# Adaptive Influence Maximization in Dynamic Social Networks

arXiv: https://arxiv.org/abs/1506.06294 · HTML: https://mlsanity.com/paper/1506.06294/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2015.

## Built on by (most cited first, 7 of 7)

- [A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization](https://mlsanity.com/paper/2211.03074/) (2022)
- [Minimum Cost Adaptive Submodular Cover](https://mlsanity.com/paper/2208.08351/) (2022)
- [Graph Representation Learning for Popularity Prediction Problem: A Survey](https://mlsanity.com/paper/2203.07632/) (2022)
- [Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization](https://mlsanity.com/paper/2206.14846/) (2022)
- [ABEM: An Adaptive Agent-based Evolutionary Approach for Mining Influencers in Online Social Networks](https://mlsanity.com/paper/2104.06563/) (2021)
- [Jointly Complementary&Competitive Influence Maximization with Concurrent Ally-Boosting and Rival-Preventing](https://mlsanity.com/paper/2302.09620/) (2023)
- [Adaptive Combinatorial Maximization: Beyond Approximate Greedy Policies](https://mlsanity.com/paper/2404.01930/) (2024)

## Abstract (arXiv)

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.