Implementing Federated Social Networking

Silva, Gabriel; Reis, Larissa Brussa; Terceiro, Antonio; Meirelles, Paulo; Kon, Fábio

doi:10.1145/3125433.3125455

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…For instance, we may find a federation of distributed Diaspora providers/instances or a federation of distributed Mastodon providers/instances; but, we cannot have a federation combining Diaspora and Mastodon providers/instances without modifying the underlying protocols of those OSNSs. Few solutions offer the federation of heterogeneous open OSNSs, such as the one presented in [Silva et al 2017]; however, they are not well-developed yet and do not consider closed and non-profile-based OSNSs.…”

Section: Introductionmentioning

confidence: 99%

Universal Social Network Bus

Angarita

Lefevre

Ahvar

et al. 2019

ACM Trans. Internet Technol.

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Services (OSNSs) are changing the fabric of our society, impacting almost every aspect of it. Over the last decades, the aggressive market rivalry has led to the emergence of multiple competing, "closed" OSNSs. As a result, users are trapped in the walled gardens of their OSNS, encountering restrictions about what they can do with their personal data, the people they can interact with and the information they get access to. As an alternative to the platform lock-in, "open" OSNSs promote the adoption of open, standardized APIs. However, users still massively adopt closed OSNSs to benefit from the services' advanced functionalities and/or follow their "friends", although the users' virtual social sphere is ultimately limited by the OSNSs they join. Our work aims at overcoming such a limitation by enabling users to meet and interact beyond the boundary of their OSNSs, including reaching out to "friends" of distinct closed OSNSs. We specifically introduce USNB-Universal Social Network Bus, which revisits the "service bus" paradigm that enables interoperability across computing systems, to address the requirements of "social interoperability". USNB features synthetic profiles and personae for interaction across the boundaries of-closed and open- ,-profile-and non-profile-based-OSNSs through a reference social interaction service. We ran a 1-day workshop with a panel of users who experimented with the USNB prototype to assess the potential benefits of social interoperability for social network users. Results show the positive evaluation of users for USNB, especially as an enabler of applications for civic participation. This further opens up new perspectives for future work, among which, enforcing security and privacy guarantees.

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Section: Introductionmentioning

confidence: 99%

Universal Social Network Bus

Angarita

Lefevre

Ahvar

et al. 2019

ACM Trans. Internet Technol.

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“…In such a network, each node represents a social network account and each edge typically represents a friendship or follower/followee relationship between two accounts. In recent years, the growing concern over cyber privacy has led to the rise of federated social networks, such as Diaspora * , GNU Social, and pump.io [17]. In a federated social network, each edge between two accounts are stored on the servers that host these two accounts and is normally inaccessible to other servers.…”

Section: Introductionmentioning

confidence: 99%

MANELA: A Multi-Agent Algorithm for Learning Network Embeddings

Zhang,

2019

Preprint

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Playing an essential role in data mining, machine learning has a long history of being applied to networks on multifarious tasks and has played an essential role in data mining. However, the discrete and sparse natures of networks often render it difficult to apply machine learning directly to networks. To circumvent this difficulty, one major school of thought to approach networks using machine learning is via network embeddings. On the one hand, this network embeddings have achieved huge success on aggregated network data in recent years. On the other hand, learning network embeddings on distributively stored networks still remained understudied: To the best of our knowledge, all existing algorithms for learning network embeddings have hitherto been exclusively centralized and thus cannot be applied to these networks. To accommodate distributively stored networks, in this paper, we proposed a multiagent model. Under this model, we developed the multi-agent network embedding learning algorithm (MANELA) for learning network embeddings. We demonstrate MANELA's advantages over other existing centralized network embedding learning algorithms both theoretically and experimentally. Finally, we further our understanding in MANELA via visualization and exploration of its relationship to DeepWalk.

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Solving Multiagent Constraint Optimization Problems on the Constraint Composite Graph

Fioretto

Koenig

et al. 2018

Lecture Notes in Computer Science

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Implementing Federated Social Networking

Cited by 5 publications

References 7 publications

Universal Social Network Bus

Universal Social Network Bus

MANELA: A Multi-Agent Algorithm for Learning Network Embeddings

Solving Multiagent Constraint Optimization Problems on the Constraint Composite Graph

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