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Stable Communities Detection Method for Temporal Multiplex Graphs: Heterogeneous Social Network Case Study
The Computer Journal ( IF 1.5 ) Pub Date : 2020-12-22 , DOI: 10.1093/comjnl/bxaa162
Wala Rebhi 1 , Nesrine Ben Yahia 1 , Narjès Bellamine Ben Saoud 1
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Multiplex graphs have been recently proposed as a model to represent high-level complexity in real-world networks such as heterogeneous social networks where actors could be characterized by heterogeneous properties and could be linked with different types of social interactions. This has brought new challenges in community detection, which aims to identify pertinent groups of nodes in a complex graph. In this context, great efforts have been made to tackle the problem of community detection in multiplex graphs. However, most of the proposed methods until recently deal with static multiplex graph and ignore the temporal dimension, which is a key characteristic of real networks. Even more, the few methods that consider temporal graphs, they just propose to follow communities over time and none of them use the temporal aspect directly to detect stable communities, which are often more meaningful in reality. Thus, this paper proposes a new two-step method to detect stable communities in temporal multiplex graphs. The first step aims to find the best static graph partition at each instant by applying a new hybrid community detection algorithm, which considers both relations heterogeneities and nodes similarities. Then, the second step considers the temporal dimension in order to find final stable communities. Finally, experiments on synthetic graphs and a real social network show that this method is competitive and it is able to extract high-quality communities.

中文翻译:

时间多重图的稳定社区检测方法:异构社会网络案例研究

近来,已经提出了多重图形作为模型,以表示诸如异构社会网络之类的现实世界网络中的高级复杂性,其中参与者可以通过异构属性来表征,并且可以与不同类型的社会互动联系在一起。这给社区检测带来了新挑战,该社区检测旨在识别复杂图中的相关节点组。在这种情况下,已经做出很大的努力来解决多重图中的社区检测问题。然而,直到最近,大多数提出的方法都处理静态复用图并且忽略了时间维,这是实际网络的关键特征。甚至还有一些考虑时间图的方法,他们只是建议随着时间的推移关注社区,他们中的任何一个都没有直接使用时间方面来检测稳定的社区,这在现实中通常更有意义。因此,本文提出了一种新的两步法来检测时间复用图中的稳定社区。第一步旨在通过应用一种新的混合社区检测算法,在每个瞬间找到最佳的静态图分区,该算法同时考虑了关系异质性和节点相似性。然后,第二步考虑时间维度,以找到最终的稳定社区。最后,在合成图和真实的社交网络上进行的实验表明,该方法具有竞争力,并且能够提取高质量的社区。本文提出了一种新的两步法来检测时间复用图中的稳定社区。第一步旨在通过应用一种新的混合社区检测算法,在每个瞬间找到最佳的静态图分区,该算法同时考虑了关系异质性和节点相似性。然后,第二步考虑时间维度,以找到最终的稳定社区。最后,在合成图和真实的社交网络上进行的实验表明,该方法具有竞争力,并且能够提取高质量的社区。本文提出了一种新的两步法来检测时间复用图中的稳定社区。第一步旨在通过应用一种新的混合社区检测算法,在每个瞬间找到最佳的静态图分区,该算法同时考虑了关系异质性和节点相似性。然后,第二步考虑时间维度,以找到最终的稳定社区。最后,在合成图和真实的社交网络上进行的实验表明,该方法具有竞争力,并且能够提取高质量的社区。第二步考虑时间维度,以便找到最终的稳定社区。最后,在合成图和真实的社交网络上进行的实验表明,该方法具有竞争力,并且能够提取高质量的社区。第二步考虑时间维度,以便找到最终的稳定社区。最后,在合成图和真实的社交网络上进行的实验表明,该方法具有竞争力,并且能够提取高质量的社区。
更新日期:2020-12-22
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