دانلود رایگان مقاله استنباط تایید و رتبه بندی در شبکه های اجتماعی

Abstract

Some social networks, such as LinkedIn and ResearchGate, allow user endorsements for specific skills. In this way, for each skill we get a directed graph where the nodes correspond to users’ profiles and the arcs represent endorsement relations. From the number and quality of the endorsements received, an authority score can be assigned to each profile. In this paper we propose an authority score computation method that takes into account the relations existing among different skills. Our method is based on enriching the information contained in the digraph of endorsements corresponding to a specific skill, and then applying a ranking method admitting weighted digraphs, such as PageRank. We describe the method, and test it on a synthetic network of 1493 nodes, fitted with endorsements.

5. Conclusions and future research

In this paper we describe endorsement deduction, a preprocessing algorithm to enrich the endorsement digraphs of a social network with endorsements, such as LinkedIn or ResearchGate, which can then be used in connection with a ranking method, such as PageRank, to compute an authority score of network members with respect to some desired skill. Endorsement deduction makes use of the relationships among the different skills, given by an ontology in the form of a skill deduction matrix. A preliminary set of experiments shows that the rankings obtained by this method do not differ much from the rankings obtained by simple PageRank, and that this method represents an improvement over simple PageRank with respect to two additional criteria: number of ties, and robustness to collusion spam. Our experiments also show that the benefits provided by PageRank with deduction are likely to increase in the future, with the densification of the endorsement networks, and the introduction of new skills. However, this has to be confirmed by larger-scale experiments.