Adaptive Influence Maximization in Dynamic Social Networks (1506.06294v1)

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.

Adaptive Influence Maximization in Dynamic Social Networks

The paper "Adaptive Influence Maximization in Dynamic Social Networks" by Guangmo Tong et al. explores the issue of influence maximization within dynamically changing social networks. The influence maximization problem seeks to identify an initial set of users in a social network who can maximally spread information or influence through the network. This research highlights an existing gap in addressing influence maximization strategies in static networks, leading to the proposal of an adaptive approach suitable for dynamic networks.

Key Contributions

1. Dynamic Independent Cascade (DIC) Model: The authors introduce the DIC model, which extends the traditional Independent Cascade (IC) model to accommodate dynamic changes in network topology and stochastic elements in social networks. While classical models treat propagation probabilities as constants, the DIC model considers these probabilities as random variables, allowing for variance over time and between nodes.
2. Adaptive Seeding Strategy: The concept of adaptive seeding is formalized, wherein seed nodes are selected through a dynamic approach responsive to changes in the network. Contrary to static strategies, adaptive seeding makes decisions based on prior diffusion results, heightening its efficacy in dynamic environments.
3. Greedy Algorithm with Performance Guarantee: The paper proposes a greedy adaptive seeding strategy with a provable performance guarantee of at least a $(1-1/e)$ approximation ratio. This is crucial as it ensures the strategy performs close to optimally, even without exhaustive computation.
4. Heuristic Approach: To meet practical scalability requirements, the authors also provide a heuristic algorithm, H-Greedy, that significantly enhances computational efficiency while maintaining performance close to the optimal adaptive strategy.

Experimental Evaluation

The research demonstrates the superiority of the proposed adaptive strategies (A-Greedy and H-Greedy) over traditional non-adaptive methods across various datasets, including real-world networks such as Hep and Wiki. Notable observations are that adaptive strategies lead to a more significant influence spread and are advantageous in terms of computational efficiency, especially crucial for handling larger networks or tighter computational budgets.

Theoretical and Practical Implications

Theoretically, the paper advances the understanding of influence maximization in variable settings, bridging a critical gap in current models—overcoming static limitations to include dynamic network changes. Practically, these insights hold significant potential in optimizing marketing strategies, information dissemination, and controlling misinformation in digital platforms, where networks are inherently and continuously changing.

Potential for Future Research

This work opens several avenues for future explorations. Advanced techniques could focus on optimizing seeding strategies under additional real-world constraints, such as time-limited diffusion. Furthermore, applying these strategies to different types of networks, especially those with rapidly evolving topologies, could extend the applicability of the findings.

Conclusion

In conclusion, "Adaptive Influence Maximization in Dynamic Social Networks" proposes significant advancements in optimizing influence spread under dynamic conditions. By extending the framework of social network analysis and applying rigorous computational techniques, this research enhances both theoretical models and practical applications for complex, evolving networks. This paper sets a foundation for ongoing research and development in adaptive strategies aligned with the dynamic nature of real-world social networks.