PeerSoN: P2P social networking: early experiences and insights
Proceedings of the Second ACM EuroSys Workshop on Social Network Systems
Probably Approximately Correct Search
ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory
Twittering by cuckoo: decentralized and socio-aware online microblogging services
Proceedings of the ACM SIGCOMM 2010 conference
PAC'nPost: a framework for a micro-blogging social network in an unstructured P2P network
Proceedings of the 21st international conference companion on World Wide Web
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We investigate the problem of identifying trending information in a peer-to-peer micro-blogging online social network. In a distributed decentralized environment, the participating nodes do not have access to global statistics such as the frequencies of the keywords and the information creation rate. We propose a two step solution. First, nodes make a local estimate of the frequency of keywords in the network based on their local information. At each iteration a subset of nodes collect this information from a small subset of random nodes in the network and aggregate the results. The most frequently occurring keywords are identified. In the second step, a node requests another small random subset of nodes to identify when, in the recent past, the more frequently occurring keywords were seen in micro-blogs. Once again this information is aggregated the fraction of time within a consecutive period that keywords were encountered is calculated. If this fraction, referred to as the trending fraction, is close to 1, then the keyword is predicted to be trending. A simulation on a network of 10,000 nodes shows that the solution is capable of detecting multiple trending keywords with a moderate increase in bandwidth.