JPEC: A Novel Graph Neural Network for Competitor Retrieval in Financial Knowledge Graphs
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916468363362304 |
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| author | Ding, Wanying Cherukumalli, Manoj Chikoti, Santosh Chaudhri, Vinay K. |
| author_facet | Ding, Wanying Cherukumalli, Manoj Chikoti, Santosh Chaudhri, Vinay K. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_02692 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | JPEC: A Novel Graph Neural Network for Competitor Retrieval in Financial Knowledge Graphs Ding, Wanying Cherukumalli, Manoj Chikoti, Santosh Chaudhri, Vinay K. Information Retrieval Artificial Intelligence Computational Engineering, Finance, and Science 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. |
| title | JPEC: A Novel Graph Neural Network for Competitor Retrieval in Financial Knowledge Graphs |
| topic | Information Retrieval Artificial Intelligence Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2411.02692 |