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- Clique communities in social networksPublication . Cavique, Luís; Mendes, Armando B.; Santos, Jorge M. A.Given the large amount of data provided by the Web 2.0, there is a pressing need to obtain new metrics to better understand the network structure; how their communities are organized and the way they evolve over time. Complex network and graph mining metrics are essentially based on low complexity computational procedures like the diameter of the graph, clustering coefficient and the degree distribution of the nodes. The connected communities in the social networks have, essentially, been studied in two contexts: global metrics like the clustering coefficient and the node groups, such as the graph partitions and clique communities.
- An algorithm to discover the k-clique cover in networksPublication . Cavique, Luís; Mendes, Armando B.; Santos, Jorge M. A.In social network analysis, a k-clique is a relaxed clique, i.e., a k-clique is a quasi-complete sub-graph. A k-clique in a graph is a sub-graph where the distance between any two vertices is no greater than k. The visualization of a small number of vertices can be easily performed in a graph. However, when the number of vertices and edges increases the visualization becomes incomprehensible. In this paper, we propose a new graph mining approach based on k-cliques. The concept of relaxed clique is extended to the whole graph, to achieve a general view, by covering the network with k-cliques. The sequence of k-clique covers is presented, combining small world concepts with community structure components. Computational results and examples are presented.
- Estudo dos próximos locais a visitar na Rede FoursquarePublication . Bilro, Eudália; Cavique, LuísA comunicação nas redes sociais surgiu da necessidade que o ser humano tem em partilhar assuntos, ideias, preferências comuns criando assim laços assentes em afinidades. A constante presença dos utilizadores nas redes sociais expressando as suas opiniões sobre produtos, marcas, pessoas, gostos, ou costumes tem vindo a desencadear um grande interesse por parte de empresas e pessoas em analisar essas informações. Numa sociedade que diariamente é capaz de produzir dados em massa, é cada vez mais necessária a criação de ferramentas para a sua análise e interpretação de forma a disponibilizar todo um conjunto de informações úteis para a tomada de decisões. Neste contexto, este trabalho descreve o processo de aplicação de técnicas de Data Mining em dados extraídos da rede social Foursquare de forma a obter informações relevantes que auxiliem na identificação de padrões de comportamentos. Através da descoberta de padrões sequenciais, este estudo irá permitir a visualização dos dados organizados numa poli-árvore com o objetivo de estudar os próximos locais a visitar na rede Foursquare.
- An algorithm to condense social networks and identify brokersPublication . Cavique, Luís; Marques, Nuno C.; Santos, Jorge M. A.In social network analysis the identification of communities and the discovery of brokers is a very important issue. Community detection typically uses partition techniques. In this work the information extracted from social networking goes beyond cohesive groups, enabling the discovery of brokers that interact between communities. The partition is found using a set covering formulation, which allows the identification of actors that link two or more dense groups. Our algorithm returns the needed information to create a good visualization of large networks, using a condensed graph with the identification of the brokers.
- Brokerage discovery in social networksPublication . Cavique, LuísIn social networks two types of measures can be identified, the structural measures and community structure based on diameter and centrality. The community structure usually deals with network partition into communities. The key idea of this work is to explore the concept of strong and weak ties by finding brokers within communities. The strict partition problem is relaxed into a bi-objective set covering problem with k-cliques which allows over-covered and uncovered nodes. The information extracted from social networking goes beyond cohesive groups, allowing the finding of brokers that interact between groups.