Labeling actors in social networks using a heterogeneous graph kernel

Ngot Bui, Vasant Honavar

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

1 Citation (Scopus)

Abstract

We consider the problem of labeling actors in social networks where the labels correspond to membership in specific interest groups, or other attributes of the actors. Actors in a social network are linked to not only other actors but also items (e.g., video and photo) which in turn can be linked to other items or actors. Given a social network in which only some of the actors are labeled, our goal is to predict the labels of the remaining actors. We introduce a variant of the random walk graph kernel to deal with the heterogeneous nature of the network (i.e., presence of a large number of node and link types). We show that the resulting heterogeneous graph kernel (HGK) can be used to build accurate classifiers for labeling actors in social networks. Specifically, we describe results of experiments on two real-world data sets that show HGK classifiers often significantly outperform or are competitive with the state-of-the-art methods for labeling actors in social networks.

Original languageEnglish (US)
Title of host publicationSocial Computing, Behavioral-Cultural Modeling, and Prediction - 7th International Conference, SBP 2014, Proceedings
PublisherSpringer Verlag
Pages27-34
Number of pages8
ISBN (Print)9783319055787
DOIs
StatePublished - Jan 1 2014
Event7th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, SBP 2014 - Washington, DC, United States
Duration: Apr 1 2014Apr 4 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8393 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, SBP 2014
CountryUnited States
CityWashington, DC
Period4/1/144/4/14

Fingerprint

Social Networks
Labeling
kernel
Labels
Classifiers
Graph in graph theory
Classifier
Actors
Experiments
Random walk
Attribute
Predict
Vertex of a graph
Experiment

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Bui, N., & Honavar, V. (2014). Labeling actors in social networks using a heterogeneous graph kernel. In Social Computing, Behavioral-Cultural Modeling, and Prediction - 7th International Conference, SBP 2014, Proceedings (pp. 27-34). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8393 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-05579-4_4
Bui, Ngot ; Honavar, Vasant. / Labeling actors in social networks using a heterogeneous graph kernel. Social Computing, Behavioral-Cultural Modeling, and Prediction - 7th International Conference, SBP 2014, Proceedings. Springer Verlag, 2014. pp. 27-34 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{8a6c74ed77ab42dea88cb2e17220bd1a,
title = "Labeling actors in social networks using a heterogeneous graph kernel",
abstract = "We consider the problem of labeling actors in social networks where the labels correspond to membership in specific interest groups, or other attributes of the actors. Actors in a social network are linked to not only other actors but also items (e.g., video and photo) which in turn can be linked to other items or actors. Given a social network in which only some of the actors are labeled, our goal is to predict the labels of the remaining actors. We introduce a variant of the random walk graph kernel to deal with the heterogeneous nature of the network (i.e., presence of a large number of node and link types). We show that the resulting heterogeneous graph kernel (HGK) can be used to build accurate classifiers for labeling actors in social networks. Specifically, we describe results of experiments on two real-world data sets that show HGK classifiers often significantly outperform or are competitive with the state-of-the-art methods for labeling actors in social networks.",
author = "Ngot Bui and Vasant Honavar",
year = "2014",
month = "1",
day = "1",
doi = "10.1007/978-3-319-05579-4_4",
language = "English (US)",
isbn = "9783319055787",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "27--34",
booktitle = "Social Computing, Behavioral-Cultural Modeling, and Prediction - 7th International Conference, SBP 2014, Proceedings",
address = "Germany",

}

Bui, N & Honavar, V 2014, Labeling actors in social networks using a heterogeneous graph kernel. in Social Computing, Behavioral-Cultural Modeling, and Prediction - 7th International Conference, SBP 2014, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8393 LNCS, Springer Verlag, pp. 27-34, 7th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, SBP 2014, Washington, DC, United States, 4/1/14. https://doi.org/10.1007/978-3-319-05579-4_4

Labeling actors in social networks using a heterogeneous graph kernel. / Bui, Ngot; Honavar, Vasant.

Social Computing, Behavioral-Cultural Modeling, and Prediction - 7th International Conference, SBP 2014, Proceedings. Springer Verlag, 2014. p. 27-34 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8393 LNCS).

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

TY - GEN

T1 - Labeling actors in social networks using a heterogeneous graph kernel

AU - Bui, Ngot

AU - Honavar, Vasant

PY - 2014/1/1

Y1 - 2014/1/1

N2 - We consider the problem of labeling actors in social networks where the labels correspond to membership in specific interest groups, or other attributes of the actors. Actors in a social network are linked to not only other actors but also items (e.g., video and photo) which in turn can be linked to other items or actors. Given a social network in which only some of the actors are labeled, our goal is to predict the labels of the remaining actors. We introduce a variant of the random walk graph kernel to deal with the heterogeneous nature of the network (i.e., presence of a large number of node and link types). We show that the resulting heterogeneous graph kernel (HGK) can be used to build accurate classifiers for labeling actors in social networks. Specifically, we describe results of experiments on two real-world data sets that show HGK classifiers often significantly outperform or are competitive with the state-of-the-art methods for labeling actors in social networks.

AB - We consider the problem of labeling actors in social networks where the labels correspond to membership in specific interest groups, or other attributes of the actors. Actors in a social network are linked to not only other actors but also items (e.g., video and photo) which in turn can be linked to other items or actors. Given a social network in which only some of the actors are labeled, our goal is to predict the labels of the remaining actors. We introduce a variant of the random walk graph kernel to deal with the heterogeneous nature of the network (i.e., presence of a large number of node and link types). We show that the resulting heterogeneous graph kernel (HGK) can be used to build accurate classifiers for labeling actors in social networks. Specifically, we describe results of experiments on two real-world data sets that show HGK classifiers often significantly outperform or are competitive with the state-of-the-art methods for labeling actors in social networks.

UR - http://www.scopus.com/inward/record.url?scp=84958536066&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84958536066&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-05579-4_4

DO - 10.1007/978-3-319-05579-4_4

M3 - Conference contribution

SN - 9783319055787

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 27

EP - 34

BT - Social Computing, Behavioral-Cultural Modeling, and Prediction - 7th International Conference, SBP 2014, Proceedings

PB - Springer Verlag

ER -

Bui N, Honavar V. Labeling actors in social networks using a heterogeneous graph kernel. In Social Computing, Behavioral-Cultural Modeling, and Prediction - 7th International Conference, SBP 2014, Proceedings. Springer Verlag. 2014. p. 27-34. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-05579-4_4