Deep graph representation learning and optimization for influence maximization

C Ling, J Jiang, J Wang, MT Thai… - International …, 2023 - proceedings.mlr.press
Influence maximization (IM) is formulated as selecting a set of initial users from a social
network to maximize the expected number of influenced users. Researchers have made …

Deep reinforcement learning-based approach to tackle topic-aware influence maximization

S Tian, S Mo, L Wang, Z Peng - Data Science and Engineering, 2020 - Springer
Motivated by the application of viral marketing, the topic-aware influence maximization (TIM)
problem has been proposed to identify the most influential users under given topics. In …

Deepinf: Social influence prediction with deep learning

J Qiu, J Tang, H Ma, Y Dong, K Wang… - Proceedings of the 24th …, 2018 - dl.acm.org
Social and information networking activities such as on Facebook, Twitter, WeChat, and
Weibo have become an indispensable part of our everyday life, where we can easily access …

A survey on influence maximization: From an ml-based combinatorial optimization

Y Li, H Gao, Y Gao, J Guo, W Wu - ACM Transactions on Knowledge …, 2023 - dl.acm.org
Influence Maximization (IM) is a classical combinatorial optimization problem, which can be
widely used in mobile networks, social computing, and recommendation systems. It aims at …

Influence maximization across heterogeneous interconnected networks based on deep learning

MM Keikha, M Rahgozar, M Asadpour… - Expert Systems with …, 2020 - Elsevier
With the fast development of online social networks, a large number of their members are
involved in more than one social network. Finding most influential users is one of the …

Network dynamic GCN influence maximization algorithm with leader fake labeling mechanism

C Zhang, W Li, D Wei, Y Liu, Z Li - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Influence maximization is an important technique for its significant value on various social
network applications, such as viral marketing, advertisement, and recommendation …

Influence maximization in unknown social networks: Learning policies for effective graph sampling

H Kamarthi, P Vijayan, B Wilder, B Ravindran… - arXiv preprint arXiv …, 2019 - arxiv.org
A serious challenge when finding influential actors in real-world social networks is the lack
of knowledge about the structure of the underlying network. Current state-of-the-art methods …

PIANO: Influence maximization meets deep reinforcement learning

H Li, M Xu, SS Bhowmick, JS Rayhan… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Since its introduction in 2003, the influence maximization (IM) problem has drawn significant
research attention in the literature. The aim of IM, which is NP-hard, is to select a set of users …

Influence maximization in social networks using transfer learning via graph-based LSTM

S Kumar, A Mallik, BS Panda - Expert Systems with Applications, 2023 - Elsevier
Social networks have emerged as efficient platforms to connect people worldwide and
facilitate the rapid spread of information. Identifying influential nodes in social networks to …

Influence maximization in complex networks by using evolutionary deep reinforcement learning

L Ma, Z Shao, X Li, Q Lin, J Li… - … on Emerging Topics …, 2022 - ieeexplore.ieee.org
Influence maximization (IM) in complex networks tries to activate a small subset of seed
nodes that could maximize the propagation of influence. The studies on IM have attracted …