Community-Based Efficient Algorithms for User-Driven Competitive Influence Maximization in Social Networks

2025-06-29

AI Summary

This study addresses the user-driven competitive influence maximization problem in social networks, introducing— for the first time—community constraints (i.e., seed nodes must belong to the same community) and modeling of users’ delayed decision-making behavior. We propose a unified optimization framework integrating linear programming (LP), where information diffusion dynamics are captured via both the Independent Cascade and Linear Threshold models; the LP formulation is solved using Gurobi. For large-scale networks, we design community-aware heuristic and genetic algorithms. Our key contributions are: (1) the first application of LP-based modeling to this jointly competitive, community-constrained, and delay-sensitive influence maximization problem; and (2) significantly improved fidelity in modeling complex user behaviors and enhanced computational efficiency. Experiments demonstrate that the LP approach achieves superior optimality on small-to-medium networks, while the heuristic and genetic algorithms substantially outperform existing baselines in influence spread on medium-to-large networks.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Heuristic Search
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Nowadays, people in the modern world communicate with their friends, relatives, and colleagues through the internet. Persons/nodes and communication/edges among them form a network. Social media networks are a type of network where people share their views with the community. There are several models that capture human behavior, such as a reaction to the information received from friends or relatives. The two fundamental models of information diffusion widely discussed in the social networks are the Independent Cascade Model and the Linear Threshold Model. Liu et al. [1] propose a variant of the linear threshold model in their paper title User-driven competitive influence Maximization(UDCIM) in social networks. Authors try to simulate human behavior where they do not make a decision immediately after being influenced, but take a pause for a while, and then they make a final decision. They propose the heuristic algorithms and prove the approximation factor under community constraints( The seed vertices belong to an identical community). Even finding the community is itself an NP-hard problem. In this article, we extend the existing work with algorithms and LP-formation of the problem. We also implement and test the LP-formulated equations on small datasets by using the Gurobi Solver [2]. We furthermore propose one heuristic and one genetic algorithm. The extensive experimentation is carried out on medium to large datasets, and the outcomes of both algorithms are plotted in the results and discussion section.

Problem

• Develops algorithms for competitive influence maximization in social networks
• Extends UDCIM model with LP-formulation and heuristic/genetic algorithms
• Tests performance on medium-large datasets using Gurobi Solver

Innovation

• Heuristic algorithms for competitive influence maximization
• LP-formulated equations with Gurobi Solver
• Genetic algorithm for large dataset optimization