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Overlapping communities and roles in networks with node attributes: Probabilistic graphical modeling, Bayesian formulation and variational inference
Artificial Intelligence ( IF 14.4 ) Pub Date : 2021-08-23 , DOI: 10.1016/j.artint.2021.103580
Gianni Costa 1 , Riccardo Ortale 1
Affiliation  

Community and role discovery are key tasks in network analysis. The former unveils the organization of a network, whereas the latter highlights the social functions of nodes. The integration of community discovery and role analysis has been investigated, to gain a deeper understanding of topology, i.e., the social functions fulfilled by nodes to pursue community purposes. However, hitherto, node attributes and behavioral role patterns have been ignored in the combination of both tasks. In this manuscript, we study the seamless integration of community discovery and behavioral role analysis, in the domain of networks with node attributes. In particular, we focus on unifying the two tasks, by explicitly harnessing node attributes and behavioral role patterns in a principled manner. To this end, we propose two Bayesian probabilistic generative models of networks, whose novelty consists in the interrelationship of overlapping communities, roles, their behavioral patterns and node attributes. The devised models allow for a variety of exploratory, descriptive and predictive tasks. These are carried out through mean-field variational inference, which is in turn mathematically derived and implemented into a coordinate-ascent algorithm.

A wide spectrum of experiments is designed, to validate the devised models against three classes of state-of-the-art competitors using various real-world benchmark data sets from different social networking services. Our models are found to be more accurate in community detection, link prediction and attribute prediction. Notably, the gain in accuracy is robust to perturbations in the form of noise or lack of observations in either network structure or node attributes. Beside accuracy, scalability is also comparatively investigated. Finally, a qualitative demonstration of the tasks enabled by our models is developed, in which node roles are intuitively explained through an unprecedented visual representation.



中文翻译:

具有节点属性的网络中重叠社区和角色:概率图形建模、贝叶斯公式和变分推理

社区和角色发现是网络分析中的关键任务。前者揭示了网络的组织结构,而后者则突出了节点的社会功能。研究了社区发现和角色分析的集成,以更深入地了解拓扑,即节点为追求社区目的而履行的社会功能。然而,迄今为止,在两个任务的组合中,节点属性和行为角色模式都被忽略了。在这份手稿中,我们研究了在具有节点属性的网络领域中社区发现和行为角色分析的无缝集成。特别是,我们通过以有原则的方式明确利用节点属性和行为角色模式,专注于统一这两个任务。为此,我们提出了两个贝叶斯网络概率生成模型,其新颖性在于重叠社区、角色、它们的行为模式和节点属性的相互关系。设计的模型允许进行各种探索性、描述性和预测性任务。这些是通过平均场变分推理来实现的,而平均场变分推理又是通过数学方法推导出来的,并实施到坐标上升算法中。

设计了广泛的实验,使用来自不同社交网络服务的各种真实世界基准数据集,针对三类最先进的竞争对手验证设计的模型。发现我们的模型在社区检测、链接预测和属性预测方面更准确。值得注意的是,准确性的提高对于噪声形式的扰动或在网络结构或节点属性中缺乏观察是鲁棒的。除了准确性之外,还比较研究了可扩展性。最后,开发了我们模型支持的任务的定性演示,其中通过前所未有的视觉表示直观地解释了节点角色。

更新日期:2021-09-03
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