一种基于嵌入技术的异构信息网络的快速聚类算法

陈丽敏; 杨静; 张健沛

doi:10.11999/JEIT150106

一种基于嵌入技术的异构信息网络的快速聚类算法

doi: 10.11999/JEIT150106

2.
(哈尔滨工程大学计算机科学与技术学院哈尔滨 150001) ②(牡丹江师范学院计算机系牡丹江 157012)

基金项目:

国家自然科学基金(61370083, 61073043, 61073041)和高等学校博士学科点专项科研基金(20112304110011, 20122304110012)

计量
- 文章访问数: 1219
- HTML全文浏览量: 105
- PDF下载量: 594
- 被引次数: 0
出版历程
- 收稿日期: 2015-01-21
- 修回日期: 2015-07-16
- 刊出日期: 2015-11-19

A Fast Clustering Algorithm Based on Embedding Technology for Heterogeneous Information Networks

2.
(Institute of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China)

Funds:

The National Natural Science Foundation of China (61370083, 61073043, 61073041)

摘要

摘要: 异构信息网络聚类分析是当前的热点研究问题之一。利用异构信息网络的稀疏性，该文提出一种基于嵌入技术的星型模式的异构信息网络的快速聚类算法。首先从相容的角度将异构信息网络转化为若干个相容的二部图，使用随机映射和一种线性时间求解程序快速计算出每个二部图的近似通勤距离嵌入，每个嵌入都存在一个子集指示目标数据集；然后，使用这些指示子集构建一个通用的聚类模型；最后，将所有指示子集的类设置标号，通过计算指示同一目标对象的指示数据与标号相同类的中心点的加权距离总和，同时划分所有的指示子集，从而快速获得通用模型的极小值。通过理论分析及实验验证，该文算法聚类速度快，聚类准确率高。
- 异构信息网络 /
- 聚类 /
- 通勤距离 /
- 嵌入 /
- 加权距离总和
Abstract: Research on clustering heterogeneous information networks is one of the current hotspots. Taking advantages of the sparsity of heterogeneous information networks, a fast clustering algorithm based on embedding technology for heterogeneous information networks of star network schema is proposed in this paper. First, the heterogeneous information network is transformed into some compatible bipartite graphs from the point of compatible view. Then, the approximate commute distance embedding of each bipartite graph is computed via random mapping and a linear time solver, and an indicator subset in each embedding indicates the target dataset. At last, a general model is formulated via all the indicator subsets, and a minimum value of the model is derived by simultaneously clustering all of the indicator subsets using the sum of the weighted distances for all indicators for an identical target object. This proposed algorithm is effective by theory analysis and experimental verification.
- Heterogeneous information network /
- Clustering /
- Commute distance /
- Embedding /
- Sum of weighted distances

HTML全文

参考文献(24)

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.

施引文献

资源附件(0)

访问统计