2024/07/10 by Wanying Ding, Manoj Cherukumalli, Santosh Chikoti +1 · 1 voice
Computer Science · #Advanced Graph Neural Networks #Data Mining Algorithms and Applications #Topic Modeling #cs.AI #cs.CE #cs.IR
paper · pdf · doi:10.1145/3626772.3657677
openalex publication_date 2024/07/10 · arxiv published 2024/11/05 · arxiv updated 2024/11/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
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(<u>J</u>PMorgan <u>P</u>roximity <u>E</u>mbedding for <u>C</u>ompetitor 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.