RankMerging: a supervised learning-to-rank framework to predict links in large social networks

DOI10.1007/S10994-019-05792-4zbMath1493.68318arXiv1407.2515OpenAlexW2963480208WikidataQ128039537 ScholiaQ128039537MaRDI QIDQ2320535

Daniel F. Bernardes, Renaud Lambiotte, Lionel Tabourier, Anne-Sophie Libert

Publication date: 23 August 2019

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1407.2515

zbMATH Keywords

supervised learninglearning to ranklink predictionsocial network analysislarge networks

Mathematics Subject Classification ID

Social networks; opinion dynamics (91D30) Small world graphs, complex networks (graph-theoretic aspects) (05C82) Learning and adaptive systems in artificial intelligence (68T05) Statistical ranking and selection procedures (62F07)

Related Items (1)

RankMerging

Uses Software

• Scikit
• LinkBoost

Cites Work

• Unnamed Item
• Unnamed Item
• Predicting missing links via local information
• Empirical Analysis of an Evolving Social Network
• LOCAL COMMUNITY IDENTIFICATION IN SOCIAL NETWORKS
• 10.1162/1532443041827916

This page was built for publication: RankMerging: a supervised learning-to-rank framework to predict links in large social networks