Latent Network Features and Overlapping Community Discovery via Boolean Intersection Representations.
Latent Network Features and Overlapping Community Discovery via Boolean Intersection Representations.
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通过布尔交集表示的潜在网络特征和重叠社区发现。
DOI：
10.1109/tnet.2017.2728638
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Milenkovic,Olgica
中科院分区：
文献类型：
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作者：
Dau,Hoang;Milenkovic,Olgica
我们提出了一个新的潜在布尔特征模型的复杂网络，捕捉不同类型的节点的相互作用和网络社区。该模型基于图论中的一个新概念，称为图的布尔交集表示，它概括了交集表示的概念。我们主要集中在一种形式的布尔交集，称为cointersection，并描述如何使用这种表示推导节点的功能集和他们的社区。我们推导出几个一般的界限上的最小数量的功能，用于共交表示和讨论图形的家庭，确切的共交表征是可能的。我们的研究结果还包括算法找到最佳和近似的共交表示的图。
We propose a new latent Boolean feature model for complex networks that capture 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.