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Network modelling and variational Bayesian inference for structure analysis of signed networks

机译：网络建模和变分贝叶斯推理，用于签名网络的结构分析

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摘要

Currently, structure analysis of signed networks with positive and negative links has received wide attention and is becoming a research focus in the area of network science. In recent years, many community detection methods for signed networks have been proposed to analyze the structure of signed networks. However, current methods can only efficiently analyze the signed networks with the single community structure and unable to analyze the signed networks with the coexisting structure of communities and peripheral nodes, bipartite, or other structures. To address this problem, in this study, we present a mathematically principled method for the structure analysis of signed networks with positive and negative links, in which a probabilistic model firstly is proposed to model the signed networks with the single community or the coexisting structure, and a variational Bayesian approach is deduced to learn the approximate distribution of model parameters. For determining the optimal model, we also deduce a model selection criterion based on the evidence theory. In addition, to efficiently analyze the large signed networks, we propose a fast learning version of our algorithm with the time complexityO(k2E) wherekis the number of groups andEis the number of links. In our experiments, the proposed method is validated in the synthetic and real-world signed networks, and is compared with the state-of-the-art methods. The experimental results demonstrate that the proposed method can more efficiently and accurately analyze to the structure of signed networks than the state-of-the-art methods.
机译：目前，具有正负链接的签名网络的结构分析已受到广泛关注，并成为网络科学领域的研究重点。近年来，提出了许多用于签名网络的社区检测方法来分析签名网络的结构。但是，当前的方法只能有效地分析具有单一社区结构的签名网络，而无法分析具有社区和外围节点，二分之一或其他结构的共存结构的签名网络。为了解决这个问题，在这项研究中，我们提出了一种数学原理的正负链接网络结构分析方法，其中首先提出了一个概率模型来建模具有单个社区或共存结构的签名网络，推导了变分贝叶斯方法以学习模型参数的近似分布。为了确定最佳模型，我们还基于证据理论推导了模型选择准则。另外，为了有效地分析大型签名网络，我们提出了一种算法的快速学习版本，其时间复杂度为O（k2E），其中组数为E，链路数为E。在我们的实验中，该方法在合成和真实世界的签名网络中得到了验证，并与最新方法进行了比较。实验结果表明，与最新方法相比，该方法可以更有效，更准确地分析签名网络的结构。

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