Adaptive Influence Maximization in Dynamic Social Networks

Abstract

For the purpose of propagating information and ideas through a social network, a seeding strategy aims to find a small set of seed users that are able to maximize the spread of the influence, which is termed as influence maximization problem. Despite a large number of works have studied this problem, the existing seeding strategies are limited to the static social networks. In fact, due to the high speed data transmission and the large population of participants, the diffusion processes in real-world social networks have many aspects of uncertainness. Unfortunately, as shown in the experiments, in such cases the state-of-art seeding strategies are pessimistic as they fails to trace the dynamic changes in a social network. In this paper, we study the strategies selecting seed users in an adaptive manner. We first formally model the Dynamic Independent Cascade model and introduce the concept of adaptive seeding strategy. Then based on the proposed model, we show that a simple greedy adaptive seeding strategy finds an effective solution with a provable performance guarantee. Besides the greedy algorithm an efficient heuristic algorithm is provided in order to meet practical requirements. Extensive experiments have been performed on both the real-world networks and synthetic power-law networks. The results herein demonstrate the superiority of the adaptive seeding strategies to other standard methods.

References in corpus (3)

• Power-law distributions in empirical data
• Influence Maximization in Continuous Time Diffusion Networks
• Scalable Methods for Adaptively Seeding a Social Network

Cited by in corpus (16)

• A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization
• Neighborhood Matters: Influence Maximization in Social Networks with Limited Access
• Combined Centrality Measures for an Improved Characterization of Influence Spread in Social Networks
• Thompson Sampling for Combinatorial Network Optimization in Unknown Environments
• Adaptive Influence Maximization with Myopic Feedback
• On Adaptivity Gaps of Influence Maximization under the Independent Cascade Model with Full Adoption Feedback
• Adaptive Influence Maximization under General Feedback Models
• Graph Representation Learning for Popularity Prediction Problem: A Survey
• Adaptivity in Adaptive Submodularity
• Computational Aspects of Optimal Strategic Network Diffusion
• On the Aggression Diffusion Modeling and Minimization in Online Social Networks
• Time-constrained Adaptive Influence Maximization
• Social Influence and Radicalization: A Social Data Analytics Study
• A Co\mbox{-}Operative Game Theoretic Approach for the Budgeted Influence Maximization Problem
• ABEM: An Adaptive Agent-based Evolutionary Approach for Mining Influencers in Online Social Networks
• Evolving Influence Maximization in Evolving Networks