Bin Wu
ABSTRACT
With the development of Internet, we are gradually entering the era of big data. The size of network is increasing and the structure of network is becoming more complex. Community is a unique network structure with great research value. The task of community analysis goes through two separate phases: first, detection of meaningful community structure from a network, and second, evaluation of the appropriateness of the detected community structure. With the popularity of network research, many community detection algorithms emerged which can be grouped in categories, based on different criteria. In order to applicate community detection algorithms in real-world network analysis, we need to measure the performance of the algorithm. The performance depends on two points, that is, whether the algorithm can give the result of community division in an acceptable time, and whether the algorithm can reveal the community structure of the network with high quality. In recent years, systems used to analyze network and detect community are mushrooming. However, existing systems rarely have evaluation function, either providing social network analysis or providing data analysis service. We need a tool to evaluate community detection algorithms. In response to the challenge, the Community Evaluation System (CES) is proposed to meet the demands of community detection algorithms' evaluation. CES can evaluate community detection algorithms with multiple metrics. It uses B/S mode, integrates Spark, Yarn and HDFS technology to support the operation of large-scale data, and experiments prove that it is effective.
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