Unraveling human social behavior motivations via inverse reinforcement learning-based link prediction

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10256844" target="_blank" >RIV/61989100:27240/24:10256844 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s00607-024-01279-w" target="_blank" >https://link.springer.com/article/10.1007/s00607-024-01279-w</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00607-024-01279-w" target="_blank" >10.1007/s00607-024-01279-w</a>

Result language

angličtina

Original language name

Unraveling human social behavior motivations via inverse reinforcement learning-based link prediction

Original language description

Link prediction aims to capture the evolution of network structure, especially in real social networks, which is conducive to friend recommendations, human contact trajectory simulation, and more. However, the challenge of the stochastic social behaviors and the unstable space-time distribution in such networks often leads to unexplainable and inaccurate link predictions. Therefore, taking inspiration from the success of imitation learning in simulating human driver behavior, we propose a dynamic network link prediction method based on inverse reinforcement learning (DN-IRL) to unravel the motivations behind social behaviors in social networks. Specifically, the historical social behaviors (link sequences) and a next behavior (a single link) are regarded as the current environmental state and the action taken by the agent, respectively. Subsequently, the reward function, which is designed to maximize the cumulative expected reward from expert behaviors in the raw data, is optimized and utilized to learn the agent&apos;s social policy. Furthermore, our approach incorporates the neighborhood structure based node embedding and the self-attention modules, enabling sensitivity to network structure and traceability to predicted links. Experimental results on real-world dynamic social networks demonstrate that DN-IRL achieves more accurate and explainable of prediction compared to the baselines.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10200 - Computer and information sciences

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Computing

ISSN

0010-485X

e-ISSN

1436-5057

Volume of the periodical

106

Issue of the periodical within the volume

6

Country of publishing house

AT - AUSTRIA

Number of pages

24

Pages from-to

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UT code for WoS article

001194867800001

EID of the result in the Scopus database

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