Section: New Results

Social Data Management and Crowdsourcing

In [13] , we focused on the issue of defining models and metrics for reciprocity in signed graphs. In unsigned directed networks, reciprocity quantifies the predisposition of network members in creating mutual connections. On the other hand, this concept has not yet been investigated in the case of signed graphs. We capitalize on the graph degeneracy concept to identify subgraphs of the signed network in which reciprocity is more likely to occur. This enables us to assess reciprocity at a global level, rather than at an exclusively local one as in existing approaches. The large scale experiments we perform on real world data sets of trust networks lead to both interesting and intuitive results. We believe these reciprocity measures can be used in various social applications such as trust management, community detection and evaluation of individual nodes. The global reciprocity we define in this paper is closely correlated to the clustering structure of the graph, more than the local reciprocity as it is indicated by the experimental evaluation we conducted.

As initial step towards better answering information needs in applications managing social content that is structured and possibly enriched with semantic annotations, in [20] , we present a preliminary data model and an approach for answering queries over structured, social and semantic-rich content, taking into account all dimensions of the data in order to return the most meaningful results.