JPEC: A Novel Graph Neural Network for Competitor Retrieval in Financial Knowledge Graphs

• URL: http://arxiv.org/abs/2411.02692v1
• Date: Tue, 05 Nov 2024 00:39:22 GMT
• Title: JPEC: A Novel Graph Neural Network for Competitor Retrieval in Financial Knowledge Graphs
• Authors: Wanying Ding, Manoj Cherukumalli, Santosh Chikoti, Vinay K. Chaudhri,
• Abstract summary: This study explores the application of graph embedding in identifying competitors from a financial knowledge graph. 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.
• Score: 2.756147934836574
• License:
• Abstract: 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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