提出JPEC模型，精准从金融知识图谱中挖掘竞争对手。
JPEC: A Novel Graph Neural Network for Competitor Retrieval in Financial Knowledge Graphs
- 融合一阶与二阶邻近性，结合节点属性学习图嵌入。
- 在真实金融图谱上超越多数现有模型，提升竞争关系识别准确率。
- 适合金融分析、企业关系挖掘等场景使用。
知识图谱因其高效组织和分析复杂数据的能力而受到关注。结合图嵌入技术（如图神经网络，GNN），知识图谱成为提供有价值洞察的强大工具。本研究探索了图嵌入在金融知识图谱中识别竞争对手的应用。现有先进模型因知识图谱的独特属性面临挑战，包括有向与无向关系、带属性的节点以及极少标注的竞争连接。为解决这些问题，我们提出一种新型图嵌入模型JPEC（JPMorgan Proximity Embedding for Competitor Detection），利用图神经网络同时学习一阶与二阶节点邻近性，并融合关键特征用于竞争关系检索。在大量实验中，JPEC显著优于多数现有模型，证明其在竞争关系识别中的有效性。
原文摘要 · Abstract (English)
Knowledge graphs have gained popularity for their ability to organize and analyze complex data effectively. When combined with graph embedding techniques, such as graph neural networks (GNNs), knowledge graphs become a potent tool in providing valuable insights. This study explores the application of graph embedding in identifying competitors from a financial knowledge graph. Existing state-of-the-art(SOTA) models face challenges due to the unique attributes of our knowledge graph, including directed and undirected relationships, attributed nodes, and minimal annotated competitor connections. To address these challenges, we propose a novel graph embedding model, JPEC(JPMorgan Proximity Embedding for Competitor Detection), which utilizes graph neural network to learn from both first-order and second-order node proximity together with vital features for competitor retrieval. JPEC had outperformed most existing models in extensive experiments, showcasing its effectiveness in competitor retrieval.
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