Results 71 to 80 of about 10,146,366 (200)

A Decentralised Coordination Algorithm for Maximising Sensor Coverage in Large Sensor Networks

, 2010
In large wireless sensor networks, the problem of assigning radio frequencies to sensing agents such that no two connected sensors are assigned the same value (and will thus interfere with one another) is a major challenge.
Jennings, Nick, Stranders, Ruben, Rogers, Alex   +2 more
core   +1 more source

Neural Probabilistic Relational Games (N-PRG): Learning Influence Coalitions from Cascade Data via Gradient Descent

Machine Learning and Knowledge Extraction
Influence maximisation traditionally assumes that each activated neighbour contributes independently to the likelihood of a user adopting information, ignoring conjunctive synergies where a set of users must be active simultaneously to trigger another ...
Duc Nghia Vu, Thanh Huy Nguyen, Duc Thi Vu, Janos Demetrovics   +3 more
doaj   +1 more source

Assortative Matching on Income

Econometrica, Volume 94, Issue 3, Page 957-989, May 2026.
We analyze marital matching on income using an extremely rich Dutch data set containing all income tax files over seven years. We develop a novel methodology that directly extends previous contributions to allow for highly flexible matching patterns. Investigating all marriages that took place between 2013 and 2019, we find that marital patterns are ...
Pierre‐André Chiappori, Carlo Fiorio, Alfred Galichon, Stefano Verzillo   +3 more
wiley   +1 more source

A Universal Meta‐Heuristic Framework for Influence Maximisation in Hypergraphs

CAAI Transactions on Intelligence Technology, Volume 11, Issue 2, Page 396-410, April 2026.
ABSTRACT Influence maximisation (IM) aims to select a small number of nodes that are able to maximise their influence in a network and covers a wide range of applications. Despite numerous attempts to provide effective solutions in simple networks, higher‐order interactions between entities in various real‐world systems are usually not taken into ...
Ming Xie, Chuang Liu, Yang Chen, Zi‐Ke Zhang, Xiaoyang Liu, Xiu‐Xiu Zhan   +5 more
wiley   +1 more source

Generalized Budgeted Submodular Set Function Maximization [PDF]

, 2018
In this paper we consider a generalization of the well-known budgeted maximum coverage problem. We are given a ground set of elements and a set of bins. The goal is to find a subset of elements along with an associated set of bins, such that the overall ...
Velaj, Yllka, Cellinese, Francesco, Francesco Cellinese, Gianpiero Monaco, Yllka Velaj, D'Angelo, Gianlorenzo, Gianlorenzo D'Angelo, Monaco, Gianpiero   +7 more
core   +1 more source

Fossil Fuels and Renewable Energy: Mix or Match?

The RAND Journal of Economics, Volume 57, Issue 1, Page 140-157, Spring 2026.
ABSTRACT This article investigates the influence of technological ownership on pricing strategies and productive efficiency. Our motivation comes from the evolving landscape of electricity markets where firms are transitioning from diversified to specialized portfolios, focusing on renewable energy or fossil fuels.
Natalia Fabra, Gerard Llobet
wiley   +1 more source

Same/Other/All K‐Fold Cross‐Validation for Estimating Similarity of Patterns in Data Subsets

Statistical Analysis and Data Mining: An ASA Data Science Journal, Volume 19, Issue 1, February 2026.
ABSTRACT In many real‐world applications of machine learning, we are interested to know if it is possible to train on the data that we have gathered so far, and obtain accurate predictions on a new test data subset that is qualitatively different in some respect (time period, geographic region, etc.).
Toby Dylan Hocking, Gabrielle Thibault, Cameron Scott Bodine, Paul Nelson Arellano, Alexander F. Shenkin, Olivia Jasmine Lindly   +5 more
wiley   +1 more source

Game‐Theoretic Multi‐Agent Dynamic Traffic Assignment Using Congestion‐Aware Adaptive Learning

IET Intelligent Transport Systems, Volume 20, Issue 1, January/December 2026.
This paper proposes a novel game‐theoretic multi‐agent framework for dynamic traffic assignment problems, integrating congestion‐aware adaptive learning to simultaneously reduce individual travel time and improve overall network efficiency under dynamic congestion.
Dandan Wu, Shiru Qu, Xu He, Hongming Xu, Ji Li   +4 more
wiley   +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

Sequence independent lifting for a set of submodular maximization problems

, 2022
We study the polyhedral structure of a mixed 0-1 set arising from the submodular maximization problem, given by P = {(w, x) ∈ R × {0, 1}n : w ≤ f (x), x ∈ X}, where submodular function f (x) is represented by a concave function composed with an affine ...
Shi, Xueyu, Prokopyev, Oleg A; https://orcid.org/, Zeng, Bo; https://orcid.org/, Oleg A. Prokopyev, Bo Zeng, Xueyu Shi   +5 more
core   +1 more source