肖杰斌, 张绍武.基于随机游走和增量相关节点的动态网络社团挖掘算法[J]. 电子与信息学报. 2013, 35(4): 977-981.
|
Xiao Jie-bin and Zhang Shao-wu. An algorithm of integrating random walk and increment correlative vertexes for mining community of dynamic networks[J]. Journal of Electronics Information Technology, 2013, 35(4): 977-981.
|
陈季梦, 陈家俊, 刘杰, 等. 基于结构相似度的大规模社交网络聚类算法[J]. 电子与信息学报. 2015, 37(2): 449-454.
|
Chen Ji-meng, Chen Jia-jun, Liu Jie, et al.. Clustering algorithms for large-scale social networks based on structural similarity[J]. Journal of Electronics Information Technology, 2015, 37(2): 449-454.
|
Sun Y and Han J. Mining heterogeneous information networks: principles and methodologies[J]. Proceedings of Mining Heterogeneous Information Networks: Principles and Methodologies, 2012, 3(2): 1-159.
|
Huang Y and Gao X. Clustering on heterogeneous networks [J]. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2014, 4(3): 213-233.
|
Gao B, Liu T Y, Zheng X, et al.. Consistent bipartite graph co-partitioning for star-structured high-order heterogeneous data co-clustering[C]. Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, Chicago, 2005: 41-50.
|
Gao B, Liu T, and Ma W-Y. Star-structured high-order heterogeneous data co-clustering based on consistent information theory[C]. Proceedings of the 6th International Conference on Data Mining (ICDM 2006), Hong Kong, 2006: 880-884.
|
Long B, Zhang Z M, Wu X, et al.. Spectral clustering for multi-type relational data[C]. Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, 2006: 585-592.
|
Sun Y, Yu Y, and Han J. Ranking-based clustering of heterogeneous information networks with star network schema[C]. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, 2009: 797-806.
|
Li P, Wen J, and Li X. SNTClus: a novel service clustering algorithm based on network analysis and service tags[J]. Przeglad Elektrotechniczny, 2013, 89(1): 208-210.
|
Li P, Chen L, Li X, et al.. RNRank: Network-Based Ranking on Relational Tuples[M]. Boston: Behavior and Social Computing, Springer International Publishing, 2013: 139-150.
|
Wang R, Shi C, Philip S Y, et al.. Integrating Clustering and Ranking on Hybrid Heterogeneous Information Network[M]. Berlin: Advances in Knowledge Discovery and Data Mining, Springer Berlin Heidelberg, 2013: 583-594.
|
Boden B, Ester M, and Seidl T. Density-Based Subspace Clustering in Heterogeneous Networks[M]. Berlin: Machine Learning and Knowledge Discovery in Databases, Springer Berlin Heidelberg, 2014: 149-164.
|
Meng Q, Tafavogh S, and Kennedy P J. Community detection on heterogeneous networks by multiple semantic- path clustering[C]. 2014 6th IEEE International Conference on Computational Aspects of Social Networks (CASoN), Porto, 2014: 7-12.
|
Meng X, Shi C, Li Y, et al.. Relevance Measure in Large-scale Heterogeneous Networks[M]. Boston: Web Technologies and Applications, Springer International Publishing, 2014: 636-643.
|
Aggarwal C C, Xie Y, and Philip S Y. On dynamic link inference in heterogeneous networks[C]. SIAM International Conference on Data?Mining, Anaheim, 2012: 415-426.
|
Khoa N L D and Chawla S. Large Scale Spectral Clustering Using Resistance Distance and Spielman-teng Solvers[M]. Berlin: Discovery Science, Springer Berlin Heidelberg, 2012: 7-21.
|
Spielman D A and Teng S H. Nearly-linear time algorithms for graph partitioning, graph sparsification, and solving linear systems[C]. Proceedings of the 36th Annual ACM Symposium on Theory of Computing, Chicago, 2004: 81-90.
|
Spielman D A and Teng S H. Nearly linear time algorithms for preconditioning and solving symmetric, diagonally dominant linear systems[J]. SIAM Journal on Matrix Analysis and Applications, 2014, 35(3): 835-885.
|
Fouss F, Pirotte A, Renders J M, et al.. Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2007, 19(3): 355-369.
|
Spielman D A and Srivastava N. Graph sparsification by effective resistances[J]. SIAM Journal on Computing, 2011, 40(6): 1913-1926.
|
Achlioptas D. Database-friendly random projections[C]. Proceedings of the 20th ACM Sigmod-Sigact-Sigart Symposium on Principles of Database Systems, New York, 2001: 274-281.
|
Koutis I, Miller G L, and Tolliver D. Combinatorial preconditioners and multilevel solvers for problems in computer vision and image processing[J]. Computer Vision and Image Understanding, 2011, 115(12): 1638-1646.
|