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Publications (10 of 34) Show all publications
Fatemi, Z., Magnani, M. & Salehi, M. (2018). A generalized force-directed layout for multiplex sociograms. In: Social Informatics: 10th International Conference, SocInfo 2018, St. Petersburg, Russia, September 25-28, 2018, Proceedings, Part I. Paper presented at 10th International Conference on Social Informatics (SocInfo), SEP 25-28, 2018, St Petersburg, RUSSIA (pp. 212-227). Springer, 11185
Open this publication in new window or tab >>A generalized force-directed layout for multiplex sociograms
2018 (English)In: Social Informatics: 10th International Conference, SocInfo 2018, St. Petersburg, Russia, September 25-28, 2018, Proceedings, Part I, Springer, 2018, Vol. 11185, p. 212-227Conference paper, Published paper (Refereed)
Abstract [en]

Multiplex networks are defined by the presence of multiple edge types. As a consequence, it is hard to produce a single visualization of a network revealing both the structure of each edge type and their mutual relationships: multiple visualization strategies are possible, depending on how each edge type should influence the position of the nodes in the sociogram. In this paper we introduce multiforce, a force-directed layout for multiplex networks where both intra-layer and inter-layer relationships among nodes are used to compute node coordinates. Despite its simplicity, our algorithm can reproduce the main existing approaches to draw multiplex sociograms, and also supports a new intermediate type of layout. Our experiments on real data show that multiforce enables layered visualizations where each layer represents an edge type, nodes are well aligned across layers and the internal layout of each layer highlights the structure of the corresponding edge type.

Place, publisher, year, edition, pages
Springer, 2018
Series
Lecture Notes in Computer Science, E-ISSN 1611-3349 ; 0302-9743
Keywords
Visualization, Multiplex network, Layout, Force-directed
National Category
Other Computer and Information Science Computer Sciences
Identifiers
urn:nbn:se:uu:diva-368790 (URN)10.1007/978-3-030-01129-1_13 (DOI)000476935500013 ()978-3-030-01128-4 (ISBN)978-3-030-01129-1 (ISBN)
Conference
10th International Conference on Social Informatics (SocInfo), SEP 25-28, 2018, St Petersburg, RUSSIA
Funder
EU, Horizon 2020, 732027
Available from: 2018-12-07 Created: 2018-12-07 Last updated: 2019-08-22Bibliographically approved
Vega, D. & Magnani, M. (2018). Foundations of Temporal Text Networks. Applied Network Science, 3(25)
Open this publication in new window or tab >>Foundations of Temporal Text Networks
2018 (English)In: Applied Network Science, Vol. 3, no 25Article in journal (Refereed) Published
Abstract [en]

Three fundamental elements to understand human information networks are the individuals (actors) in the network, the information they exchange, that is often observable online as text content (emails, social media posts, etc.), and the time when these exchanges happen. An extremely large amount of research has addressed some of these aspects either in isolation or as combinations of two of them. There are also more and more works studying systems where all three elements are present, but typically using ad hoc models and algorithms that cannot be easily transfered to other contexts. To address this heterogeneity, in this article we present a simple, expressive and extensible model for temporal text networks, that we claim can be used as a common ground across different types of networks and analysis tasks, and we show how simple procedures to produce views of the model allow the direct application of analysis methods already developed in other domains, from traditional data mining to multilayer network mining.

National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:uu:diva-368785 (URN)0.1007/s41109-018-0082-3 (DOI)
Funder
EU, Horizon 2020, 727040
Available from: 2018-12-07 Created: 2018-12-07 Last updated: 2019-01-30Bibliographically approved
Hanteer, O., Rossi, L., Vega, D. & Magnani, M. (2018). From Interaction to Participation: The Role of the Imagined Audience in Social Media Community Detection and an Application to Political Communication on Twitter. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM): . Paper presented at IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM),Barcelona, Spain, August 28-31, 2018 (pp. 531-534). IEEE Computer Society
Open this publication in new window or tab >>From Interaction to Participation: The Role of the Imagined Audience in Social Media Community Detection and an Application to Political Communication on Twitter
2018 (English)In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), IEEE Computer Society , 2018, p. 531-534Conference paper, Published paper (Refereed)
Abstract [en]

In the context of community detection in online social media, a lot of effort has been put into the definition of sophisticated network clustering algorithms and much less on the equally crucial process of obtaining high-quality input data. User-interaction data explicitly provided by social media platforms has largely been used as the main source of data because of its easy accessibility. However, this data does not capture a fundamental and much more frequent type of participatory behavior where users do not explicitly mention others but direct their messages to an invisible audience following a common hashtag. In the context of multiplex community detection, we show how to construct an additional data layer about user participation not relying on explicit interactions between users, and how this layer can be used to find different types of communities in the context of Twitter political communication.

Place, publisher, year, edition, pages
IEEE Computer Society, 2018
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:uu:diva-368788 (URN)10.1109/ASONAM.2018.8508575 (DOI)000455640600086 ()978-1-5386-6051-5 (ISBN)
Conference
IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM),Barcelona, Spain, August 28-31, 2018
Funder
EU, Horizon 2020, 727040
Available from: 2018-12-07 Created: 2018-12-07 Last updated: 2019-08-01
Atzmueller, M., Gaito, S., Interdonato, R., Kanawati, R., Largeron, C., Magnani, M. & Sala, A. (2018). International Workshop on Mining Attributed Networks (MATNET 2018) Chairs’ Welcome. ACM
Open this publication in new window or tab >>International Workshop on Mining Attributed Networks (MATNET 2018) Chairs’ Welcome
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2018 (English)Other (Other academic)
Place, publisher, year, pages
ACM, 2018
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:uu:diva-368786 (URN)
Available from: 2018-12-07 Created: 2018-12-07 Last updated: 2018-12-07
Tehrani, N. A. & Magnani, M. (2018). Partial and Overlapping Community Detection in Multiplex Social Networks. In: Social Informatics - 10th International Conference: . Paper presented at Social Informatics, September 25–28, 2018 Saint Petersburg, Russia (pp. 15-28). Springer, 11186
Open this publication in new window or tab >>Partial and Overlapping Community Detection in Multiplex Social Networks
2018 (English)In: Social Informatics - 10th International Conference, Springer , 2018, Vol. 11186, p. 15-28Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Springer, 2018
Series
Lecture Notes in Computer Science
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:uu:diva-368787 (URN)
Conference
Social Informatics, September 25–28, 2018 Saint Petersburg, Russia
Funder
EU, Horizon 2020, 727040
Available from: 2018-12-07 Created: 2018-12-07 Last updated: 2019-08-01
Brodka, P., Chmiel, A., Magnani, M. & Ragozini, G. (2018). Quantifying layer similarity in multiplex networks: a systematic study. Royal Society Open Science, 5(8), Article ID 171747.
Open this publication in new window or tab >>Quantifying layer similarity in multiplex networks: a systematic study
2018 (English)In: Royal Society Open Science, E-ISSN 2054-5703, Vol. 5, no 8, article id 171747Article in journal (Refereed) Published
Abstract [en]

Computing layer similarities is an important way of characterizing multiplex networks because various static properties and dynamic processes depend on the relationships between layers. We provide a taxonomy and experimental evaluation of approaches to compare layers in multiplex networks. Our taxonomy includes, systematizes and extends existing approaches, and is complemented by a set of practical guidelines on how to apply them.

Place, publisher, year, edition, pages
ROYAL SOC, 2018
Keywords
multiplex networks, layer similarity, network similarity, property matrix
National Category
Computer Sciences
Identifiers
urn:nbn:se:uu:diva-364470 (URN)10.1098/rsos.171747 (DOI)000443443000008 ()30224981 (PubMedID)
Funder
EU, Horizon 2020, 691152EU, Horizon 2020, 732027
Available from: 2018-10-31 Created: 2018-10-31 Last updated: 2018-10-31Bibliographically approved
Magnani, M. & Wasserman, S. (2017). Introduction to the special issue on multilayer networks. NETWORK SCIENCE, 5(2), 141-143
Open this publication in new window or tab >>Introduction to the special issue on multilayer networks
2017 (English)In: NETWORK SCIENCE, ISSN 2050-1242, Vol. 5, no 2, p. 141-143Article in journal, Editorial material (Other academic) Published
Place, publisher, year, edition, pages
CAMBRIDGE UNIV PRESS, 2017
National Category
Other Social Sciences not elsewhere specified
Identifiers
urn:nbn:se:uu:diva-332746 (URN)10.1017/nws.2017.15 (DOI)000402960800001 ()
Available from: 2017-11-09 Created: 2017-11-09 Last updated: 2017-11-09Bibliographically approved
Ghariblou, S., Salehi, M., Magnani, M. & Jalili, M. (2017). Shortest Paths in Multiplex Networks. Scientific Reports, 7, Article ID 2142.
Open this publication in new window or tab >>Shortest Paths in Multiplex Networks
2017 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, article id 2142Article in journal (Refereed) Published
Abstract [en]

The shortest path problem is one of the most fundamental networks optimization problems. Nowadays, individuals interact in extraordinarily numerous ways through their offline and online life (e.g., co-authorship, co-workership, or retweet relation in Twitter). These interactions have two key features. First, they have a heterogeneous nature, and second, they have different strengths that are weighted based on their degree of intimacy, trustworthiness, service exchange or influence among individuals. These networks are known as multiplex networks. To our knowledge, none of the previous shortest path definitions on social interactions have properly reflected these features. In this work, we introduce a new distance measure in multiplex networks based on the concept of Pareto efficiency taking both heterogeneity and weighted nature of relations into account. We then model the problem of finding the whole set of paths as a form of multiple objective decision making and propose an exact algorithm for that. The method is evaluated on five real-world datasets to test the impact of considering weights and multiplexity in the resulting shortest paths. As an application to find the most influential nodes, we redefine the concept of betweenness centrality based on the proposed shortest paths and evaluate it on a real-world dataset from two-layer trade relation among countries between years 2000 and 2015.

National Category
Computer Sciences
Identifiers
urn:nbn:se:uu:diva-348806 (URN)10.1038/s41598-017-01655-x (DOI)000425896000001 ()28526822 (PubMedID)
Available from: 2018-05-04 Created: 2018-05-04 Last updated: 2018-05-04Bibliographically approved
Sharma, R., Magnani, M. & Montesi, D. (2016). Effects of missing data in multilayer networks. Social Network Analysis and Mining, 6(1)
Open this publication in new window or tab >>Effects of missing data in multilayer networks
2016 (English)In: Social Network Analysis and Mining, ISSN 1869-5450, E-ISSN 1869-5469, Vol. 6, no 1Article in journal (Refereed) Published
Abstract [en]

A common problem in social network analysis is the presence of missing data. This problem has been extensively investigated in single-layer networks, that is, considering one network at a time. However, in multilayer networks, in which a holistic view of multiple networks is taken, the problem has not been specifically studied. In this work, we take an exhaustive and systematic approach to understand the effect of missing data in multilayer networks. Differently from the single-layer networks, depending on layer interdependencies, the common network properties can increase or decrease with respect to the properties of the complete network. Another important aspect we observed through our experiments on six real and eleven synthetic datasets is that multilayer network properties like layer correlation and relevance can be used to understand the impact of missing data compared to measuring traditional network measures.

Keywords
Missing data, Multilayer networks, Social network analysis
National Category
Computer Sciences
Identifiers
urn:nbn:se:uu:diva-304132 (URN)10.1007/s13278-016-0384-3 (DOI)000382364400012 ()
Available from: 2016-10-04 Created: 2016-10-03 Last updated: 2018-01-14Bibliographically approved
Ashcroft, M., Magnani, M., Vega, D., Montesi, D. & Rossi, L. (2016). Multilayer Analysis of Online Illicit Marketplaces. In: Brynielsson, J Johansson, F (Ed.), 2016 European Intelligence And Security Informatics Conference (EISIC): . Paper presented at Conference on European Intelligence and Security Informatics Conference (EISIC), AUG 17-19, 2016, Uppsala, SWEDEN (pp. 199-199). IEEE
Open this publication in new window or tab >>Multilayer Analysis of Online Illicit Marketplaces
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2016 (English)In: 2016 European Intelligence And Security Informatics Conference (EISIC) / [ed] Brynielsson, J Johansson, F, IEEE , 2016, p. 199-199Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2016
Series
European Intelligence and Security Informatics Conference, ISSN 2572-3723
National Category
Computer Sciences
Identifiers
urn:nbn:se:uu:diva-346563 (URN)10.1109/EISIC.2016.47 (DOI)000411272300042 ()978-1-5090-2857-3 (ISBN)
Conference
Conference on European Intelligence and Security Informatics Conference (EISIC), AUG 17-19, 2016, Uppsala, SWEDEN
Available from: 2018-03-19 Created: 2018-03-19 Last updated: 2018-03-19Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-3437-9018

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