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Latent Network Features and Overlapping Community Discovery via Boolean Intersection Representations.
IEEE/ACM Transactions on Networking ( IF 3.7 ) Pub Date : 2018-11-27 , DOI: 10.1109/tnet.2017.2728638
Hoang Dau 1 , Olgica Milenkovic 1
Affiliation  

We propose a new latent Boolean feature model for complex networks that captures different types of node interactions and network communities. The model is based on a new concept in graph theory, termed the Boolean intersection representation of a graph, which generalizes the notion of an intersection representation. We mostly focus on one form of Boolean intersection, termed cointersection, and describe how to use this representation to deduce node feature sets and their communities. We derive several general bounds on the minimum number of features used in cointersection representations and discuss graph families for which exact cointersection characterizations are possible. Our results also include algorithms for finding optimal and approximate cointersection representations of a graph.

中文翻译:

潜在网络功能和通过布尔交集表示法重叠社区发现。

我们为复杂网络提出了一个新的潜在布尔特征模型,该模型捕获了不同类型的节点交互和网络社区。该模型基于图论中的一个新概念,称为图的布尔交集表示,该概念概括了交集表示的概念。我们主要关注布尔交集的一种形式,称为共交集,并描述如何使用这种表示来推导节点特征集及其社区。我们推导了在相交表示中使用的最少特征数的几个一​​般界限,并讨论了可能进行精确的相交表征的图族。我们的结果还包括用于找到图形的最佳和近似共相交表示的算法。
更新日期:2019-11-01
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