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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Computing

ISSN

0010-485X

e-ISSN

1436-5057

Svazek periodika

106

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

AT - Rakouská republika

Počet stran výsledku

24

Strana od-do

—

Kód UT WoS článku

001194867800001

EID výsledku v databázi Scopus

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