[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.
|