Graph label prediction based on local structure characteristics representation. (May 2022) - Ding, Jingyi
Highlights: Centrality regions are more representative than random partial substructures. Divide the selected paths into similar set and dissimilar set to increases model generalization. Design a new index to measure the similarity between two paths. Vote for multi-layer MLP prediction results to increase accuracy. Abstract: A recent study has shown that the real-time anti-noise challenges faced by molecular activity prediction algorithms can be solved by using the part structure features of the molecular graph. However, the sub-structures selected by this method are distributed in a scattered manner such that although they include as many block features as possible, they do not fully consider the connections between these blocks. Therefore, this study was conducted to fully consider the physical interpretation of the betweenness centrality node in the graph, and a sub-structure was obtained by depth-first search (DFS) from this node. This sub-structure not only contains the characteristics of each region but also retains the connections between each region. Then, a cascading multi-layer perception (MLP) model was designed to learn the characteristic representation of the graph from its local structure features. Experiments demonstrated that the performance of our algorithm is superior to that of other algorithms when evaluated on different datasets.