Results 101 to 110 of about 1,352 (208)

A New Algorithm Framework for the Influence Maximization Problem Using Graph Clustering

Information
Given a social network modelled by a graph, the goal of the influence maximization problem is to find k vertices that maximize the number of active vertices through a process of diffusion. For this diffusion, the linear threshold model is considered.
Agostinho Agra, Jose Maria Samuco
doaj   +1 more source

Reinforcement Learning–Enhanced Influence Maximisation via Credibility‐Based Clustering and Metaheuristic Optimisation

IET Networks, Volume 15, Issue 1, January/December 2026.
In this study, a novel approach of integrating artificial intelligence (AI) and machine learning, that is, reinforcement learning (RL), with centrality measures is introduced to improve information spreading in social networks. RL models the decision process to dynamically select powerful nodes that bypass static measures of centrality constraints ...
Mohammad Mehdi Karimi, Abbas Karimi, Faraneh Zarafshan, Javad Mohammadzadeh   +3 more
wiley   +1 more source

Influential Billboard Slot Selection Using Spatial Clustering and Pruned Submodularity Graph

Data Science and Engineering
Billboard Advertisement is a popular out-of-home advertising technique adopted by commercial houses. Companies own billboards and offer them to commercial houses on a payment basis.
Dildar Ali, Suman Banerjee, Yamuna Prasad   +2 more
doaj   +1 more source

Carbon Dioxide Storage Site Location and Transport Assignment Optimization for Sustainable Maritime Transport

Journal of Marine Science and Engineering
Maritime carbon dioxide (CO2) transport plays a pivotal role in facilitating carbon capture and storage (CCS) systems by connecting emission sources with appropriate storage sites. This process often incurs significant transportation costs, which must be
Yanmeng Tao, Ying Yang, Yuquan Du, Shuaian Wang   +3 more
doaj   +1 more source

Multipass Target Search in Natural Environments

Sensors, 2017
Consider a disaster scenario where search and rescue workers must search difficult to access buildings during an earthquake or flood. Often, finding survivors a few hours sooner results in a dramatic increase in saved lives, suggesting the use of drones ...
Michael J. Kuhlman, Michael W. Otte, Donald Sofge, Satyandra K. Gupta   +3 more
doaj   +1 more source

Symmetric Submodular Functions, Uncrossable Functions, and Structural Submodularity

CoRR
Diestel, et al. (see Order 35 (2017), JCT-A 167 (2019), arXiv:1805.01439) introduced the notion of abstract separation systems that satisfy a submodularity property, and they call this structural submodularity. Williamson, Goemans, Mihail, and Vazirani (Combinatorica 15 (1995)) call a family of sets $\mathcal{F}$ uncrossable if the following holds: for
Miles Simmons, Ishan Bansal, Joe Cheriyan   +2 more
openaire   +3 more sources

Distributionally robust optimization through the lens of submodularity [PDF]

, 2023
Distributionally robust optimization is used to solve decision making problems under adversarial uncertainty where the distribution of the uncertainty is itself ambiguous.
Padmanabhan, Divya, Ramachandra, Arjun, Natarajan, Karthik   +2 more
core  

Minimal leader selection in general linear multi-agent systems with switching topologies : leveraging submodularity ratio

, 2023
In multi-agent systems with leader-follower dynamics, choosing a subset of agents as leaders is a critical step in achieving the desired coordination performance.
Zhang, Wenle, He, Wangli, Chen, Kaile, Tang, Yang, Zheng, Wei Xing (R8901)   +4 more
core   +1 more source

Sequential Decision Making in Computational Sustainability via Adaptive Submodularity

, 2014
Many problems in computational sustainability require making a sequence of decisions in complex, uncertain environments. Such problems are generally notoriously difficult.
Sarah Converse, Daniel Golovin, Andreas Krause   +2 more
core   +1 more source

Weakly Submodular Functions

CoRR, 2014
Submodular functions are well-studied in combinatorial optimization, game theory and economics. The natural diminishing returns property makes them suitable for many applications. We study an extension of monotone submodular functions, which we call {\em weakly submodular functions}. Our extension includes some (mildly) supermodular functions.
Allan Borodin, Dai Le, Yuli Ye
openaire   +3 more sources