Synonyms
Glossary
- HIN:
-
Heterogeneous information network
Definition
Information networks have been intensively studied in recent years, ranging from community detection to graph classification. Typical applications of information networks include web mining, social network analysis, bioinformatics, etc. Most previous research on information networks focuses on homogeneous networks, which involve one type of nodes and one type of links, e.g., social networks with friendship links and webpage networks with hyperlinks. With the recent advance in data collection techniques, many real-world applications are facing large-scale heterogeneous information networks (Sun et al. 2011), which involve multiple types of objects interconnected through multiple types of links. These networks are multimode and multi-relational networks, which involves large amount of information. For example, a bibliographic network in Fig. 1involves five types...
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Ding Y, Yan E, Frazho A, Caverlee J (2009) PageRank for ranking authors in co-citation networks. J Am Soc Inf Sci Technol 60(11):2229–2243
Gao J, Liang F, Fan W, Wang C, Sun Y, Han J (2010) Community outliers and their efficient detection in information networks. In: KDD, Washington, DC, pp 913–822
Ji M, Han J, Danilevsky M (2011) Ranking-based classification of heterogeneous information networks. In: KDD, San Diego, pp 1298–1306
Lao N, Cohen W (2010a) Fast query execution for retrieval models based on path-constrained random walks. In: KDD, Washington, DC, pp 881–888
Lao N, Cohen W (2010b) Relational retrieval using a combination of path-constrained random walks. Mach Learn 81(2):53–67
Lu Q, Getoor L (2003) Link-based classification. In: ICML, Washington, DC, pp 496–503
McDowell LK, Gupta KM, Aha DW (2007) Cautious inference in collective classification. In: AAAI, Vancouver, pp 596–601
McDowell LK, Gupta KM, Aha DW (2009) Cautious collective classification. J Mach Learn Res 10:2777–2836
Neville J, Jensen D (2000) Iterative classification in relational data. In: AAAI'10 workshop on learning statistical models from relational data, Austin
Neville J, Jensen D (2003) Collective classification with relational dependency networks. In: KDD'03 workshop on multi-relational data mining, Washington, DC, pp 77–91
Sen P, Namata G, Bilgic M, Getoor L, Gallagher B, Eliassi-Rad T (2008) Collective classification in network data. AI Mag 29(3):93–106
Sun Y, Han J, Zhao P, Yin Z, Cheng H, Wu T (2009a) RankClus: integrating clustering with ranking for heterogeneous information network analysis. In: EDBT, Saint-Petersburg, pp 565–576
Sun Y, Yu YS, Han J (2009b) Ranking-based clustering of heterogeneous information networks with star network schema. In: KDD, Paris, pp 797–806
Sun Y, Han J, Yan X, Yu P, Wu T (2011) PathSim: meta path-based top-k similarity search in heterogeneous information networks. In: VLDB, Seattle
Taskar B, Abbeel P, Koller D (2002) Discriminative probabilistic models for relational data. In: UAI, Edmonton, pp 482–492
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2018 Springer Science+Business Media LLC, part of Springer Nature
About this entry
Cite this entry
Kong, X., Yu, P.S. (2018). Graph Classification in Heterogeneous Networks. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-7131-2_176
Download citation
DOI: https://doi.org/10.1007/978-1-4939-7131-2_176
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4939-7130-5
Online ISBN: 978-1-4939-7131-2
eBook Packages: Computer ScienceReference Module Computer Science and Engineering