山东大学学报 (工学版) ›› 2023, Vol. 53 ›› Issue (2): 51-60.doi: 10.6040/j.issn.1672-3961.0.2022.157
吴艳丽,刘淑薇,何东晓,王晓宝*,金弟
WU Yanli, LIU Shuwei, HE Dongxiao, WANG Xiaobao*, JIN Di
摘要： 为探索节点间链接结构的多种潜在关系并对其进行语义解释,提出一个刻画多种潜在关系的泊松-伽马主题模型,刻画不同潜在关系下节点内容与链接结构(边)的生成过程,利用全期望定律来聚合所有潜在关系中的内容信息与拓扑信息。对于模型推断,进一步提出一种封闭式的吉布斯采样算法。在8个真实数据集上与8种代表性社团发现方法进行比较,并对所有潜在关系中的链接结构进行可视化和案例分析。试验结果表明,本研究方法优于8种代表性的社团发现方法,能够在多种潜在关系中探索节点间链接结构的有效性,还能够利用节点内容来解释链接关系中的语义信息。
中图分类号:
| [1] | ZHOU Q, CAI S M, ZHANG Y C. Parallel heuristic community detection method based on node similarity[J]. IEEE Access, 2019, 7: 184145-184159. |
| [2] | NEWMAN M E J. Equivalence between modularity optimization and maximum likelihood methods for community detection[J]. Physical Review E, 2016, 94(5): 052315. |
| [3] | LI Y, HE K, BINDEL D, et al. Uncovering the small community structure in large networks: a local spectral approach[C] //Proceedings of the 24th International Conference on World Wide Web. WWW'15. Florence, Italy: International World Wide Web Conferences Steering Committee, 2015: 658-668. |
| [4] | YANG B, LIU X, LI Y, et al. Stochastic blockmodeling and variational Bayes learning for signed network analysis[J]. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(9):2026-2039. |
| [5] | LI P Z, HUANG L, WANG C D, et al. Community detection by motif-aware label propagation[J]. ACM Transactions on Knowledge Discovery from Data(TKDD), 2020, 14(2): 1-19. |
| [6] | BLEI D M, NG A Y, JORDAN M I. Latent Dirichlet allocation[J]. Journal of Machine Learning Research, 2003, 3(5):993-1022. |
| [7] | CHANG J, BLEI D M. Relational topic models for document networks[C] //Proceedings of the 12th International Conference on Artificial Intelligence and Statistics(AISTATS). Clearwater Beach, Florida, USA: PMLR, 2009: 81-88. |
| [8] | BAVOTA G, OLIVETO R, GETHERS M, et al. Methodbook: recommending move method refactorings via relational topic models[J]. IEEE Transactions on Software Engineering, 2013, 40(7): 671-694. |
| [9] | SACHAN M, CONTRACTOR D, FARUQUIE T A, et al. Using content and interactions for discovering communities in social networks[C] //Proceedings of the 21st International Conference on World Wide Web. New York, USA: Association for Computing Machinery, 2012: 331-340. |
| [10] | RANGANATH R, TANG L, CHARLIN L, et al. Deep exponential families[C] //Artificial Intelligence and Statistics. San Diego, California, USA: PMLR, 2015: 762-771. |
| [11] | GAN Z, CHEN C, HENAO R, et al. Scalable deep Poisson factor analysis for topic modeling[C] //Proceedings of the 32nd International Conference on Machine Learning. Lille, France: JMLR.org, 2015: 1823-1832. |
| [12] | WANG C, CHEN B, XIAO S, et al. Convolutional Poisson gamma belief network[C] //International Conference on Machine Learning. Long Beach, California, USA: PMLR, 2019: 6515-6525. |
| [13] | WANG C, ZHANG H, CHEN B, et al. Deep relational topic modeling via graph Poisson gamma belief network[J]. Advances in Neural Information Processing Systems, 2020, 33: 488-500. |
| [14] | CAI D, HE X, HAN J, et al. Graph regularized nonnegative matrix factorization for data representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 33(8): 1548-1560. |
| [15] | CHANG J, BLEI D M. Hierarchical relational models for document networks[J]. The Annals of Applied Statistics, 2010, 4(1): 124-150. |
| [16] | ZHOU M. Infinite edge partition models for overlapping community detection and link prediction[C] //Artificial Intelligence and Statistics. San Diego, California, USA: PMLR, 2015: 1135-1143. |
| [17] | ZHOU M, CONG Y, CHEN B. Augmentable gamma belief networks[J]. The Journal of Machine Learning Research, 2016, 17(1): 5656-5699. |
| [18] | ZHOU M, CONG Y, CHEN B. The Poisson gamma belief network[J]. Advances in Neural Information Processing Systems, 2015, 28: 562-570. |
| [19] | SEN P, NAMATA G, BILGIC M, et al. Collective classification in network data[J]. AI Magazine, 2008, 29(3): 93-106. |
| [20] | KARRER B, NEWMAN M E J. Stochastic blockmodels and community structure in networks[J]. Physical Review E, 2011, 83(1): 016107. |
| [21] | BALASUBRAMANYAN R, COHEN W W. Block-LDA: jointly modeling entity-annotated text and entity-entity links[C] //Proceedings of the 2011 SIAM International Conference on Data Mining. Mesa, Arizona, USA: Society for Industrial and Applied Mathematics, 2011: 450-461. |
| [22] | YANG T, JIN R, CHI Y, et al. Combining link and content for community detection: a discriminative approach[C] //Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: Association for Computing Machinery, 2009: 927-936. |
| [23] | WANG X, JIN D, CAO X, et al. Semantic community identification in large attribute networks[C] //Proceedings of the AAAI Conference on Artificial Intelligence. Palo Alto, California, USA: AAAI Press, 2016: 265-271. |
| [24] | HE D, SONG W, JIN D, et al. An end-to-end community detection model: integrating lda into markov random field via factor graph[C] //International Joint Confer-ences on Artifical Intelligence(IJCAI). Macao, China: IJCAI, 2019: 5730-5736. |
| [25] | ZHANG G, JIN D, GAO J, et al. Finding communities with hierarchical semantics by distinguishing general and specialized topics[C] //Proceedings of the 27th International Joint Conference on Artificial Intelligence. Stockholm, Sweden: AAAI Press, 2018: 3648-3654. |
| [26] | LIU H, WU Z, LI X, et al. Constrained nonnegative matrix factorization for image representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 34(7): 1299-1311. |
| [27] | CANTADOR I, BRUSILOVSKY P, KUFLIK T. Second workshop on information heterogeneity and fusion in recommender systems(HetRec2011)[C] //Proceedings of the Fifth ACM Conference on Recommender Systems. New York, USA: Association for Computing Machinery, 2011: 387-388. |
| [28] | HE D, FENG Z, JIN D, et al. Joint identification of network communities and semantics via integrative modeling of network topologies and node contents[C] //Thirty-First AAAI Conference on Artificial Intelligence. San Francisco, California, USA: AAAI Press, 2017: 116-124. |
| [1] | 宋佳芮,陈艳平,王凯,黄瑞章,秦永彬. 基于Affix-Attention的命名实体识别语义补充方法[J]. 山东大学学报 (工学版), 2023, 53(2): 70-76. |
| [2] | 李旭涛,杨寒玉,卢业飞,张玮. 基于深度学习的遥感图像道路分割[J]. 山东大学学报 (工学版), 2022, 52(6): 139-145. |
| [3] | 孙志巍,宋明阳,潘泽华,景丽萍. 上下文感知的判别式主题模型[J]. 山东大学学报 (工学版), 2022, 52(4): 131-138. |
| [4] | 尹旭,刘兆英,张婷,李玉鑑. 基于弱监督和半监督学习的红外舰船分割方法[J]. 山东大学学报 (工学版), 2022, 52(2): 99-106. |
| [5] | 胡军,杨冬梅,刘立,钟福金. 融合节点状态信息的跨社交网络用户对齐[J]. 山东大学学报 (工学版), 2021, 51(6): 49-58. |
| [6] | 曹春红,段鸿轩,曹玲,张乐乐,胡凯,肖芬. 基于多级特征级联的遥感图像实时语义分割[J]. 山东大学学报 (工学版), 2021, 51(2): 19-25. |
| [7] | 段江丽,胡新. 自然语言问答中的语义关系识别[J]. 山东大学学报 (工学版), 2020, 50(3): 1-7. |
| [8] | 孔令龙,田国会. 智能家庭中一种基于本体的机器人服务认知机制[J]. 山东大学学报 (工学版), 2019, 49(6): 45-54. |
| [9] | 周杨浩,刘一帆,李瑮. 一种自动读取指针式仪表读数的方法[J]. 山东大学学报 (工学版), 2019, 49(4): 1-7. |
| [10] | 何奕江,杜军平,寇菲菲,梁美玉,王巍,罗盎. 基于深度卷积神经网络的图像自编码算法[J]. 山东大学学报 (工学版), 2019, 49(2): 61-66. |
| [11] | 张红斌,邱蝶蝶,邬任重,朱涛,滑瑾,姬东鸿. 基于极端梯度提升树算法的图像属性标注[J]. 山东大学学报 (工学版), 2019, 49(2): 8-16. |
| [12] | 朱映雪,黄瑞章,马灿. 一种具有新主题偏向性的短文本动态聚类方法[J]. 山东大学学报 (工学版), 2018, 48(6): 8-18. |
| [13] | 李广丽,刘斌,朱涛,殷依,张红斌. 基于优选典型相关分量的跨媒体检索模型[J]. 山东大学学报 (工学版), 2018, 48(5): 38-46. |
| [14] | 林江豪,周咏梅,阳爱民,陈锦. 基于词向量的领域情感词典构建[J]. 山东大学学报(工学版), 2018, 48(3): 40-47. |
| [15] | 闫盈盈,黄瑞章,王瑞,马灿,刘博伟,黄庭. 一种长文本辅助短文本的文本理解方法[J]. 山东大学学报(工学版), 2018, 48(3): 67-74. |
|
