Multidimensional Network Analysis to Predict Correlations Between Offline and Online Interactions

Lapo Chirici; Francesca Parotti; Yi Wang

doi:10.1007/978-981-33-6318-2_71

Multidimensional Network Analysis to Predict Correlations Between Offline and Online Interactions

Lapo Chirici^*
, Francesca Parotti
, Yi Wang

^*Corresponding author for this work

Plymouth Business School

University of Pisa
University of Florence

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings published in a book › peer-review

Abstract

Community detection inside networks represents an area which has gained a paramount importance in the last decade. With the massive spreading of online communications several attempts have been performed to investigate correlations between the “Real world” and social networks, both in terms number of connections between people and consistency of relationship. This research delves into the relations occurred on Facebook and Twitter which can be used as a proxy for the relations in offline social networks, such as networks of human face-to-face (F2F) proximity. The final aim is focused on discovering the most meaningful patterns that rule the hierarchy of bonds by means of the comparison of several algorithms widely recognized as effective for different applications, such a routing, link prediction, crimes and recently containment of virus outbreak.

Original language	English
Title of host publication	Advanced Manufacturing and Automation X
Editors	Yi Wang, Kristian Martinsen, Tao Yu, Kesheng Wang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	569-578
Number of pages	10
ISBN (Print)	9789813363175
DOIs	https://doi.org/10.1007/978-981-33-6318-2_71
Publication status	Published - 2021
Event	10th International Workshop of Advanced Manufacturing and Automation, IWAMA 2020 - Zhanjiang, China Duration: 12 Oct 2020 → 13 Oct 2020

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	737
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	10th International Workshop of Advanced Manufacturing and Automation, IWAMA 2020
Country/Territory	China
City	Zhanjiang
Period	12/10/20 → 13/10/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

ASJC Scopus subject areas

Industrial and Manufacturing Engineering

Keywords

Community discovery
Knowledge discovery
Network analysis

Access to Document

10.1007/978-981-33-6318-2_71

Cite this

Chirici, L., Parotti, F., & Wang, Y. (2021). Multidimensional Network Analysis to Predict Correlations Between Offline and Online Interactions. In Y. Wang, K. Martinsen, T. Yu, & K. Wang (Eds.), Advanced Manufacturing and Automation X (pp. 569-578). (Lecture Notes in Electrical Engineering; Vol. 737). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-33-6318-2_71

@inproceedings{2e1e1dcc955b4ba48f72363ce4d77c8e,

title = "Multidimensional Network Analysis to Predict Correlations Between Offline and Online Interactions",

abstract = "Community detection inside networks represents an area which has gained a paramount importance in the last decade. With the massive spreading of online communications several attempts have been performed to investigate correlations between the “Real world” and social networks, both in terms number of connections between people and consistency of relationship. This research delves into the relations occurred on Facebook and Twitter which can be used as a proxy for the relations in offline social networks, such as networks of human face-to-face (F2F) proximity. The final aim is focused on discovering the most meaningful patterns that rule the hierarchy of bonds by means of the comparison of several algorithms widely recognized as effective for different applications, such a routing, link prediction, crimes and recently containment of virus outbreak.",

keywords = "Community discovery, Knowledge discovery, Network analysis",

author = "Lapo Chirici and Francesca Parotti and Yi Wang",

note = "Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 10th International Workshop of Advanced Manufacturing and Automation, IWAMA 2020 ; Conference date: 12-10-2020 Through 13-10-2020",

year = "2021",

doi = "10.1007/978-981-33-6318-2\_71",

language = "English",

isbn = "9789813363175",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

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editor = "Yi Wang and Kristian Martinsen and Tao Yu and Kesheng Wang",

booktitle = "Advanced Manufacturing and Automation X",

address = "Germany",

}

Chirici, L, Parotti, F & Wang, Y 2021, Multidimensional Network Analysis to Predict Correlations Between Offline and Online Interactions. in Y Wang, K Martinsen, T Yu & K Wang (eds), Advanced Manufacturing and Automation X. Lecture Notes in Electrical Engineering, vol. 737, Springer Science and Business Media Deutschland GmbH, pp. 569-578, 10th International Workshop of Advanced Manufacturing and Automation, IWAMA 2020, Zhanjiang, China, 12/10/20. https://doi.org/10.1007/978-981-33-6318-2_71

Multidimensional Network Analysis to Predict Correlations Between Offline and Online Interactions. / Chirici, Lapo; Parotti, Francesca; Wang, Yi.
Advanced Manufacturing and Automation X. ed. / Yi Wang; Kristian Martinsen; Tao Yu; Kesheng Wang. Springer Science and Business Media Deutschland GmbH, 2021. p. 569-578 (Lecture Notes in Electrical Engineering; Vol. 737).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings published in a book › peer-review

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N2 - Community detection inside networks represents an area which has gained a paramount importance in the last decade. With the massive spreading of online communications several attempts have been performed to investigate correlations between the “Real world” and social networks, both in terms number of connections between people and consistency of relationship. This research delves into the relations occurred on Facebook and Twitter which can be used as a proxy for the relations in offline social networks, such as networks of human face-to-face (F2F) proximity. The final aim is focused on discovering the most meaningful patterns that rule the hierarchy of bonds by means of the comparison of several algorithms widely recognized as effective for different applications, such a routing, link prediction, crimes and recently containment of virus outbreak.

AB - Community detection inside networks represents an area which has gained a paramount importance in the last decade. With the massive spreading of online communications several attempts have been performed to investigate correlations between the “Real world” and social networks, both in terms number of connections between people and consistency of relationship. This research delves into the relations occurred on Facebook and Twitter which can be used as a proxy for the relations in offline social networks, such as networks of human face-to-face (F2F) proximity. The final aim is focused on discovering the most meaningful patterns that rule the hierarchy of bonds by means of the comparison of several algorithms widely recognized as effective for different applications, such a routing, link prediction, crimes and recently containment of virus outbreak.

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KW - Knowledge discovery

KW - Network analysis

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Y2 - 12 October 2020 through 13 October 2020

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Multidimensional Network Analysis to Predict Correlations Between Offline and Online Interactions

Abstract

Publication series

Conference

UN SDGs

ASJC Scopus subject areas

Keywords

Access to Document

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