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Meetup.com is a global online platform which facilitates the organisation of meetups in different parts of the world. A meetup group typically focuses on one specific topic of interest, such as sports, music, language, or technology. However, many users of this platform attend multiple meetups. On this basis, we can construct a co-membership network for a given location. This network encodes how pairs of meetups are connected to one another via common members. In this work we demonstrate that, by applying techniques from social network analysis to this type of representation, we can reveal the underlying meetup community structure, which is not immediately apparent from the platforms website. Specifically, we map the landscape of Dublins meetup communities, to explore the interests and activities of meetup.com users in the city.
To understand the formation, evolution, and function of complex systems, it is crucial to understand the internal organization of their interaction networks. Partly due to the impossibility of visualizing large complex networks, resolving network str
We here study the behavior of political party members aiming at identifying how ideological communities are created and evolve over time in diverse (fragmented and non-fragmented) party systems. Using public voting data of both Brazil and the US, we
Sequences of events including infectious disease outbreaks, social network activities, and crimes are ubiquitous and the data on such events carry essential information about the underlying diffusion processes between communities (e.g., regions, onli
As the COVID-19 pandemic is disrupting life worldwide, related online communities are popping up. In particular, two new communities, /r/China flu and /r/Coronavirus, emerged on Reddit and have been dedicated to COVID- related discussions from the ve
In network science, assortativity refers to the tendency of links to exist between nodes with similar attributes. In social networks, for example, links tend to exist between individuals of similar age, nationality, location, race, income, educationa