Network representation learning endeavors to learn low-dimensional dense representations for nodes in a network. With the rapid development of online social platforms, the analysis of social networks has become increasingly significant. Although network representation learning can facilitate the social network analysis, most existing algorithms merely exploit the explicit structure among nodes to obtain the node representations. Besides, traditional network representation learning techniques ignore the influence of nodes in a network when generating the representations of nodes. Motivated by this, we innovatively propose an influence-aware graph neural network (IAGNN) framework, which can learn the latent feature representations of nodes by incorporating both node influence and global structure information into the embedding process for encoding graph-structured data. The generated low-dimensional dense representations of the nodes in a network can be used for subsequent tasks such as user classification and user behavior prediction. Specifically, we assign different weights to each node according to different types of topology between their neighbors, and integrate with the basic influence of each node to generate an intermediate matrix with influence information. The intermediate matrix is encoded into low-dimensional and dense vector spaces by leveraging the attention mechanism and the graph convolution operation. Extensive experiments are conducted on five datasets, and IAGNN achieves an average accuracy of 3% higher than the comparison algorithms on the node classification and link prediction tasks. The experimental results demonstrate that our model can significantly outperform the state-of-the-art network embedding methods such as GCN, GAT, GraphSage, AGNN on node classification and link prediction tasks.

网络表示学习努力学习网络中节点的低维密集表示。随着在线社交平台的快速发展，对社交网络的分析变得越来越重要。尽管网络表示学习可以促进社交网络分析，但是大多数现有算法仅利用节点之间的显式结构来获取节点表示。此外，传统的网络表示学习技术在生成节点表示时会忽略网络中节点的影响。因此，我们创新地提出了一种影响感知图神经网络（IAGNN）框架，通过将节点影响力和全局结构信息合并到用于对图形结构数据进行编码的嵌入过程中，可以学习节点的潜在特征表示。网络中节点的生成的低维密集表示可以用于后续任务，例如用户分类和用户行为预测。具体来说，我们根据邻居之间不同的拓扑类型为每个节点分配不同的权重，并与每个节点的基本影响力集成，以生成一个具有影响信息的中间矩阵。通过利用注意力机制和图卷积运算，将中间矩阵编码为低维和密集向量空间。在五个数据集上进行了广泛的实验，IAGNN的平均准确度比节点分类和链接预测任务上的比较算法高3％。实验结果表明，在节点分类和链接预测任务上，我们的模型可以显着优于最新的网络嵌入方法，例如GCN，GAT，GraphSage，AGNN。