Influence Maximization

Influence Maximization #

Influence Maximization seeks to select a set of influential nodes in a network to maximize information spread. It has applications in social network marketing.

Recent Literature #

1. Learning Heuristics over Large Graphs via Deep Reinforcement Learning. NeurIPS, 2020. paper

   Mittal, Akash and Dhawan, Anuj and Manchanda, Sahil and Medya, Sourav and Ranu, Sayan and Singh, Ambuj.

2. Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks. ICML, 2021. paper

   Eli A. Meirom, Haggai Maron, Shie Mannor, Gal Chechik

3. LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation ICML, 2022. paper, code

   Ireland, David and G. Montana

4. ⭐Towards One-shot Neural Combinatorial Solvers: Theoretical and Empirical Notes on the Cardinality-Constrained Case ICLR, 2023. paper, code

   Wang, Runzhong and Shen, Li and Chen, Yiting and Yan, Junchi and Yang, Xiaokang and Tao, Dacheng

5. Deep Graph Representation Learning and Optimization for Influence Maximization ICML, 2023. paper

   Chen Ling and Junji Jiang and Junxiang Wang and My T. Thai and Lukas Xue and James Song and Meikang Qiu and Liang Zhao