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 …

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 …

Adversarial socialbots modeling based on structural information principles

X Zeng, H Peng, A Li - Proceedings of the AAAI Conference on Artificial …, 2024 - ojs.aaai.org
The importance of effective detection is underscored by the fact that socialbots imitate
human behavior to propagate misinformation, leading to an ongoing competition between …

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 …

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 …

Frequent itemset-driven search for finding minimal node separators and its application to air transportation network analysis

Y Zhou, X Zhang, N Geng, Z Jiang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The-separator problem (-SP) consists of finding the minimum set of vertices whose removal
separates the network into multiple different connected components with fewer than a limited …

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 …

Contingency-aware influence maximization: A reinforcement learning approach

H Chen, W Qiu, HC Ou, B An… - Uncertainty in Artificial …, 2021 - proceedings.mlr.press
The influence maximization (IM) problem aims at finding a subset of seed nodes in a social
network that maximize the spread of influence. In this study, we focus on a sub-class of IM …

Random majority opinion diffusion: Stabilization Time, absorbing states, and influential nodes

AN Zehmakan - arXiv preprint arXiv:2302.06760, 2023 - arxiv.org
Consider a graph G with n nodes and m edges, which represents a social network, and
assume that initially each node is blue or white. In each round, all nodes simultaneously …

Gac: A deep reinforcement learning model toward user incentivization in unknown social networks

S Wu, W Li, Q Bai - Knowledge-Based Systems, 2023 - Elsevier
In recent years, many applications have deployed incentive mechanisms to promote users'
attention and engagement. Most incentive mechanisms determine specific incentive values …