Prediction of Influential Spreaders in Complex Social Networks Using Hybrid Sarimax-LSTM Model

Abstract

In the digital era, the rapid and widespread diffusion of information through social media has made it essential to identify influential spreaders—users who can significantly impact information propagation. This study proposes a hybrid predictive model that combines Seasonal Autoregressive Integrated Moving Average with exogenous factors (SARIMAX) and Long Short-Term Memory (LSTM) networks to forecast influence trends within complex social networks. The model leverages both statistical time-series forecasting and deep learning to capture linear seasonal patterns and complex non-linear behaviours in user interactions. Through systematic data preprocessing, including noise removal, tokenization, and lemmatization, the model processes structured and unstructured data to identify key influencers. Evaluation results demonstrate high precision, recall, and F1-scores, confirming the model’s effectiveness in dynamic environments. The proposed system offers valuable applications in marketing, crisis management, and public opinion tracking, supporting real-time influencer identification with enhanced accuracy and robustness.

Introduction

Objective:

In a world increasingly driven by social media, identifying influential users (or “spreaders”) is essential for marketing, health, politics, and crisis communication. Traditional network metrics like degree or betweenness centrality fall short in capturing dynamic user behavior and real-time influence trends.

Proposed Solution:

This project presents a hybrid model combining SARIMAX (a statistical time-series model) and LSTM (a deep learning model) to predict influential users based on historical behavior, seasonal trends, and contextual factors.

Key Features of the Approach:

1. Hybrid Model Architecture:

• SARIMAX captures linear, seasonal, and exogenous effects (like events or campaigns).

• LSTM captures non-linear temporal patterns and long-term dependencies.

• The fused output from both models predicts a user’s influence score.

2. Dataset & Preprocessing:

• Data Source: Twitter (1000 tweets, 1988 interactions, 2595 users).

• Features used: Retweets, mentions, demographics, centrality metrics.

• Processing: Tokenization, lemmatization, normalization (Z-score).

Influence Ranking Mechanism:

• Users are ranked based on predicted influence.

• k-Shell decomposition helps verify structural importance.

• Kendall Tau correlation checks ranking stability.

Optimization:

• Metaheuristic algorithms (Genetic Algorithm and Simulated Annealing) are used to select the top-k influencers for campaign targeting, ensuring diversity and maximum impact.

Performance Metrics:

• These results show the model is both accurate and reliable in detecting influential spreaders.

Comparative Analysis:

Key Insights:

• Eigenvector centrality showed the highest correlation with predicted influencers (8 out of top 10 matched).

• The model outperformed all traditional ranking methods, especially under real-time, fast-changing network conditions.

Conclusion

The growing influence of social networks in shaping public opinion, marketing trends, and information dissemination has led to a critical need for accurately identifying key individualsknown as influential spreaderswithin these platforms. This project presented a hybrid approach using SARIMAX (Seasonal AutoRegressive Integrated Moving Average with eXogenous factors) integrated with deep learning (LSTM) to effectively predict such influencers over time. Through comprehensive exploration of social network dynamics, temporal behaviors, and forecasting models, the proposed framework demonstrated significant improvement in identifying potential spreaders with high accuracy. The SARIMAX model effectively captured seasonality and exogenous patterns in time series data, while the LSTM component handled non-linear dependencies and long-term trends. Together, they offered a robust solution capable of adapting to complex and evolving social media environments. Feasibility analysis confirmed the system’s technical, operational, economic, and performance viability. Real-world datasets were used to validate the model, and the results showed consistent performance across multiple social platforms. The solution not only enhances influencer marketing strategies but also contributes to network analysis, digital campaigning, and public awareness programs.

References

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Copyright

Copyright © 2025 Subathra S, Leelavathi K, Rajasri M, Iswarya V, Dr. V. Govindasamy. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.