Library and Information Service

Library and Information Service >

2017 , Vol. 61 >Issue 7: 137 - 144

DOI: https://doi.org/10.13266/j.issn.0252-3116.2017.07.019

Review of Studies on Heterogeneous Information Network Fusion Methods

  • Tian Pengwei ,
  • Zhang Xian ,
  • Hu Zhengyin ,
  • Dong Kun ,
  • Xu Haiyun
Expand
  • 1. Chengdu Library and Information Center, Chinese Academy of Sciences, Chengdu 610041;
    2. University of Chinese Academy of Sciences, Beijing 100190

Received date: 2017-01-06

  Revised date: 2017-03-20

  Online published: 2017-04-05

Fold

Abstract

[Purpose/significance] The study of heterogeneous information networks (HINs) fusion is of great significance for heterogeneous information network analysis and their applications. In this paper, some typical methods are reviewed in order to provide new ideas for further research. [Method/process] Based on the comparison of the concepts between HINs and other networks, this paper investigated, analyzed and summarized the research methods of HINs fusion, reviewed the current research in this field, and put forward the future research directions possibly. [Result/conclusion] The methods of HINs fusion are divided into five types: meta-path extraction, multi-relational network and hyper-graph/super-network modeling, and so on. and these specific methods have their own advantages and limitations. At present, HINs fusion is still at the initial stage. The research methods and applications based on HINs fusion needs further exploration.

Key words： heterogeneous information network; network fusion; meta-path; hyper-graph; super-network; clustering; classification

Cite this article

Tian Pengwei , Zhang Xian , Hu Zhengyin , Dong Kun , Xu Haiyun . Review of Studies on Heterogeneous Information Network Fusion Methods[J]. Library and Information Service, 2017 , 61(7) : 137 -144 . DOI: 10.13266/j.issn.0252-3116.2017.07.019

References

[1] CHEN C L P, ZHANG C Y. Data-intensive applications, challenges, techniques and technologies: a survey on big data[J]. Information sciences, 2014, 275(11): 314-347.
[2] 孟祥保. 图书情报学交叉融合与发展——基于国外35种核心期刊的引文分析[J]. 图书情报知识, 2012(5):50-58.
[3] PARK H W. Hyperlink network analysis: a new method for the study of social structure on the web[J]. Connections, 2003, 25(1): 49-61.
[4] SUN Y, HAN J, ZHAO P, et al. Rankclus: integrating clustering with ranking for heterogeneous information network analysis[C]//Proceedings of the 12th international conference on extending database technology: advances in database technology. New York,: ACM, 2009: 565-576.
[5] SHI C, LI Y, ZHANG J, et al. A survey of heterogeneous information network analysis[J]. Computer science, 2015, 134(12):87-99.
[6] JI M, HAN J, DANILEVSKY M. Ranking-based classification of heterogeneous information networks[C]//Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. New York: ACM, 2011: 1298-1306.
[7] SUN Y, HAN J. Mining heterogeneous information networks: principles and methodologies[J]. ACM SIGKDD explorations newsletter, 2012, 14(2):439-473.
[8] FEDERICA. Innovating in computing network architectures[EB/OL].[2016-11-03]. http://www.fp7-federica.eu/infrastructure/architech.php.
[9] 王海涛, 付鹰. 异构网络融合——研究发展现状及存在的问题[J]. 数据通信, 2012(2):18-21.
[10] BOUANAN Y, RIBAULT J, FORESTIER M, et al. Modeling and simulation of human reaction in a multidimensional social network[J]. IFAC-PapersOnLine, 2015, 48(3): 592-597.
[11] BERLINGERIO M, COSCIA M, GIANNOTTI F, et al. Foundations of multidimensional network analysis[C]//International conference on advances in social networks analysis and mining. Piscataway: IEEE, 2011:485-489.
[12] TANG L, LIU H, ZHANG J, et al. Community evolution in dynamic multi-mode networks[C]//Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. New York,: ACM, 2008: 677-685.
[13] 王娜, 李霞, 徐红英. 社会网络分析之社区发现研究[J]. 深圳大学学报(理工版), 2014, 31(1):35-42.
[14] BOCCALETTI S, LATORA V, MORENO Y, et al. Complex networks: structure and dynamics[J]. Physics reports, 2006, 424(4/5):175-308.
[15] STROGATZ S H. Exploring complex networks[J]. Nature, 2001, 410(6825):268-76.
[16] HWANG T H, KUANG R. A heterogeneous label propagation algorithm for disease gene discovery[C]//Proceedings of the 2010 SIAM International conference on data mining. Philadelphia: SIAM, 2010: 583-594.
[17] SUN Y, TANG J, HAN J, et al. Community evolution detection in dynamic heterogeneous information networks[C]//Proceedings of the eighth workshop on mining and learning with graphs. New York: ACM, 2010: 137-146.
[18] RAVASZ E, BARABASI A, OLTVAI Z. Hierarchical organization of complex networks[EB/OL]. [2016-10-11]. https://repository.library.northeastern.edu/files/neu:331069/fulltext.pdf.
[19] 张自立, 张紫琼, 李向阳. 基于2-模网络的科研单位和关键词共现分析方法[J]. 情报学报, 2011, 30(12):1249-1260.
[20] 许海云, 郭婷, 岳增慧,等. 基于TI指标系列的情报学学科交叉主题研究[J]. 情报学报, 2015, 34(10):1067-1078.
[21] LEYDESDORFF L. What can heterogeneity add to the scientometric map? Steps towards algorithmic historiography[EB/OL]. [2016-12-20]. http://leydesdorff.net/mcallon/mcallon.pdf
[22] 李长玲, 刘非凡, 魏绪秋. 基于3-mode网络的领域主题演化规律分析——以知识网络研究领域为例[J]. 情报理论与实践, 2014, 37(12):104-110.
[23] SUN Y, HAN J, YAN X, et al. PathSim: meta path-based top-k similarity search in heterogeneous information networks[J]. Proceedings of the VLDB endowment, 2011, 4(11):992-1003.
[24] LUO C, GUAN R, WANG Z, et al. HetPathMine: A novel transductive classification algorithm on heterogeneous information networks[M]//Advances in Information Retrieval. 2014: 210-221.
[25] FU G, DING Y, SEAL A, et al. Predicting drug target interactions using meta-path-based semantic network analysis[J]. BMC bioinformatics, 2016, 17(1):1-10.
[26] SUN Y, NORICK B, HAM J, et al. PathSelClus: integrating meta-path selection with user-guided object clustering in heterogeneous information networks[J]. ACM transactions on knowledge discovery from data, 2012, 7(3):723-724.
[27] ZHAO Y, SUN Z, XU C, et al. Meta-path based nonnegative matrix factorization for clustering on multi-type relational data[C]//International joint conference on neural networks (IJCNN). Piscataway: IEEE, 2015: 1-8.
[28] SHI B, WENINGER T. Mining interesting meta-paths from complex heterogeneous information networks[C]//IEEE International conference on data mining workshop. Piscataway: IEEE, 2014:488-495.
[29] MENG C, CHENG R, MANIU S, et al. Discovering meta-paths in large heterogeneous information networks[C]//Proceedings of the 24th international conference on World Wide Web. New York: ACM, 2015: 754-764.
[30] SUN Y, BARBER R, GUPTA M, et al. Co-author relationship prediction in heterogeneous bibliographic networks[C]//International conference on advances in social networks analysis and mining. Piscataway: IEEE, 2011:121-128.
[31] SHI C, KONG X, HUANG Y, et al. HeteSim: a general framework for relevance measure in heterogeneous networks[J]. IEEE transactions on knowledge and data engineering, 2014, 26(10): 2479-2492.
[32] KONG X, YU P S, DING Y, et al. Meta path-based collective classification in heterogeneous information networks[C]//Proceedings of the 21st ACM international conference on information and knowledge management. New York: ACM, 2012: 1567-1571.
[33] LUO C, PANG W, WANG Z. Semi-supervised clustering on heterogeneous information networks[C]//Pacific-Asia conference on knowledge discovery and data mining. Berlin,German: Springer, 2014: 548-559.
[34] 刘彤, 杨冠灿, 侯元元. 基于多重关系整合的专利网络分析方法研究与应用[J]. 情报理论与实践, 2016, 39(2):59-63.
[35] HE X, ZHA H, DING C H Q, et al. Web document clustering using hyperlink structures[J]. Computational statistics & data analysis, 2002, 41(1): 19-45.
[36] LIU X, SHI Y, FRIZO J, et al. Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database[J]. Journal of the American society for information science & technology, 2010, 61(6):1105-1119.
[37] LIU D R, SHIH M J. Hybrid-patent classification based on patent-network analysis[J]. Journal of the American society for information science & technology, 2011, 62(2):246-256.
[38] MAGNANI M, MICENKOVA B, Rossi L. Combinatorial analysis of multiple networks [EB/OL]. [2016-10-20]. http://ia601009.us.archive.org/19/items/arxiv-1303.4986/1303.4986.pdf.
[39] JANSSENS F, GLANZEL W, MOOR B D. A hybrid mapping of information science[J]. Scientometrics, 2008, 75(3):607-631.
[40] DAI B T, CHUA F C T, LIM E P. Structural analysis in multi-relational social networks[EB/OL]. [2016-10-12]. http://epubs.siam.org/doi/pdf/10.1137/1.9781611972825.39.
[41] 吴蕾, 张文生, 王珏. 异构信息网络数据上的融合概率图模型[J]. 计算机科学与探索, 2014, 8(6):712-718.
[42] 张邦佐, 桂欣, 何涛,等. 一种融合异构信息网络和评分矩阵的推荐新算法[J]. 计算机研究与发展, 2014(s2):69-75.
[43] JEH G, WIDOM J. SimRank: a measure of structural-context similarity[C]//Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining. New York: ACM, 2002: 538-543.
[44] SHI J, MALIK J. Normalized cuts and image segmentation[J]. IEEE transactions on pattern analysis and machine intelligence, 2000, 22(8): 888-905.
[45] SUN Y, YU Y, 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. New York: ACM, 2009: 797-806.
[46] SUN Y, AGGARWAL C C, HAN J. Relation strength-aware clustering of heterogeneous information networks with incomplete attributes[J]. Computer science, 2012, 5(5):394-405.
[47] ZITNIK M, ZUPAN B. Data fusion by matrix factorization[J]. IEEE transactions on pattern analysis & machine intelligence, 2015, 37(1):41-53.
[48] ZITNIK M, ZUPAN B. Matrix factorization-based data fusion for drug-induced liver injury prediction[J]. Systems biomedicine, 2014, 2(1):16-22.
[49] REGENBOGEN S, WILKINS A D, LICHTARGE O. Computing therapy for precision medicine: collaborative filtering integrates and predicts multi-entity interactions[C].Pacific symposium on biocomputing. Pacific symposium on biocomputing. NIH public access, 2016, 21:21.
[50] MACSKASSY S A, PROVOST F. Classification in networked data: a toolkit and a univariate case study[J]. Journal of machine learning research, 2007, 8(May): 935-983.
[51] YIN Z, LI R, MEI Q, et al. Exploring social tagging graph for web object classification[C]//Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, 2009: 957-966.
[52] JI M, SUN Y, DANILEVSKY M, et al. Graph regularized transductive classification on heterogeneous information networks[C]//European conference on machine learning and knowledge discovery in databases. Berlin, Heidelberg: Springer, 2010:570-586.
[53] ZHOU D, BOUSQUET O, LAL T N, et al. Learning with local and global consistency[J]. Advances in neural information processing systems, 2004, 16(16): 321-328.
[54] BERGE C. Graphs and hypergraphs[M]. New York: Elsevier, 1973.
[55] BERGE C. Hypergraphs: Combinatorics of finite sets[M]//North Holland: Elsevier, 1989:521-552.
[56] Hypergraph[EB/OL]. [2016-12-08]. https://en.wikipedia.org/wiki/Hypergraph#Theorems.
[57] CHEN F, GAO Y, CAO D, et al. Multimodal hypergraph learning for microblog sentiment prediction [C]//Multimedia and Expo (ICME), 2015 IEEE International Conference on. Piscataway: IEEE, 2015: 1-6.
[58] 罗永恩, 胡继承, 徐茜. 基于超图的多模态关联特征处理方法[J]. 计算机工程, 2017, 43(1):226-230.
[59] 蔡淑琴, 肖泉, 吴颖敏. 基于超图的知识表示及检索相似性度量研究[J]. 图书情报工作, 2009, 53(8):102-105.
[60] SHEFFI Y. Urban transportation networks: equilibrium analysis with mathematical programming methods[J]. Transportation science, 1985, 19(4):463-466.
[61] 王众托, 王志平. 超网络初探[J]. 管理学报, 2008, 5(1):1-8.
[62] SUO Q, SUN S, HAJLI N, et al. User ratings analysis in social networks through a hypernetwork method[J]. Expert systems with applications, 2015, 42(21):7317-7325.
[63] 滕立. 基于超网络的作者-机构-国家混合共现网络研究[J]. 情报学报, 2015(1):28-36.
[64] ZHAO Y S, SHEN B. Empirical study of user preferences based on rating data of movies[EB/OL]. [2016-11-12]. http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC4703247&blobtype=pdf.
[65] DBLP. DBLP XML database [EB/OL]. [2016-12-01]. http://dblp.org/xml/dblp.dtd.
[66] IMDb. IMDb database[EB/OL]. [2016-12-01]. http://www.imdb.com/interfaces.
[67] DBpedia. DBpedia database[EB/OL]. [2016-12-01]. http://wiki.dbpedia.org/downloads-2016-04.
Options
Outlines

/

〈 〉

    Copyrights © LIS Press Co., Ltd. Reproduction is prohibited without permission.