TY - JOUR
T1 - Social science-guided feature engineering
T2 - A novel approach to signed link analysis
AU - Beigi, Ghazaleh
AU - Tang, Jiliang
AU - Liu, Huan
N1 - Funding Information:
This material is based upon the work supported, in part, by NSF #1614576, ARO W911NF-15-1-0328, and ONR N00014-17-1-2605. Authors’ addresses: G. Beigi and H. Liu, Arizona State University, Tempe, AZ, 85281; emails: {gbeigi, huan.liu}@asu.edu; J. Tang, Computer Science and Engineering Department, Michigan State University, East Lansing, MI, 48824; email: tangjili@msu.edu. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2020 Association for Computing Machinery. 2157-6904/2020/01-ART11 $15.00 https://doi.org/10.1145/3364222
Publisher Copyright:
© 2020 Association for Computing Machinery.
PY - 2020/1/9
Y1 - 2020/1/9
N2 - Many real-world relations can be represented by signed networks with positive links (e.g., friendships and trust) and negative links (e.g., foes and distrust). Link prediction helps advance tasks in social network analysis such as recommendation systems. Most existing work on link analysis focuses on unsigned social networks. The existence of negative links piques research interests in investigating whether properties and principles of signed networks differ from those of unsigned networks and mandates dedicated efforts on link analysis for signed social networks. Recent findings suggest that properties of signed networks substantially differ from those of unsigned networks and negative links can be of significant help in signed link analysis in complementary ways. In this article,we center our discussion on a challenging problem of signed link analysis. Signed link analysis faces the problem of data sparsity, i.e., only a small percentage of signed links are given. This problem can even getworse when negative links are much sparser than positive ones as users are inclined more toward positive disposition rather than negative. We investigate how we can take advantage of other sources of information for signed link analysis. This research is mainly guided by three social science theories, Emotional Information, Diffusion of Innovations, and Individual Personality. Guided by these, we extract three categories of related features and leverage them for signed link analysis. Experiments showthe significance of the features gleaned from social theories for signed link prediction and addressing the data sparsity challenge.
AB - Many real-world relations can be represented by signed networks with positive links (e.g., friendships and trust) and negative links (e.g., foes and distrust). Link prediction helps advance tasks in social network analysis such as recommendation systems. Most existing work on link analysis focuses on unsigned social networks. The existence of negative links piques research interests in investigating whether properties and principles of signed networks differ from those of unsigned networks and mandates dedicated efforts on link analysis for signed social networks. Recent findings suggest that properties of signed networks substantially differ from those of unsigned networks and negative links can be of significant help in signed link analysis in complementary ways. In this article,we center our discussion on a challenging problem of signed link analysis. Signed link analysis faces the problem of data sparsity, i.e., only a small percentage of signed links are given. This problem can even getworse when negative links are much sparser than positive ones as users are inclined more toward positive disposition rather than negative. We investigate how we can take advantage of other sources of information for signed link analysis. This research is mainly guided by three social science theories, Emotional Information, Diffusion of Innovations, and Individual Personality. Guided by these, we extract three categories of related features and leverage them for signed link analysis. Experiments showthe significance of the features gleaned from social theories for signed link prediction and addressing the data sparsity challenge.
KW - Data sparsity
KW - Diffusion of innovation
KW - Emotional information
KW - Feature engineering
KW - Individual personality
KW - Signed link analysis
KW - Social theory
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U2 - 10.1145/3364222
DO - 10.1145/3364222
M3 - Article
AN - SCOPUS:85078532954
VL - 11
JO - ACM Transactions on Intelligent Systems and Technology
JF - ACM Transactions on Intelligent Systems and Technology
SN - 2157-6904
IS - 1
M1 - A11
ER -