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

TL;DR

This paper introduces JPEC, a new graph neural network model designed to improve competitor retrieval in financial knowledge graphs by effectively handling complex graph attributes and relationships.

Contribution

The paper presents JPEC, a novel GNN model tailored for financial knowledge graphs, addressing unique graph attributes and outperforming existing models in competitor retrieval tasks.

Findings

JPEC outperforms most existing models in experiments.

Effective learning from first- and second-order node proximity.

Addresses challenges of directed, undirected, and attributed graph data.

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.