| 1. |
- Hanteer, Obaida, et al.
(författare)
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From Interaction to Participation : The Role of the Imagined Audience in Social Media Community Detection and an Application to Political Communication on Twitter
- 2018
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Ingår i: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). - : IEEE Computer Society. - 9781538660515 ; , s. 531-534
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 2. |
- Hanteer, Obaida, et al.
(författare)
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Unspoken Assumptions in Multi-layer Modularity maximization
- 2020
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Ingår i: Scientific Reports. - : NATURE PUBLISHING GROUP. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- A principled approach to recover communities in social networks is to find a clustering of the network nodes into modules (i.e groups of nodes) for which the modularity over the network is maximal. This guarantees partitioning the network nodes into sparsely connected groups of densely connected nodes. A popular extension of modularity has been proposed in the literature so it applies to multi-layer networks, that is, networks that model different types/aspects of interactions among a set of actors. In this extension, a new parameter, the coupling strength omega, has been introduced to couple different copies (i.e nodes) of the same actor with specific weights across different layers. This allows two nodes that refer to the same actor to reward the modularity score with an amount proportional to omega when they appear in the same community. While this extension seems to provide an effective tool to detect communities in multi-layer networks, it is not always clear what kind of communities maximising the generalised modularity can identify in multi-layer networks and whether these communities are inclusive to all possible community structures possible to exist in multi-layer networks. In addition, it has not been thoroughly investigated yet how to interpret omega in real-world scenarios, and whether a proper tuning of omega, if exists, is enough to guarantee an accurate recoverability for different types of multi-layer community structures. In this article, we report the different ways used in the literature to tune omega. We analyse different community structures that can be recovered by maximising the generalised modularity in relation to omega. We propose different models for multi-layer communities in multiplex and time-dependent networks and test if they are recoverable by modularity-maximization community detection methods under any assignment of omega. Our main finding is that only few simple models of multi-layer communities in multiplex and time-dependent networks are recoverable by modularity maximisation methods while more complex models are not accurately recoverable under any assignment of omega.
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| 3. |
- Magnani, Matteo, et al.
(författare)
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Community Detection in Multiplex Networks
- 2021
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Ingår i: ACM Computing Surveys. - : Association for Computing Machinery (ACM). - 0360-0300 .- 1557-7341. ; 54:3
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Tidskriftsartikel (refereegranskat)abstract
- A multiplex network models different modes of interaction among same-type entities. In this article, we provide a taxonomy of community detection algorithms in multiplex networks. We characterize the different algorithms based on various properties and we discuss the type of communities detected by each method. We then provide an extensive experimental evaluation of the reviewed methods to answer three main questions: to what extent the evaluated methods are able to detect ground-truth communities, to what extent different methods produce similar community structures, and to what extent the evaluated methods are scalable. One goal of this survey is to help scholars and practitioners to choose the right methods for the data and the task at hand, while also emphasizing when such choice is problematic.
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| 4. |
- Rossi, Luca, et al.
(författare)
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Observing the tech, using meetup data to observe the evolution of the discourse around IoT
- 2019
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Ingår i: Selected Papers of Internet Research, SPIR. - : Annual Conference of the Association of Internet Researchers. - 2162-3317.
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes to use MeetUp data to study the emergence and the evolution of the technological trend commonly known as Internet of Things (IoT). Starting from a manually selected sample of 220 European MeetUp groups we used MeetUp's APIs, to retrieve additional information about the events and participating users. The final dataset consists of 220 groups, 32967 members and 2386 events from 2011 until now (Jan 2019). The results suggest the presence of clearly identifiable European hubs for IoT development but a worldwide crowd of users. From a temporal perspective the MeetUp data shows how IoT exploded in 2015 and how it might have peaked in 2017. Within this period of time IoT has not been a “stable technology” but as evolved incorporating, within its area of “related topics” new and emerging technologies such as Cryptocurrency or cloud computing.
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