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Graph label prediction based on local structure characteristics representation
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DOI：10.1016/j.patcog.2022.108525.png)
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摘要
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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. (c) 2022 Elsevier Ltd. All rights reserved.
Keyword:
Graph classification
Graph neural network
Betweenness centrality node
Feature fusion
Characteristics representation
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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引用论文
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