Managing communities in decentralised social environments

Many decentralised systems can be represented as graphs, and the detection of their community structure can uncover important properties. Several community detection algorithms have been proposed, however, only a few solutions are suitable for detecting and managing communities in a distributed and highly dynamic environment. This lacking is mainly due to the difficulty of defining self-organising solutions in the presence of a high rate of dynamism. The main contribution of this paper is DISCO, a distributed protocol for community detection and management in a Peer-to-Peer dynamic environment. Our approach is mainly targeted to Decentralised Online Social Networks (DOSNs), but it can be applied in other distributed scenarios. In the context of DOSNs, DISCO allows to discover communities in the local social network of a user, named ego network, and to manage their evolution over time. DISCO is based on a Temporal Trade-off approach and exploits a set of super-peers for the management of the communities. The paper presents an extensive evaluation of the proposed approach based on a dataset gathered from Facebook and shows the ability of DISCO to orchestrate a set of nodes to detect and manage communities in a highly dynamic and decentralised environment. The paper also proposes a comparison with a state of the art approach, showing that it is capable of reducing the number of critical community lifecycle events by over 25%, and reducing the average loading factor by up to 50%.

Automated summary

This paper introduces a distributed protocol DISCO for detecting and managing communities in a distributed dynamic environment, specifically in Decentralised Online Social Networks (DOSNs). DISCO is based on a Temporal Trade-off approach and utilizes super-peers for community management. The paper evaluates DISCO using a dataset from Facebook and demonstrates its effectiveness in highly dynamic and distributed environments.