首页|Peter the Great St. Petersburg Polytechnic University Researcher Reveals New Fin dings on Machine Learning (Algorithm for link prediction in self-regulating netw ork with adaptive topology based on graph theory and machine learning)
Peter the Great St. Petersburg Polytechnic University Researcher Reveals New Fin dings on Machine Learning (Algorithm for link prediction in self-regulating netw ork with adaptive topology based on graph theory and machine learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews-Investigators publish new report on artificial in telligence. According to news originating from Peterthe Great St. Petersburg Po lytechnic University by NewsRx correspondents, research stated, "The paperprese nts a graph model of the functioning of a network with adaptive topology, where the network nodesrepresent the vertices of the graph, and data exchange between the nodes is represented as edges."Our news editors obtained a quote from the research from Peter the Great St. Pet ersburg PolytechnicUniversity: "The dynamic nature of network interaction compl icates the solution of the task of monitoring and controlling the functioning of a network with adaptive topology, which must be performed to ensureguaranteed correct network interaction. The importance of solving such a problem is justifi ed by thecreation of modern information and cyber-physical systems, which are b ased on networks with adaptivetopology. The dynamic nature of links between nod es, on the one hand, allows to provide self-regulationof the network, on the ot her hand, significantly complicates the control over the network operation duet o the impossibility of identifying a single pattern of network interaction. On t he basis of the developedmodel of network functioning with adaptive topology, a graph algorithm for link prediction is proposed,which is extended to the case of peer-to-peer networks. The algorithm is based on significant parametersof ne twork nodes, characterizing both their physical characteristics (signal level, b attery charge) and theircharacteristics as objects of network interaction (char acteristics of centrality of graph nodes)."
Peter the Great St. Petersburg Polytechn ic UniversityAlgorithmsCyborgsEmerging TechnologiesMachine LearningMat hematical Theories
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