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Multiplex Graph Association Rules for Link Prediction

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Multiplex networks allow us to study a variety of complex systems where nodes connect to each other in multiple ways, for example friend, family, and co-worker relations in social networks. Link prediction is the branch of network analysis allowing us to forecast the future status of a network: which new connections are the most likely to appear in the future? In multiplex link prediction we also ask: of which type? Because this last question is unanswerable with classical link prediction, here we investigate the use of graph association rules to inform multiplex link prediction. We derive such rules by identifying all frequent patterns in a network via multiplex graph mining, and then score each unobserved link's likelihood by finding the occurrences of each rule in the original network. Association rules add new abilities to multiplex link prediction: to predict new node arrivals, to consider higher order structures with four or more nodes, and to be memory efficient. In our experiments, we show that, exploiting graph association rules, we are able to achieve a prediction performance close to an ideal ensemble classifier. Further, we perform a case study on a signed multiplex network, showing how graph association rules can provide valuable insights to extend social balance theory.
Original languageEnglish
Title of host publicationProceedings of the Fifteenth International AAAI Conference on Web and Social Media, ICWSM 2021
PublisherAAAI Press
Publication date7 Jun 2021
Pages129-139
ISBN (Print) 978-1-57735-869-5
Publication statusPublished - 7 Jun 2021
Event15th International AAAI Conference on Web and Social Media - Virtual
Duration: 7 Jun 202110 Jun 2021
Conference number: 15
https://www.icwsm.org/2021/index.html

Conference

Conference15th International AAAI Conference on Web and Social Media
Nummer15
LocationVirtual
Periode07/06/202110/06/2021
Internetadresse
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    Research areas

  • graph mining, association rules, multilayer networks, multiplex networks

ID: 85885109