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首页> 外文期刊>International Journal of Web Based Communities >Towards realistic artificial benchmark for community detection algorithms evaluation
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Towards realistic artificial benchmark for community detection algorithms evaluation

机译：朝着逼真的人工基准进行社区检测算法评估

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摘要

Many algorithms have been proposed for revealing the community structure in complex networks. Tests under a wide range of realistic conditions must be performed in order to select the most appropriate for a particular application. Artificially generated networks are often used for this purpose. The most realistic generative method to date has been proposed by l.ancichinetti, Fortunato and Radicchi (LER). However, it does not produce networks with some typical features of real-world networks. To overcome this drawback, we investigate two alternative modifications of this algorithm. Experimental results show that in both eases, centralisation and degree correlation values of generated networks are closer to those encountered in real-world networks. The three benchmarks have been used on a wide set of prominent community detection algorithms in order to reveal the limits and the robustness of the algorithms. Results show that the detection of meaningful communities gets harder with more realistic networks, and particularly when the proportion of inter-community links increases.
机译：已经提出了许多算法来揭示复杂网络中的社区结构。为了选择最适合特定应用的实际条件，必须进行各种测试。为此，通常使用人工生成的网络。 l.ancichinetti，Fortunato和Radicchi（LER）提出了迄今为止最现实的生成方法。但是，它不会生成具有现实网络某些典型功能的网络。为了克服此缺点，我们研究了该算法的两个替代修改。实验结果表明，所生成网络的集中度和相关度值在两个方面都更接近于实际网络中遇到的那些。为了揭示算法的局限性和鲁棒性，这三个基准已被广泛用于各种著名的社区检测算法。结果表明，通过更现实的网络发现有意义的社区变得更加困难，尤其是当社区间链接的比例增加时。

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