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Social networks are effective tools for analyzing many social topics in sociology. In the past few decades, a great deal of efforts have been made to study the balance property of social networks. This paper presents a novel bi-objective model for social network structural balance, and a multiobjective discrete particle swarm optimizer is used to optimize the bi-objective model. Each single run of...
Deep community can be detected by removing noise nodes or edges from a network. A centrality measure, named local Fiedler vector centrality is proposed for deep community detection. Algorithms to optimize local Fiedler vector centrality are either with high computation complexity or difficult to find the optimal solution of local Fiedler vector centrality. In this paper, a novel memetic algorithm...
There is an increasing recognition on community detection in complex networks in recent years. In this study, we improve a recently proposed memetic algorithm for community detection in networks. By introducing a Population Generation via Label Propagation (PGLP) tactic, an Elitism Strategy (ES) and an Improved Simulated Annealing Combined Local Search (ISACLS) strategy, the improved memetic algorithm...
Cooperation is a key issue of multi-agent system (MAS), pursuit problem as a test platform of multi-agent system and learning algorithm is widely used for testing the performance of cooperation, learning method and communication of MAS. To reduce the large computation of current pursuit methods such as the shortest distance first and contract net protocol, a method of applying pheromone-based ant...
In this paper, we apply TDT technology to the vertical search engine in the financial field. The returned results are grouped into several topics with the stock as the unit. Then we show the topics to the users in time series order. As a result, users can easily learn about the important events which belong to a stock. Moreover, the causes and the effects of these events can also be found out easily...
The algorithm of locally adaptive clustering for high dimensional data (LAC) processes soft subspace clustering by local weightings of features. To solve the localization of LAC in specifying the number of clusters, this paper reworks the validity index for fuzzy clustering to evaluate the clustering results of LAC. Compared with real clustered data, the method is proved feasible. In the new algorithm,...
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