论文标题
优惠：网络表示学习的主题维度框架
OFFER: A Motif Dimensional Framework for Network Representation Learning
论文作者
论文摘要
为了更好地代表多元关系，本文研究了一个用于高阶图学习的主题维度框架。可以通过报价提高图表学习效率。提出的框架主要旨在加速和改善高阶图学习结果。我们从网络图案的维度应用加速程序。具体而言，在两个阶段进行了节点和边缘的精制度：（1）使用节点的基序来完善网络的邻接矩阵； （2）采用基序边缘来完善学习过程中的过渡概率矩阵。为了评估所提出的框架的效率，修改和检查了四种流行的网络表示算法。通过评估要约的性能，链接预测结果和聚类结果都表明，图表表示学习算法增强了，始终优于效率更高的原始算法。
Aiming at better representing multivariate relationships, this paper investigates a motif dimensional framework for higher-order graph learning. The graph learning effectiveness can be improved through OFFER. The proposed framework mainly aims at accelerating and improving higher-order graph learning results. We apply the acceleration procedure from the dimensional of network motifs. Specifically, the refined degree for nodes and edges are conducted in two stages: (1) employ motif degree of nodes to refine the adjacency matrix of the network; and (2) employ motif degree of edges to refine the transition probability matrix in the learning process. In order to assess the efficiency of the proposed framework, four popular network representation algorithms are modified and examined. By evaluating the performance of OFFER, both link prediction results and clustering results demonstrate that the graph representation learning algorithms enhanced with OFFER consistently outperform the original algorithms with higher efficiency.
