Graph classification using high-difference-frequency subgraph embedding.

With the rapid growth of big data analysis, graphs have become an important data structure in relationship extraction and learning. However, the complexity of graph structure increases the difficulty of downstream learning tasks, e.g., graph classification. For example, some traditional graph classification methods rely on the attributes of nodes and edges; the attributes may be incomplete or missing in large-scale graph datasets, e.g., online social networks and knowledge graphs. Focusing on the node-level features overlooks the global characteristics, leading to decreased classification accuracy. This paper proposes a novel graph classification method based on the subgraph-level feature the high-difference-frequency subgraph. We measure the difference in subgraph appearing frequency and use the high difference frequency subgraphs as features in graph learning and classification. The experiments demonstrate that the proposed method outperforms the state-of-the-art graph classification methods. [ABSTRACT FROM AUTHOR]