RL4CO: a unified reinforcement learning for combinatorial optimization library

F Berto, C Hua, J Park, M Kim, H Kim, J Son… - … 2023 Workshop: New …, 2023 - openreview.net
Deep reinforcement learning offers notable benefits in addressing combinatorial problems
over traditional solvers, reducing the reliance on domain-specific knowl-edge and expert …

HypOp: Distributed Constrained Combinatorial Optimization leveraging Hypergraph Neural Networks

N Heydaribeni, X Zhan, R Zhang, T Eliassi-Rad… - arXiv preprint arXiv …, 2023 - arxiv.org
Scalable addressing of high dimensional constrained combinatorial optimization problems
is a challenge that arises in several science and engineering disciplines. Recent work …

Crosswalk: Fairness-enhanced node representation learning

A Khajehnejad, M Khajehnejad, M Babaei… - Proceedings of the …, 2022 - ojs.aaai.org
The potential for machine learning systems to amplify social inequities and unfairness is
receiving increasing popular and academic attention. Much recent work has focused on …

Neural combinatorial optimization beyond the TSP: Existing architectures under-represent graph structure

M Boffa, ZB Houidi, J Krolikowski, D Rossi - arXiv preprint arXiv …, 2022 - arxiv.org
Recent years have witnessed the promise that reinforcement learning, coupled with Graph
Neural Network (GNN) architectures, could learn to solve hard combinatorial optimization …

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 …

NEDRL-CIM: Network embedding meets deep reinforcement learning to tackle competitive influence maximization on evolving social networks

K Ali, CY Wang, MY Yeh, CT Li… - 2021 IEEE 8th …, 2021 - ieeexplore.ieee.org
Competitive Influence Maximization (CIM) aims to maximize the influence of a party given
the competition from other parties in the same social network, like companies find key users …

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 …

COMBHelper: A Neural Approach to Reduce Search Space for Graph Combinatorial Problems

H Tian, S Medya, W Ye - Proceedings of the AAAI Conference on …, 2024 - ojs.aaai.org
Combinatorial Optimization (CO) problems over graphs appear routinely in many
applications such as in optimizing traffic, viral marketing in social networks, and matching for …

Finding seeds and relevant tags jointly: For targeted influence maximization in social networks

X Ke, A Khan, G Cong - … of the 2018 international conference on …, 2018 - dl.acm.org
-1mmWe study the novel problem of jointly finding the top-k seed nodes and the top-r
relevant tags for targeted influence maximization in a social network. The bulk of the …

Conformity-aware influence maximization in online social networks

H Li, SS Bhowmick, A Sun, J Cui - The VLDB Journal, 2015 - Springer
Influence maximization (im) is the problem of finding a small subset of nodes (seed nodes)
in a social network that could maximize the spread of influence. Despite the progress …