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The trust value between two nodes is important for their interactions in social network . In this paper we propose an ACO-based trust inference algorithm. First, we use the Ant Colony Optimization algorithm to find two trust trains with high trust value between any two nodes, since the most accurate information comes from the node with the highest trust value. We optimized the corresponding parameters...
Community detection is an important technique to understand the structure of complex social networks. Many approaches have been devised to extract community structures in recent years. In this paper we propose a novel neighborhood vector propagation algorithm (NVPA) to detect communities in a social network which has greater accuracy than algorithms in the literature. In our approach, a neighborhood...
A Social network structure contains several nodes which are connected based on the relationships. Network community mining methods are used discover all hidden communities in distributed social networks based on some criteria. Several algorithms have been developed to solve the hidden community mining problem. In a given network the links between the nodes are opaque, but few nodes are thin. Finding...
Community mining has been the focus of many recent researches on dynamic social networks. In this paper, we propose a clustering based improved ant colony algorithm (CIACA) for community mining in social networks. The CIACA combines the local pheromone update rule with the global update rule and utilizes heuristic function to adjust the clustering solution dynamically, assisted by decay coefficient...
Summary form only given. In this talk, we will present how semantics can improve the quality of the data mining process. In particular, first, we will focus on geospatial schema matching with high quality cluster assurance. Next, we will focus on location mining from social network. With regard to the first problem, resolving semantic heterogeneity across distinct data sources remains a highly relevant...
As the Web contains rich and convenient information, Web search engine is increasingly becoming the dominant information retrieving approach. In order to rank the query results of web pages in an effective and efficient fashion, we propose a new page rank algorithm based on similarity measure from the vector space model, called SimRank, to score web pages. Firstly, we propose a new similarity measure...
Many systems in sciences, engineering and nature can be modeled as networks. Examples include the Internet, WWW and social networks. Finding hidden structures is important for making sense of complex networked data. In this paper we present a new network clustering method that can find clusters in an agglomerative fashion using structural similarity of vertices in the given network. Experiments conducted...
Communities in social networks may overlap, with some hub nodes belonging to multiple communities. They may also have outliers, which are nodes that belong to no community. The criterion to locate hubs or outliers is network dependent. Previous methods usually require this information as input parameters, e.g., an expected number of communities, with no intuition or assistance. Here we present a visual...
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