论文标题
社交网络中不同社区检测算法的亲和力功能的组合
Combinations of Affinity Functions for Different Community Detection Algorithms in Social Networks
论文作者
论文摘要
社交网络分析是社会和行为科学中流行的学科，在社会和行为科学中，不同社会实体之间的关系被建模为网络。社交网络分析中最受欢迎的问题之一是在其网络结构中找到社区。通常，社交网络中的社区是图表的功能子分区。但是，由于社区的定义在某种程度上是不精确的，因此已经提出了许多算法来解决这项任务，每个算法都集中在参与者和社区的不同社会特征上。在这项工作中，我们建议使用亲和力功能的新型组合，这些功能旨在捕获网络互动中的不同社会力学。我们使用它们扩展已经存在的社区检测算法，以结合与原始算法相比，亲和力功能对不同的社交互动进行建模的能力。
Social network analysis is a popular discipline among the social and behavioural sciences, in which the relationships between different social entities are modelled as a network. One of the most popular problems in social network analysis is finding communities in its network structure. Usually, a community in a social network is a functional sub-partition of the graph. However, as the definition of community is somewhat imprecise, many algorithms have been proposed to solve this task, each of them focusing on different social characteristics of the actors and the communities. In this work we propose to use novel combinations of affinity functions, which are designed to capture different social mechanics in the network interactions. We use them to extend already existing community detection algorithms in order to combine the capacity of the affinity functions to model different social interactions than those exploited by the original algorithms.
