Labeling actors in multi-view social networks by integrating information from within and across multiple views

Ngot Bui, Thanh Le, Vasant Honavar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Real world social networks typically consist of actors (individuals) that are linked to other actors or different types of objects via links of multiple types. Different types of relationships induce different views of the underlying social network. We consider the problem of labeling actors in such multi-view networks based on the connections among them. Given a social network in which only a subset of the actors are labeled, our goal is to predict the labels of the rest of the actors. We introduce a new random walk kernel, namely the Inter-Graph Random Walk Kernel (IRWK), for labeling actors in multi-view social networks. IRWK combines information from within each of the views as well as the links across different views. The results of our experiments on two real-world multi-view social networks show that: (i) IRWK classifiers outperform or are competitive with several state-of-the-art methods for labeling actors in a social network; (ii) IRWKs are robust with respect to different choices of user-specified parameters; and (iii) IRWK kernel computation converges very fast within a few iterations.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
EditorsRonay Ak, George Karypis, Yinglong Xia, Xiaohua Tony Hu, Philip S. Yu, James Joshi, Lyle Ungar, Ling Liu, Aki-Hiro Sato, Toyotaro Suzumura, Sudarsan Rachuri, Rama Govindaraju, Weijia Xu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages616-625
Number of pages10
ISBN (Electronic)9781467390040
DOIs
StatePublished - Jan 1 2016
Event4th IEEE International Conference on Big Data, Big Data 2016 - Washington, United States
Duration: Dec 5 2016Dec 8 2016

Publication series

NameProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016

Other

Other4th IEEE International Conference on Big Data, Big Data 2016
CountryUnited States
CityWashington
Period12/5/1612/8/16

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All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Hardware and Architecture

Cite this

Bui, N., Le, T., & Honavar, V. (2016). Labeling actors in multi-view social networks by integrating information from within and across multiple views. In R. Ak, G. Karypis, Y. Xia, X. T. Hu, P. S. Yu, J. Joshi, L. Ungar, L. Liu, A-H. Sato, T. Suzumura, S. Rachuri, R. Govindaraju, ... W. Xu (Eds.), Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016 (pp. 616-625). [7840654] (Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2016.7840654
Bui, Ngot ; Le, Thanh ; Honavar, Vasant. / Labeling actors in multi-view social networks by integrating information from within and across multiple views. Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016. editor / Ronay Ak ; George Karypis ; Yinglong Xia ; Xiaohua Tony Hu ; Philip S. Yu ; James Joshi ; Lyle Ungar ; Ling Liu ; Aki-Hiro Sato ; Toyotaro Suzumura ; Sudarsan Rachuri ; Rama Govindaraju ; Weijia Xu. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 616-625 (Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016).
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abstract = "Real world social networks typically consist of actors (individuals) that are linked to other actors or different types of objects via links of multiple types. Different types of relationships induce different views of the underlying social network. We consider the problem of labeling actors in such multi-view networks based on the connections among them. Given a social network in which only a subset of the actors are labeled, our goal is to predict the labels of the rest of the actors. We introduce a new random walk kernel, namely the Inter-Graph Random Walk Kernel (IRWK), for labeling actors in multi-view social networks. IRWK combines information from within each of the views as well as the links across different views. The results of our experiments on two real-world multi-view social networks show that: (i) IRWK classifiers outperform or are competitive with several state-of-the-art methods for labeling actors in a social network; (ii) IRWKs are robust with respect to different choices of user-specified parameters; and (iii) IRWK kernel computation converges very fast within a few iterations.",
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Bui, N, Le, T & Honavar, V 2016, Labeling actors in multi-view social networks by integrating information from within and across multiple views. in R Ak, G Karypis, Y Xia, XT Hu, PS Yu, J Joshi, L Ungar, L Liu, A-H Sato, T Suzumura, S Rachuri, R Govindaraju & W Xu (eds), Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016., 7840654, Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016, Institute of Electrical and Electronics Engineers Inc., pp. 616-625, 4th IEEE International Conference on Big Data, Big Data 2016, Washington, United States, 12/5/16. https://doi.org/10.1109/BigData.2016.7840654

Labeling actors in multi-view social networks by integrating information from within and across multiple views. / Bui, Ngot; Le, Thanh; Honavar, Vasant.

Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016. ed. / Ronay Ak; George Karypis; Yinglong Xia; Xiaohua Tony Hu; Philip S. Yu; James Joshi; Lyle Ungar; Ling Liu; Aki-Hiro Sato; Toyotaro Suzumura; Sudarsan Rachuri; Rama Govindaraju; Weijia Xu. Institute of Electrical and Electronics Engineers Inc., 2016. p. 616-625 7840654 (Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Bui, Ngot

AU - Le, Thanh

AU - Honavar, Vasant

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N2 - Real world social networks typically consist of actors (individuals) that are linked to other actors or different types of objects via links of multiple types. Different types of relationships induce different views of the underlying social network. We consider the problem of labeling actors in such multi-view networks based on the connections among them. Given a social network in which only a subset of the actors are labeled, our goal is to predict the labels of the rest of the actors. We introduce a new random walk kernel, namely the Inter-Graph Random Walk Kernel (IRWK), for labeling actors in multi-view social networks. IRWK combines information from within each of the views as well as the links across different views. The results of our experiments on two real-world multi-view social networks show that: (i) IRWK classifiers outperform or are competitive with several state-of-the-art methods for labeling actors in a social network; (ii) IRWKs are robust with respect to different choices of user-specified parameters; and (iii) IRWK kernel computation converges very fast within a few iterations.

AB - Real world social networks typically consist of actors (individuals) that are linked to other actors or different types of objects via links of multiple types. Different types of relationships induce different views of the underlying social network. We consider the problem of labeling actors in such multi-view networks based on the connections among them. Given a social network in which only a subset of the actors are labeled, our goal is to predict the labels of the rest of the actors. We introduce a new random walk kernel, namely the Inter-Graph Random Walk Kernel (IRWK), for labeling actors in multi-view social networks. IRWK combines information from within each of the views as well as the links across different views. The results of our experiments on two real-world multi-view social networks show that: (i) IRWK classifiers outperform or are competitive with several state-of-the-art methods for labeling actors in a social network; (ii) IRWKs are robust with respect to different choices of user-specified parameters; and (iii) IRWK kernel computation converges very fast within a few iterations.

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A2 - Ak, Ronay

A2 - Karypis, George

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A2 - Ungar, Lyle

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Bui N, Le T, Honavar V. Labeling actors in multi-view social networks by integrating information from within and across multiple views. In Ak R, Karypis G, Xia Y, Hu XT, Yu PS, Joshi J, Ungar L, Liu L, Sato A-H, Suzumura T, Rachuri S, Govindaraju R, Xu W, editors, Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 616-625. 7840654. (Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016). https://doi.org/10.1109/BigData.2016.7840654