Social network analysis faces profound difficulties in sharing data between researchers due to privacy and security concerns. A potential remedy to this issue are synthetic networks, that closely resemble their real counterparts, but can be freely distributed. generating synthetic networks requires the creation of network topologies that, in application, function as realistically as possible. Widely applied models are currently rule-based and can struggle to reproduce structural dynamics. Lead by recent developments in Graph Neural Network (GNN) models for network generation we evaluate the potential of GNNs for synthetic social networks. Our GNN use is specifically within a reasonable use-case and includes empirical evaluation using Maximum Mean Discrepancy (MMD). We include social network specific measurements which allow evaluation of how realistically synthetic networks behave in typical social network analysis applications. We find that the Gated Recurrent Attention Network (GRAN) extends well to social networks, and in comparison to a benchmark popular rule-based generation Recursive-MATrix (R-MAT) method, is better able to replicate realistic structural dynamics. We find that GRAN is more computationally costly than R-MAT, but is not excessively costly to employ, so would be effective for researchers seeking to create datasets of synthetic social networks.
翻译:由于隐私和安全方面的考虑,在研究人员之间分享数据方面,社会网络分析面临巨大的困难。这个问题的潜在补救办法是合成网络,这种网络与实际对应者非常相似,但可以自由分布。产生合成网络需要建立网络型态,在应用中尽可能切合实际地发挥作用。广泛应用的模式目前以规则为基础,并可以努力复制结构动态。在图形神经网络(GNN)网络(GNN)网络的网络生成模型的最新开发中,我们评估GNNs在合成社会网络中的潜力。我们的GNN(GNN)的使用具体属于合理的使用案例,包括使用最大平均值差异(MMD)的经验性评估。我们包括社会网络的具体测量方法,以便评估在典型的社会网络分析应用中,合成网络的运行情况。我们认为Gated 经常注意网络(GAN) 能够很好地扩展到社会网络,并且与基于规则的生成基准的生成Recursive-MATrix(R-MAT)方法相比,我们更能够复制现实的结构动态。我们发现GAN(GRAN)在计算上比R-MAT(MAT)更昂贵,但不会过于昂贵,因此对研究人员来说是有效的合成网络进行合成研究。