Based on Spark distributed graph computing model, a large scale and complex network community parallel discovery algorithm DBCS is proposed.
基于Spark分布式图计算模型，提出大规模复杂网络社区并行发现算法DBCS。
With the widespread use of the Internet, the network community gradually shows enormous commercial values, and evolutes to be a virtual brand community.
随着互联网的广泛使用，网络社区逐渐显示出巨大的商业价值，演变为虚拟品牌社区。
With bi-partitioning strategy, by maximizing the module density, an algorithm is proposed based on discrete quantum particle swarm optimization for complex network community detection.
采用二分策略，通过最大化模块密度，提出了基于离散量子粒子群优化进行复杂网络社区检测的算法。
To overcome the resolution limits drawback of modularity function, a new measure of modularity density in complex network community detection is studied.
针对模块度存在的解限制问题，分析了复杂网络社区检测中一种新的测度模块密度。
This helps in advertising and promotions as well as linking one Social Network community with others.
这有助于宣传和推广社区，并将某个社交网络社区与其他社区连接起来。
Network community is an important means of improving the learners' cognitive level, the learners' implicit knowledge plays an important role in the cognitive process.
网络社群是学习者提高认知水平的一种重要方式，社群中学习者的隐性知识在其认知过程中发挥着重要的作用。
Network community is an important means of improving the learners' cognitive level, the learners' implicit knowledge plays an important role in the cognitive process.
网络社群是学习者提高认知水平的一种重要方式，社群中学习者的隐性知识在其认知过程中发挥着重要的作用。
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