论文标题
使用图神经网络现实的合成社交网络
Realistic Synthetic Social Networks with Graph Neural Networks
论文作者
论文摘要
由于隐私和安全问题，社交网络分析在研究人员之间共享数据方面面临严重的困难。对此问题的潜在补救措施是合成网络，非常类似于它们的真实对应物，但可以自由分发。生成合成网络需要创建网络拓扑，这些网络拓扑在应用程序中尽可能地现实地发挥作用。广泛应用的模型目前是基于规则的，并且可能难以再现结构动态。通过图形神经网络（GNN）模型的最新发展，我们评估了GNN对合成社交网络的潜力。我们的GNN使用专门在合理的用例中，包括使用最大平均差异（MMD）的经验评估。我们包括社交网络特定的测量结果，可以评估典型的社交网络分析应用中现实合成网络的行为。 我们发现，封闭式的重复注意网络（GRAN）可以很好地扩展到社交网络，并且与基于基于规则的基于基于规则的生成复发式MATRIX（R-MAT）方法相比，可以更好地复制现实的结构动态。我们发现，Gran在计算上比R-MAT更为昂贵，但使用量的成本并不高，因此对于寻求创建合成社交网络数据集的研究人员来说是有效的。
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.
