Characterising Inter and Intra-Community Interactions in Link Streams Using Temporal Motifs

Creusefond, Jean; Cazabet, Rémy

doi:10.1007/978-3-319-54241-6_7

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…the tendency of individuals with the same attributes to participate in the same communication patterns. Creusefond et al [7] study temporal motifs by using the communities structure. From the experiments, they observe star and chain motifs are frequent inside communities where there are the same set of actors, instead spams, pingpong, and triangles occur in different communities (inter-community edges).…”

Section: Related Workmentioning

confidence: 99%

Incremental communication patterns in online social groups

et al. 2021

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In the last decades, temporal networks played a key role in modelling, understanding, and analysing the properties of dynamic systems where individuals and events vary in time. Of paramount importance is the representation and the analysis of Social Media, in particular Social Networks and Online Communities, through temporal networks, due to their intrinsic dynamism (social ties, online/offline status, users’ interactions, etc..). The identification of recurrent patterns in Online Communities, and in detail in Online Social Groups, is an important challenge which can reveal information concerning the structure of the social network, but also patterns of interactions, trending topics, and so on. Different works have already investigated the pattern detection in several scenarios by focusing mainly on identifying the occurrences of fixed and well known motifs (mostly, triads) or more flexible subgraphs. In this paper, we present the concept on the Incremental Communication Patterns, which is something in-between motifs, from which they inherit the meaningfulness of the identified structure, and subgraph, from which they inherit the possibility to be extended as needed. We formally define the Incremental Communication Patterns and exploit them to investigate the interaction patterns occurring in a real dataset consisting of 17 Online Social Groups taken from the list of Facebook groups. The results regarding our experimental analysis uncover interesting aspects of interactions patterns occurring in social groups and reveal that Incremental Communication Patterns are able to capture roles of the users within the groups.

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Section: Related Workmentioning

confidence: 99%

Incremental communication patterns in online social groups

et al. 2021

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Incremental communication patterns in blockchain online social media

Guidi

Michienzi

Ricci

2022

Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing

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Online Social Networking platforms are now more than ever part of people's everyday life. They often act as the medium through which people can communicate or discover engaging media. In recent years, thanks to the massive popularity gained by blockchain technology, a new generation of social media emerged. Steemit, one of the most well-known blockchain-based social networks, is based on the blockchain Steem. It employs the blockchain in two ways: as data storage, and to implement a rewarding mechanism for pieces of content that are relevant to the users. Employing a rewarding system based on the social activity of the users can have a strong impact on how people socialise. In this work, we study the interaction among the users of Steemit in terms of incremental patterns. In detail, we propose a set of incremental patterns, by using variants of patterns proposed in the literature and by defining a new pattern specifically thought for the scenario of Blockchain Online Social Media (BOSM). This paper's findings show that social interactions in BOSMs are highly conditioned by the presence of bots, and the patterns proposed can detect previously undetected complex interactions. CCS CONCEPTS• Information systems → Social networking sites; Social networks; Collaborative and social computing systems and tools;

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Characterising Inter and Intra-Community Interactions in Link Streams Using Temporal Motifs

Cited by 2 publications

References 18 publications

Incremental communication patterns in online social groups

Incremental communication patterns in online social groups

Incremental communication patterns in blockchain online social media

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