The objective of a community detection algorithm is to group similar nodes that are more connected to each other than with the rest of the network. Several methods have been proposed but many are of high complexity and require global knowledge of the network, which makes them less suitable for large-scale networks. The Label Propagation Algorithm initially assigns a distinct label to each node that iteratively updates its label with the one of the majority of its neighbors, until consensus is reached among all nodes in the network. Nodes sharing the same label are then grouped into communities. It runs in near linear time and is decentralized, but it gets easily stuck in local optima and often returns a single giant community. To overcome these problems we propose MemLPA, a variation of the classical Label Propagation Algorithm where each node implements a memory mechanism that allows them to “remember” about past states of the network and uses a decision rule that takes this information into account. We demonstrate through extensive experiments, on the Lancichinetti-Fortunato-Radicchi benchmark and a set of real-world networks, that MemLPA outperforms other existing label propagation algorithms that implement memory and some of the well-known community detection algorithms. We also perform a topological analysis to extend the performance study and compare the topological properties of the communities found to the ground-truth community structure.

中文翻译：

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本地内存可促进标签传播以进行社区检测

社区检测算法的目的是对相似节点进行分组，这些相似节点彼此之间的联系比与网络其余部分的联系更多。已经提出了几种方法，但是许多方法具有很高的复杂性并且需要网络的全局知识，这使得它们不太适合大规模网络。标签传播算法最初为每个节点分配一个不同的标签，该节点反复使用其大多数邻居之一更新其标签，直到网络中所有节点之间达成共识为止。然后，将共享相同标签的节点分组为社区。它以接近线性的时间运行并且分散，但是很容易陷入局部最优状态，并且经常返回单个巨型社区。为了克服这些问题，我们提出了MemLPA，经典标签传播算法的一种变体，其中每个节点都实现了一种存储机制，该机制使它们可以“记住”网络的过去状态，并使用将这一信息考虑在内的决策规则。我们在Lancichinetti-Fortunato-Radicchi基准和一组实际网络上通过广泛的实验证明，MemLPA优于其他实现内存的现有标签传播算法和一些知名的社区检测算法。我们还将执行拓扑分析以扩展性能研究，并将发现的社区的拓扑属性与真实的社区结构进行比较。我们在Lancichinetti-Fortunato-Radicchi基准和一组实际网络上通过广泛的实验证明，MemLPA优于其他实现内存的现有标签传播算法和一些知名的社区检测算法。我们还将执行拓扑分析以扩展性能研究，并将发现的社区的拓扑属性与真实的社区结构进行比较。我们在Lancichinetti-Fortunato-Radicchi基准和一组实际网络上通过广泛的实验证明，MemLPA优于其他实现内存的现有标签传播算法和一些知名的社区检测算法。我们还将执行拓扑分析以扩展性能研究，并将发现的社区的拓扑属性与真实的社区结构进行比较。