Graph sparsification aims to reduce the number of edges of a network while maintaining its accuracy for given tasks. In this study, we propose a novel method called GSGAN, which is able to sparsify networks for community detection tasks. GSGAN is able to capture those relationships that are not shown in the original graph but are relatively important, and creating artificial edges to reflect these relationships and further increase the effectiveness of the community detection task. We adopt GAN as the learning model and guide the generator to produce random walks that are able to capture the structure of a network. Specifically, during the training phase, in addition to judging the authenticity of the random walk, discriminator also considers the relationship between nodes at the same time. We design a reward function to guide the generator creating random walks that contain useful hidden relation information. These random walks are then combined to form a new social network that is efficient and effective for community detection. Experiments with real-world networks demonstrate that the proposed GSGAN is much more effective than the baselines, and GSGAN can be applied and helpful to various clustering algorithms of community detection.

中文翻译：

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生成对抗网络的图稀疏化

图稀疏化旨在减少网络的边数，同时保持其对给定任务的准确性。在这项研究中，我们提出了一种称为 GSGAN 的新方法，它能够为社区检测任务稀疏网络。GSGAN 能够捕获原始图中未显示但相对重要的关系，并创建人工边来反映这些关系并进一步提高社区检测任务的有效性。我们采用 GAN 作为学习模型并引导生成器产生能够捕捉网络结构的随机游走。具体来说，在训练阶段，判别器除了判断随机游走的真实性外，同时还要考虑节点之间的关系。我们设计了一个奖励函数来指导生成器创建包含有用隐藏关系信息的随机游走。然后将这些随机游走组合起来形成一个新的社交网络，该网络对社区检测非常有效。真实世界网络的实验表明，所提出的 GSGAN 比基线更有效，并且 GSGAN 可以应用于社区检测的各种聚类算法并对其有所帮助。