In the study of network structures, much attention has been devoted to network reconstruction, which relies on partial edge-related information or dynamical processes on the network. However, there are cases where we are only given incomplete nodal data, and the nodal data are measured with different methodologies. In this work, we present an unsupervised learning framework to construct networks from noisy and heterogeneous nodal data. First, we introduce the creating nodes' context sets, which are used to generate random node sequences. Then, a three-layer neural network is adopted to train the node sequences to infer node vectors, enabling us to capture nodes with synergistic roles within the network. Further, the effectiveness of the method is validated through both synthetic data and real data. Finally, we compare the differences between the global thresholding method and the entropy-based method in edge selection. In summary, this work presents a neural network method for node vector learning from heterogeneous nodal data and an entropy-based method for edge selection.
翻译:在网络结构的研究中,对网络重建给予了很大关注,因为网络重建依靠的是部分边缘信息或网络动态过程。然而,在有些情况下,我们只得到不完整的节点数据,节点数据用不同的方法测量。在这项工作中,我们提出了一个未经监督的学习框架,用噪音和杂交节点数据构建网络。首先,我们引入了创建节点的上下文组,用于生成随机节点序列。随后,采用了一个三层神经网络来培训节点序列,以推断节点矢量,使我们能够在网络中捕捉具有协同作用的节点。此外,该方法的有效性通过合成数据和真实数据得到验证。最后,我们比较了边缘选择中全球阈值方法与基于酶的方法之间的差异。总而言之,这项工作为从多种节点数据中学习节点矢提供了一种神经网络方法,并为边缘选择提供了一种基于酶的方法。