Network embedding aims to learn a low-dimensional representation vector for each node while preserving the inherent structural properties of the network, which could benefit various downstream mining tasks such as link prediction and node classification. Most existing works can be considered as generative models that approximate the underlying node connectivity distribution in the network, or as discriminate models that predict edge existence under a specific discriminative task. Although several recent works try to unify the two types of models with adversarial learning to improve the performance, they only consider the local pairwise connectivity between nodes. Higher-order structural information such as communities, which essentially reflects the global topology structure of the network, is largely ignored. To this end, we propose a novel framework called CANE to simultaneously learn the node representations and identify the network communities. The two tasks are integrated and mutually reinforce each other under a novel adversarial learning framework. Specifically, with the detected communities, CANE jointly minimizes the pairwise connectivity loss and the community assignment error to improve node representation learning. In turn, the learned node representations provide high-quality features to facilitate community detection. Experimental results on multiple real datasets demonstrate that CANE achieves substantial performance gains over state-of-the-art baselines in various applications including link prediction, node classification, recommendation, network visualization, and community detection.

网络嵌入旨在为每个节点学习一个低维表示向量，同时保留网络的固有结构特性，这可能有益于各种下游挖掘任务，例如链接预测和节点分类。大多数现有工作都可以被视为近似网络中底层节点连接性分布的生成模型，或被视为预测在特定判别任务下边缘存在的辨别模型。尽管最近的一些工作试图通过对抗性学习将两种类型的模型统一起来以提高性能，但他们只考虑了节点之间的局部成对连接。基本上忽略了诸如社区之类的高阶结构信息，这些信息基本上反映了网络的全局拓扑结构。为此，CANE同时学习节点表示并识别网络社区。在新颖的对抗学习框架下，这两项任务是相互融合并相互促进的。具体而言，借助检测到的社区，CANE可以将成对的连接损失和社区分配错误最小化，以改善节点表示学习。反过来，学习到的节点表示形式提供了高质量的功能，以方便社区检测。在多个真实数据集上的实验结果表明，CANE在各种应用（包括链路预测，节点分类，推荐，网络可视化和社区检测）中都比最新基准获得了可观的性能提升。