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.
@article{arxiv.2411.02692,
title = {JPEC: A Novel Graph Neural Network for Competitor Retrieval in Financial Knowledge Graphs},
author = {Wanying Ding and Manoj Cherukumalli and Santosh Chikoti and Vinay K. Chaudhri},
journal= {arXiv preprint arXiv:2411.02692},
year = {2024}
}