Computer Science ›› 2018, Vol. 45 ›› Issue (7): 38-41.doi: 10.11896/j.issn.1002-137X.2018.07.006
• CCF Big Data 2017 • Previous Articles Next Articles
LI Jia-yi1,ZHAO Yu1,WANG Li2
CLC Number:
[1]COYAL A,BONCHI F,LAKSHMANAN L.Learning influence probabilities in social networks[C]∥ACM International Conference on Web Search & Data Mining.ACM,2010:241-250. [2]CRANMER S,DESMARAIS B.Inferential Network Analysiswith Exponential Random Graph Models[J].Political Analysis,2011,19(1):66-86. [3]FAN W.Graph pattern matching revised for social network analysis[C]∥International Conference on Database Theory.ACM,2012:8-21. [4]MIKOLOV T,SUTSKEVER I,CHEN K,et al.Distributed Rep-resentations of Words and Phrases and their Compositiona-lity[J].Advances in Neural Information Processing Systems,2013,26:3111-3119. [5]KUANG L W,HAO F,YANG L,et al.A Tensor-Based Approach for Big Data Representation and Dimensionality Reduction[J].IEEE Transactions on Emerging Topics in Computing,2017,2(3):280-291. [6]CUNNINGHAM J,YU B.Dimensionality reduction for large-scale neural recordings[J].Nature Neuroscience,2014,17(11):1500-1509. [7]MEI Q Z,CAI D,ZHANG D,et al.Topic modeling with net-work regularizations[C]∥Proceedings of the 17th International Conference on World Wide Web.2008:101-110. [8]PEROZZI B,AL-RFOU’ R,SKIENA S.DeepWalk:online lear-ning of social representations[C]∥Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Disco-very and Data Mining.2014:701-710. [9]TANG J,QU M,WANG M,et al.LINE:Large-scale Information Network Embedding[C]∥Proceedings of the 24th International Conference on World Wide Web.2015:1067-1077. [10]GROVER A,LESKOVEC J.node2vec:Scalable Feature Lear-ning for Networks[C]∥Proceedings of International Conference on Knowledge Discovery & Data Mining(KDD).2016:855-864. [11]TU C C,LIU H,LIU Z Y,et al.CANE:Context-Aware Network Embedding for Relation Modeling[C]∥Meeting of the Association for Computational Linguistics.2017:1722-1731. [12]PAN S R,WU J,ZHU X Q,et al.Tri-Party Deep Network Representation[C]∥Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence.2016:1895-1901. [13]HUANG X,LI J D,HU X.Label Informed Attributed Network Embedding[C]∥Proceedings of the Tenth ACM International Conference on Web Search and Data Mining.2017:731-739. [14]YANG C,LIU Z Y,ZHAO D L,et al.Network Representation Learning with Rich Text Information[C]∥International Conference on Artificial Intelligence.AAAI Press,2015:2111-2117. [15]NATARAJAN N,DHILLON I.Inductive matrix completion for predicting gene-disease associations[J].Bioinformatics,2014,30(12):60-68. [16]HOU C P,NIE F P,ZHANG C S,et al.Multiple rank multi-li-near SVM for matrix data classification[J].Pattern Recognition,2014,47(1):454-469. |
[1] | SONG Jie, LIANG Mei-yu, XUE Zhe, DU Jun-ping, KOU Fei-fei. Scientific Paper Heterogeneous Graph Node Representation Learning Method Based onUnsupervised Clustering Level [J]. Computer Science, 2022, 49(9): 64-69. |
[2] | HUANG Li, ZHU Yan, LI Chun-ping. Author’s Academic Behavior Prediction Based on Heterogeneous Network Representation Learning [J]. Computer Science, 2022, 49(9): 76-82. |
[3] | XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai. Temporal Knowledge Graph Representation Learning [J]. Computer Science, 2022, 49(9): 162-171. |
[4] | LI Zong-min, ZHANG Yu-peng, LIU Yu-jie, LI Hua. Deformable Graph Convolutional Networks Based Point Cloud Representation Learning [J]. Computer Science, 2022, 49(8): 273-278. |
[5] | WANG Jian, PENG Yu-qi, ZHAO Yu-fei, YANG Jian. Survey of Social Network Public Opinion Information Extraction Based on Deep Learning [J]. Computer Science, 2022, 49(8): 279-293. |
[6] | HUANG Pu, DU Xu-ran, SHEN Yang-yang, YANG Zhang-jing. Face Recognition Based on Locality Regularized Double Linear Reconstruction Representation [J]. Computer Science, 2022, 49(6A): 407-411. |
[7] | HE Yi-chen, MAO Yi-jun, XIE Xian-fen, GU Wan-rong. Matrix Transformation and Factorization Based on Graph Partitioning by Vertex Separator for Recommendation [J]. Computer Science, 2022, 49(6A): 272-279. |
[8] | WEI Peng, MA Yu-liang, YUAN Ye, WU An-biao. Study on Temporal Influence Maximization Driven by User Behavior [J]. Computer Science, 2022, 49(6): 119-126. |
[9] | YU Ai-xin, FENG Xiu-fang, SUN Jing-yu. Social Trust Recommendation Algorithm Combining Item Similarity [J]. Computer Science, 2022, 49(5): 144-151. |
[10] | CHANG Ya-wen, YANG Bo, GAO Yue-lin, HUANG Jing-yun. Modeling and Analysis of WeChat Official Account Information Dissemination Based on SEIR [J]. Computer Science, 2022, 49(4): 56-66. |
[11] | ZUO Yuan-lin, GONG Yue-jiao, CHEN Wei-neng. Budget-aware Influence Maximization in Social Networks [J]. Computer Science, 2022, 49(4): 100-109. |
[12] | GUO Lei, MA Ting-huai. Friend Closeness Based User Matching [J]. Computer Science, 2022, 49(3): 113-120. |
[13] | SHAO Yu, CHEN Ling, LIU Wei. Maximum Likelihood-based Method for Locating Source of Negative Influence Spreading Under Independent Cascade Model [J]. Computer Science, 2022, 49(2): 204-215. |
[14] | JIANG Zong-li, FAN Ke, ZHANG Jin-li. Generative Adversarial Network and Meta-path Based Heterogeneous Network Representation Learning [J]. Computer Science, 2022, 49(1): 133-139. |
[15] | WANG Ying-li, JIANG Cong-cong, FENG Xiao-nian, QIAN Tie-yun. Time Aware Point-of-interest Recommendation [J]. Computer Science, 2021, 48(9): 43-49. |
|