RA-ICM: A Novel Independent Cascade Model Incorporating User Relationships and Attitudes

Keskeiset käsitteet

The author argues that considering relationships between non-adjacent users and user attitudes is crucial for accurately predicting information propagation in social networks. By proposing a new model, they aim to improve prediction accuracy and reduce time complexity.

Tiivistelmä

The rapid development of social networks has led to the need for accurate forecasting of information propagation processes. This study introduces a novel model that considers user relationships and attitudes, showing improved prediction accuracy compared to traditional models. The proposed model reflects actual information dissemination trends in social networks.
The study highlights the impact of non-neighboring users on information dissemination, emphasizing the importance of user stances in influencing the process. By incorporating these factors into the model, the authors demonstrate enhanced predictive performance and insights into emotional contagion dynamics.
Furthermore, experiments conducted on real-world datasets validate the effectiveness of the proposed model in predicting information dissemination trends accurately. The analysis also includes a comparison with existing models, showcasing superior performance in accuracy and alignment with real network dynamics.

Tilastot

Dataset I comprises 1,300 nodes and 4,951 edges.
Dataset II includes 1,061 nodes and 4,122 edges.
Dataset III consists of 1,791 nodes and 5,895 edges.
Dataset IV contains 2 topics with 2,300 nodes and 8,780 edges.
Dataset V features 2 topics with 2,754 nodes and 10,241 edges.
Dataset VI includes 3 topics with 4,005 nodes and 14,067 edges.

Lainaukset

"The relationships between non-adjacent users significantly influence information propagation within social networks." - Xinyu Li et al.
"Considering user attitudes is crucial for understanding emotional contagion dynamics." - Xinyu Li et al.

Tärkeimmät oivallukset

RA-ICM

by Xinyu Li,Yut... klo arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.06385.pdf

Syvällisempiä Kysymyksiä

How does considering non-neighboring users impact traditional information propagation models

Considering non-neighboring users in traditional information propagation models impacts the accuracy and realism of the predictions. In traditional models like the Independent Cascade Model, it was assumed that nodes could only influence their neighboring nodes and be influenced by them. However, in reality, information can spread between users who are not directly connected but share similar interests or topics. By incorporating non-neighboring users into the model, we get a more comprehensive understanding of how information actually spreads in social networks. This inclusion allows for a more accurate representation of real-world scenarios where information can reach distant users based on shared interests or topic relevance.

What are the implications of user attitudes on emotional contagion in online social networks

User attitudes play a significant role in emotional contagion within online social networks. When users express different stances or emotions towards specific topics or events, these attitudes can influence others who come across their content. Emotional contagion refers to the phenomenon where individuals' emotions and behaviors are affected by those around them. In online social networks, user attitudes can lead to the spread of emotions such as positivity, negativity, excitement, fear, etc., creating an emotional ripple effect throughout the network. Understanding user attitudes is crucial for studying emotional contagion dynamics and its impact on overall network behavior.

How can this research be applied to improve prediction accuracy in other fields beyond social network analysis

The research conducted on incorporating user relationships and attitudes into information propagation models has broader implications beyond social network analysis. This approach can be applied to various fields where understanding how information spreads is essential for decision-making processes.

Marketing: Predicting how word-of-mouth marketing campaigns will propagate through consumer networks based on user relationships and attitudes.
Healthcare: Studying how health-related information disseminates through patient communities to improve public health awareness.
Finance: Analyzing investor sentiment and its impact on stock market trends using relationship-based influence calculations.
Politics: Examining political discourse dissemination among voters based on individual preferences and stance changes during election campaigns.
By applying this research methodology outside social network analysis domains, organizations can make informed decisions leveraging insights from complex data interactions influenced by user relationships and attitudes across diverse fields.