):2887-2900. [21]TANG J,ZHANG J,YAO L,et al.ArnetMiner:Extraction and Mining of Academic Social Networks [C]//Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Las Vegas,Nevada,USA:ACM,2008:990-998. [22]SINHA A,SHEN Z,SONG Y,et al.An Overview of Microsoft Academic Service (MAS) and Applications [C]//Proceedings of the 24th International Conference on World Wide Web.Flo-rence,Italy:ACM,2015:243-246. [23]ROSSI R A,AHMED N K.The network data repository with interactive graph analytics and visualization [C]//Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence.Texas,USA:AAAI Press,2015:4292-4293. |
| [1] | 黄丽, 朱焱, 李春平. 基于异构网络表征学习的作者学术行为预测 Author’s Academic Behavior Prediction Based on Heterogeneous Network Representation Learning 计算机科学, 2022, 49(9): 76-82. https://doi.org/10.11896/jsjkx.210900078 |
| [2] | 杨林, 王永杰. 蚁群算法在动态网络持续性路径预测中的运用及仿真 Application and Simulation of Ant Colony Algorithm in Continuous Path Prediction of Dynamic Network 计算机科学, 2021, 48(6A): 485-490. https://doi.org/10.11896/jsjkx.200800132 |
| [3] | 程云飞, 田红心, 刘祖军. NOMA系统异构网络中联合用户关联和功率控制协同优化 Collaborative Optimization of Joint User Association and Power Control in NOMA Heterogeneous Network 计算机科学, 2021, 48(3): 269-274. https://doi.org/10.11896/jsjkx.191100213 |
| [4] | 肖勇, 金鑫, 冯俊豪. 一种适用于电力异构通信的链路速率跨层匹配机制 Cross-layer Matching Mechanism of Link Communication Rate for Heterogeneous Communication in Power System 计算机科学, 2021, 48(11A): 495-499. https://doi.org/10.11896/jsjkx.200500113 |
| [5] | 曾德泽, 李跃鹏, 赵宇阳, 顾琳. 基于强化学习的高能效基站动态调度方法 Reinforcement Learning Based Dynamic Basestation Orchestration for High Energy Efficiency 计算机科学, 2021, 48(11): 363-371. https://doi.org/10.11896/jsjkx.201000008 |
| [6] | 薛磊, 唐旭清. 基于中心团的重叠社区检测算法 Algorithm for Detecting Overlapping Communities Based on Centered Cliques 计算机科学, 2020, 47(8): 157-163. https://doi.org/10.11896/jsjkx.200300034 |
| [7] | 富坤, 仇倩, 赵晓梦, 高金辉. 基于节点演化分阶段优化的事件检测方法 Event Detection Method Based on Node Evolution Staged Optimization 计算机科学, 2020, 47(5): 96-102. https://doi.org/10.11896/jsjkx.190400072 |
| [8] | 赵卫绩,张凤斌,刘井莲. 复杂网络社区发现研究进展 Review on Community Detection in Complex Networks 计算机科学, 2020, 47(2): 10-20. https://doi.org/10.11896/jsjkx.190100214 |
| [9] | 杨旭华,沈敏. 基于特征向量局部相似性的社区检测算法 Community Detection Algorithm Based on Local Similarity of Feature Vectors 计算机科学, 2020, 47(2): 58-64. https://doi.org/10.11896/jsjkx.181202433 |
| [10] | 方旭愿, 田红心, 孙德春, 杜文丛, 祁婷. 基于绿色能源感知的效用函数异构网络接入算法 Utility Function Heterogeneous Network Access Algorithm Based on Green Energy Perception 计算机科学, 2019, 46(8): 127-132. https://doi.org/10.11896/j.issn.1002-137X.2019.08.021 |
| [11] | 张建安. 基于移动切换认证的分层异构网络中的用户敏感信息隐藏方法 Users’ Sensitive Information Hiding Method in Hierarchical Heterogeneous Network Based on Mobile Switching Authentication 计算机科学, 2019, 46(3): 217-220. https://doi.org/10.11896/j.issn.1002-137X.2019.03.032 |
| [12] | 张绘娟, 张达敏, 闫威, 陈忠云, 辛梓芸. 异构网络中基于吞吐量优化的资源分配机制 Throughput Optimization Based Resource Allocation Mechanism in Heterogeneous Networks 计算机科学, 2019, 46(10): 109-115. https://doi.org/10.11896/jsjkx.180901787 |
| [13] | 庄陵,尹耀虎. 认知异构网络中基于不完全频谱感知的资源分配算法 Resource Allocation Algorithm for Cognitive Heterogeneous Networks Based on Imperfect Spectrum Sensing 计算机科学, 2018, 45(5): 49-53. https://doi.org/10.11896/j.issn.1002-137X.2018.05.008 |
| [14] | 王振朝,侯欢欢,连蕊. 抑制CMT中乱序程度的路径优化方案 Path Optimization Scheme for Restraining Degree of Disorder in CMT 计算机科学, 2018, 45(4): 122-125. https://doi.org/10.11896/j.issn.1002-137X.2018.04.019 |
| [15] | 付立东,聂靖靖. 基于进化谱分方法的动态社团检测 Dynamic Community Detection Based on Evolutionary Spectral Method 计算机科学, 2018, 45(2): 171-174. https://doi.org/10.11896/j.issn.1002-137X.2018.02.030 |
|
||
首页