For expertise retrieval purposes we can rely on the endorsements received by members of a social network, with respect to some skill.Skills are correlated, which may affect individual rankings.An endorsement deduction method is proposed, which improves data consistency and completeness.Endorsement deduction makes use of the known correlation among skills.The method is validated in a synthetic network resembling LinkedIn. 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. Display Omitted
Another related field which has attained a growing interest in the last few years is that of reputation systems, that is, systems intended to rank the agents of a domain based on others' agents reports. Strategies for ranking agents in a reputation system range from a direct ranking by agents (as used in eBay) to more sophisticated approaches (see @cite_36 for a survey). One particularly important family of reputation system strategies is that of -based algorithms. There are many of such approaches. For instance, @cite_21 provides an algorithm based on the so-called Dirichlet , which addresses problems such as: (1) Some links in the network may indicate distrust rather than trust, and (2) How to infer a ranking for a node based on the ranking stated for a well-known subnetwork.
@cite_36 @cite_21
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Artificial Intelligence has become one of the most fundamental areas of computer science research. One line of research in artificial intelligence is associated with coordination of intelligent agents. Coordination takes place in multi-agent environments where distributed agents have limited knowledge about their surrounding environment; therefore, they continuously ask other agents to obtain required information. In general, the growing popularity of agents requires systematic coordination management and reputation system that enable agents to decide about their interacting partner and overall acting attitude. Using the reputation system, agents choose reliable agents to interact with. Here, the question arises that how the reputation mechanism helps agents to make the most prudent decisions that yield the best outcome. More specifically, agents need to use a decision-theoretic reasoning algorithm to optimize their decisions under uncertainty. In multi-agent systems, the reputation value is highly competitive because it is used as the beliefs of agents about one another. Thus, a carefully designed mechanism is required to maintain accuracy of this parameter. Motivated by numerous models of representing trust and distrust within a graph ranking system, we examine a quantitative vertex ranking with consideration of the influence of a subset of nodes. An efficient algorithm is given for computing Dirichlet PageRank vectors subject to Dirichlet boundary conditions on a subset of nodes. We then give several algorithms for various trust-based ranking problems using Dirichlet PageRank with boundary conditions, showing several applications of our algorithms.