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INSA de Rennes (10/12/2010), Anne-Marie Kermarrec (Dir.)
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Distributing Social Applications
Vincent Leroy1

The so-called Web 2.0 revolution has fundamentally changed the way people interact with the Internet. The Web has turned from a read-only infrastructure to a collaborative platform. By expressing their preferences and sharing private information, the users benefit from a personalized Web experience. Yet, these systems raise several problems in terms of \emph{privacy} and \emph{scalability}. The social platforms use the user information for commercial needs and expose the privacy and preferences of the users. Furthermore, centralized personalized systems require costly data-centers. As a consequence, existing centralized social platforms do not exploit the full extent of the personalization possibilities. In this thesis, we consider the design of social networks and social information services in the context of \emph{peer-to-peer} (P2P) networks. P2P networks are decentralized architecture, thus the users participates to the service and control their own data. This greatly improves the privacy of the users and the scalability of the system. Nevertheless, building social systems in a distributed context also comes with many challenges. The information is distributed among the users and the system has be able to efficiently locate relevant data. The contributions of this thesis are as follow. We define the \emph{cold start link prediction} problem, which consists in predicting the edges of a social network solely from the social information of the users. We propose a method based on a \emph{probabilistic graph} to solve this problem. We evaluate it on a dataset from Flickr, using the group membership as social information. Our results show that the social information indeed enables a prediction of the social network. Thus, the centralization of the information threatens the privacy of the users, hence the need for decentralized systems. We propose \textsc{SoCS}, a \emph{decentralized} algorithm for \emph{link prediction}. Recommending neighbors is a central functionality in social networks, and it is therefore crucial to propose a decentralized approach as a first step towards P2P social networks. \textsc{SoCS} relies on gossip protocols to perform a force-based embedding of the social networks. The social coordinates are then used to predict links among vertices. We show that \textsc{SoCS} is adapted to decentralized systems at it is churn resilient and has a low bandwidth consumption. We propose \textsc{GMIN}, a \emph{decentralized} platform for \emph{personalized services} based on social information. \textsc{GMIN} provides each user with neighbors that share her interests. The clustering algorithm we propose takes care to encompass all the different interests of the user, and not only the main ones. We then propose a personalized \emph{query expansion} algorithm (\textsc{GQE}) that leverages the \textsc{GMIN} neighbors. For each query, the system computes a tag centrality based on the relations between tags as seen by the user and her neighbors.
1:  INRIA - IRISA - ASAP
pair-à-pair – réseaux sociaux – folksonomy – expansion de requêtes – plongement de graphe – prédiction de liens

Distributing Social Applications
The so-called Web 2.0 revolution has fundamentally changed the way people interact with the Internet. The Web has turned from a read-only infrastructure to a collaborative platform. By expressing their preferences and sharing private information, the users benefit from a personalized Web experience. Yet, these systems raise several problems in terms of \emph{privacy} and \emph{scalability}. The social platforms use the user information for commercial needs and expose the privacy and preferences of the users. Furthermore, centralized personalized systems require costly data-centers. As a consequence, existing centralized social platforms do not exploit the full extent of the personalization possibilities. In this thesis, we consider the design of social networks and social information services in the context of \emph{peer-to-peer} (P2P) networks. P2P networks are decentralized architecture, thus the users participates to the service and control their own data. This greatly improves the privacy of the users and the scalability of the system. Nevertheless, building social systems in a distributed context also comes with many challenges. The information is distributed among the users and the system has be able to efficiently locate relevant data. The contributions of this thesis are as follow. We define the \emph{cold start link prediction} problem, which consists in predicting the edges of a social network solely from the social information of the users. We propose a method based on a \emph{probabilistic graph} to solve this problem. We evaluate it on a dataset from Flickr, using the group membership as social information. Our results show that the social information indeed enables a prediction of the social network. Thus, the centralization of the information threatens the privacy of the users, hence the need for decentralized systems. We propose \textsc{SoCS}, a \emph{decentralized} algorithm for \emph{link prediction}. Recommending neighbors is a central functionality in social networks, and it is therefore crucial to propose a decentralized approach as a first step towards P2P social networks. \textsc{SoCS} relies on gossip protocols to perform a force-based embedding of the social networks. The social coordinates are then used to predict links among vertices. We show that \textsc{SoCS} is adapted to decentralized systems at it is churn resilient and has a low bandwidth consumption. We propose \textsc{GMIN}, a \emph{decentralized} platform for \emph{personalized services} based on social information. \textsc{GMIN} provides each user with neighbors that share her interests. The clustering algorithm we propose takes care to encompass all the different interests of the user, and not only the main ones. We then propose a personalized \emph{query expansion} algorithm (\textsc{GQE}) that leverages the \textsc{GMIN} neighbors. For each query, the system computes a tag centrality based on the relations between tags as seen by the user and her neighbors.
peer-to-peer – social networks – folksonomy – query expansion – embedding – link prediction

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