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Multiplex Graph Association Rules for Link Prediction

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review

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Multiplex Graph Association Rules for Link Prediction. / Coscia, Michele; Szell, Michael.

Proceedings of the Fifteenth International AAAI Conference on Web and Social Media, ICWSM 2021. AAAI Press, 2021. p. 129-139.

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review

Harvard

Coscia, M & Szell, M 2021, Multiplex Graph Association Rules for Link Prediction. in Proceedings of the Fifteenth International AAAI Conference on Web and Social Media, ICWSM 2021. AAAI Press, pp. 129-139, 15th International AAAI Conference on Web and Social Media, 07/06/2021.

APA

Coscia, M., & Szell, M. (2021). Multiplex Graph Association Rules for Link Prediction. In Proceedings of the Fifteenth International AAAI Conference on Web and Social Media, ICWSM 2021 (pp. 129-139). AAAI Press.

Vancouver

Coscia M, Szell M. Multiplex Graph Association Rules for Link Prediction. In Proceedings of the Fifteenth International AAAI Conference on Web and Social Media, ICWSM 2021. AAAI Press. 2021. p. 129-139

Author

Coscia, Michele ; Szell, Michael. / Multiplex Graph Association Rules for Link Prediction. Proceedings of the Fifteenth International AAAI Conference on Web and Social Media, ICWSM 2021. AAAI Press, 2021. pp. 129-139

Bibtex

@inproceedings{5440fcc0eca64560b33b9c2e77f0a59c,
title = "Multiplex Graph Association Rules for Link Prediction",
abstract = "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. ",
keywords = "graph mining, association rules, multilayer networks, multiplex networks",
author = "Michele Coscia and Michael Szell",
year = "2021",
month = jun,
day = "7",
language = "English",
isbn = " 978-1-57735-869-5",
pages = "129--139",
booktitle = "Proceedings of the Fifteenth International AAAI Conference on Web and Social Media, ICWSM 2021",
publisher = "AAAI Press",
address = "United States",
note = "15th International AAAI Conference on Web and Social Media, ICWSM ; Conference date: 07-06-2021 Through 10-06-2021",
url = "https://www.icwsm.org/2021/index.html",

}

RIS

TY - GEN

T1 - Multiplex Graph Association Rules for Link Prediction

AU - Coscia, Michele

AU - Szell, Michael

N1 - Conference code: 15

PY - 2021/6/7

Y1 - 2021/6/7

N2 - 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.

AB - 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.

KW - graph mining

KW - association rules

KW - multilayer networks

KW - multiplex networks

M3 - Article in proceedings

SN - 978-1-57735-869-5

SP - 129

EP - 139

BT - Proceedings of the Fifteenth International AAAI Conference on Web and Social Media, ICWSM 2021

PB - AAAI Press

T2 - 15th International AAAI Conference on Web and Social Media

Y2 - 7 June 2021 through 10 June 2021

ER -

ID: 85885109