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 …

A data-based approach to social influence maximization

A Goyal, F Bonchi, LVS Lakshmanan - arXiv preprint arXiv:1109.6886, 2011 - arxiv.org
Influence maximization is the problem of finding a set of users in a social network, such that
by targeting this set, one maximizes the expected spread of influence in the network. Most of …

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 graph representation learning for influence maximization with accelerated inference

T Chowdhury, C Ling, J Jiang, J Wang, MT Thai… - Neural Networks, 2024 - Elsevier
Selecting a set of initial users from a social network in order to maximize the envisaged
number of influenced users is known as influence maximization (IM). Researchers have …

Fast and accurate influence maximization on large networks with pruned monte-carlo simulations

N Ohsaka, T Akiba, Y Yoshida… - Proceedings of the AAAI …, 2014 - ojs.aaai.org
Influence maximization is a problem to find small sets of highly influential individuals in a
social network to maximize the spread of influence under stochastic cascade models of …

Leveraging cross-network information for graph sparsification in influence maximization

X Shen, F Chung, S Mao - Proceedings of the 40th International ACM …, 2017 - dl.acm.org
When tackling large-scale influence maximization (IM) problem, one effective strategy is to
employ graph sparsification as a pre-processing step, by removing a fraction of edges to …

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 …

Efficient targeted influence minimization in big social networks

X Wang, K Deng, J Li, JX Yu, CS Jensen, X Yang - World Wide Web, 2020 - Springer
An online social network can be used for the diffusion of malicious information like
derogatory rumors, disinformation, hate speech, revenge pornography, etc. This motivates …

ToupleGDD: A fine-designed solution of influence maximization by deep reinforcement learning

T Chen, S Yan, J Guo, W Wu - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Aiming at selecting a small subset of nodes with maximum influence on networks, the
influence maximization (IM) problem has been extensively studied. Since it is# P-hard to …

Learning heuristics over large graphs via deep reinforcement learning

S Manchanda, A Mittal, A Dhawan, S Medya… - arXiv preprint arXiv …, 2019 - arxiv.org
There has been an increased interest in discovering heuristics for combinatorial problems
on graphs through machine learning. While existing techniques have primarily focused on …