RESUMOPredizer novos relacionamentos dentro de um grupo socialé uma tarefa complexa, porém extremamenteútil para potencializar ou maximizar colaborações por meio da indicação de quais seriam as parcerias mais promissoras. Nas redes sociais acadêmicas, a predição de relacionamentosé tipicamente utilizada para tentar identificar potenciais parceiros no desenvolvimento de um projeto e/ou coautores para a publicação de um artigo. Este artigo apresenta um sistema que combina técnicas de inteligência artificial com o estado da arte das métricas de predição de relacionamentos em redes sociais. O sistema resultante foi testado usando dados reais de pesquisadores em Ciência da Computação e atingiu uma precisão superior a 99,5% na predição de novas coautorias. Palavras-Chavepredição de relacionamentos, redes sociais, redes acadêmicas ABSTRACT The prediction of new relationships in a social network is a complex and extremely useful task to enhance or maximize collaborations by indicating what the most promising partnerships are. In academic social networks, prediction of relationships is typically used to try to identify potential partners in the development of a project and/or co-authors for publishing papers. This paper presents a system that combines artificial intelligence techniques with the state-ofthe-art metrics for link prediction. The resulting system was
The exponential growth and dramatic increase in demand for network bandwidth is expanding the market for broadband satellite networks. It is critical to rapidly deliver ubiquitous satellite communication networks that are differentiated by lower cost and increased Quality of Service (QoS). There is a need to develop new network architectures, control and management systems to meet the future commercial and military traffic requirements, services and applications. The next generation communication networks must support legacy and emerging network traffic while providing user negotiated levels of QoS. Network resources control algorithms must be designed to provide the guaranteed performance levels for voice, video and data having different service requirements. To evaluate network architectures and performance, it is essential to understand the network traffic characteristics.The traffic models and methodologies described in this paper are based on the data captured by the INMS from a Lockheed Martin network segment. The traffic sampling is representative of a network segment within a multinational corporation requiring high bandwidth network connectivity across geographically dispersed locations. The data set includes Simple Network Management Protocol (SNMP), Remote Monitoring (RMON) and Packet Trace metrics. The INMS data archive is continually expanded to include additional time and topological sampling points for further analysis and trend studies. Samples were selected to develop traffic flow characteristics for network protocols to be used to specify flows for performance, accounting and bundling. The HyNeT provides "hardware in the loop" simulation for high fidelity analysis of communication network architectures utilizing the traffic models. Currently, the traffic data is being used to characterize the future traffic demands for an advanced military network system. NETWORK TOPOLOGYThe data samples were collected at the uplink interface for the local segment consisting of approximately 50subnets supporting 7400 workstations. This point of interest, depicted in Fig. 1, reference point (A), is a candidate for satellite replacement, augmentation or secondary backup. The Local Area Network (LAN) segment is currently connected into the
The exponential growth and dramatic increase in demand for network bandwidth is expanding the market for broadband satellite networks. It is critical to rapidly deliver ubiquitous satellite communication networks that are differentiated by lower cost and increased Quality of Service (QoS). There is a need to develop new network architectures, control and management systems to meet the future commercial and military traffic requirements, services and applications. The next generation communication networks must support legacy and emerging network traffic while providing user negotiated levels of QoS. Network resources control algorithms must be designed to provide the guaranteed performance levels for voice, video and data having different service requirements. To evaluate network architectures and performance, it is essential to understand the network traftic characteristics.
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