符号网络中的平衡团计算：概念与算法
Balanced Clique Computation in Signed Networks: Concepts and Algorithms
摘要：
团是网络分析中凝聚子图挖掘最基本的模型之一。现有的团模型主要针对无符号网络。然而，在现实世界中，许多应用被建模为具有正负边的带符号网络。由于有符号网络具有与无符号网络不同的性质，现有的团模型不适用于有符号网络。受此启发，我们提出了平衡集团模型，认为最基本和最占主导地位的理论，结构平衡理论，签署网络。在平衡团模型的基础上，研究了计算给定符号网络中所有最大平衡团的最大平衡团枚举问题（MBCE）和计算最大规模平衡团的最大平衡团搜索问题（MBCS）。我们证明了MBCE问题和MBCS问题都是NP-难的。对于MBCE问题，一个简单的解决方案是将有符号网络视为两个无符号网络，并利用现成的无符号网络技术。然而，这样的解决方案对于大型签名网络是低效的。为了解决这个问题，在本文中，我们首先提出了一个新的最大平衡团枚举算法，利用独特的性质签署网络。在新算法的基础上，我们设计了两种优化策略，以进一步提高枚举的效率。针对MBCS问题，提出了一种基于搜索空间划分的搜索框架。为了进一步提高新框架的效率，我们提出了多个优化策略的冗余搜索分支和无效的候选人。我们在大型真实数据集上进行了广泛的实验。实验结果表明，我们提出的算法的效率，有效性和可扩展性的MBCE问题和MBCS问题。
英文摘要：
Clique is one of the most fundamental models for cohesive subgraph mining in network analysis. Existing clique model mainly focuses on unsigned networks. However, in real world, many applications are modeled as signed networks with positive and negative edges. As the signed networks hold their own properties different from the unsigned networks, the existing clique model is inapplicable for the signed networks. Motivated by this, we propose the balanced clique model that considers the most fundamental and dominant theory, structural balance theory, for signed networks. Following the balanced clique model, we study the maximal balanced clique enumeration problem (MBCE) which computes all the maximal balanced cliques in a given signed network and the maximum balanced clique search problem (MBCS) which computes the balanced clique with maximum size. We show that MBCE problem and MBCS problem are both NP-Hard. For the MBCE problem, a straightforward solution is to treat the signed network as two unsigned networks and leverage the off-the-shelf techniques for unsigned networks. However, such a solution is inefficient for large signed networks. To address this problem, in this paper, we first propose a new maximal balanced clique enumeration algorithm by exploiting the unique properties of signed networks. Based on the new proposed algorithm, we devise two optimization strategies to further improve the efficiency of the enumeration. For the MBCS problem, we propose a new search framework based on search space partition. To further improve the efficiency of the new framework, we propose multiple optimization strategies regarding to redundant search branches and invalid candidates. We conduct extensive experiments on large real datasets. The experimental results demonstrate the efficiency, effectiveness and scalability of our proposed algorithms for MBCE problem and MBCS problem.
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