An overlapping community detection algorithm for opportunistic networks

Xuebin Ma, Zhenchao Ouyang, Lin Bai, Xin Zhan, Xiangyu Bai
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引用次数: 6

Abstract

A more detailed community structure can contribute to a better understanding of the network, which can also benefit efficient routing protocols and QoS schemes designing. For an Opportunistic Network which consists of different kinds of mobile nodes, its topology changes over time. Therefore the community detection becomes more difficult than static situations. Moreover the overlapping community detection is a more complex problem. This paper analyzes the time varying topology of Opportunistic Networks and the overlapping community structures of human. Then, we propose a new detection algorithm to solve the overlapping community detection problems in Opportunistic Networks. Only with the local network topology information and a short period, nodes can get their overlapping community structures by our detection algorithm. Numerical simulations with both scenarios of movement models and real trace data are presented to illustrate the accuracy and efficiency of our algorithm.
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机会网络的重叠社团检测算法
更详细的社区结构有助于更好地理解网络,这也有利于有效的路由协议和QoS方案的设计。对于由不同类型的移动节点组成的机会网络,其拓扑结构随时间而变化。因此,社区检测变得比静态情况更加困难。此外,重叠社区检测是一个更为复杂的问题。本文分析了机会主义网络的时变拓扑结构和人类的重叠社区结构。然后,我们提出了一种新的检测算法来解决机会网络中的重叠社区检测问题。我们的检测算法只需要利用局部网络拓扑信息和较短的周期,就可以得到节点的重叠社团结构。通过运动模型和真实轨迹数据两种情况下的数值仿真,验证了算法的准确性和高效性。
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