CompanyKG: A Large-Scale Heterogeneous Graph for Company Similarity Quantification

Lele Cao, Vilhelm von Ehrenheim, Mark Granroth-Wilding, Richard Stahl, Andrew McCornack, Dhiana Deva

Keywords: knowledge graph, company similarity quantification, heterogeneous graph, benchmarks, self-supervised learning, graph neural network, text embedding, similarity prediction, similarity ranking, competitor retrieval, investment, private equity

TL;DR: A large company relation graph (1.17M companies, 51.06M edges, 15 relations) for similarity analysis. Evaluated 11 methods across 3 tasks (competitor analysis, market mapping, M&A) with open access to data, code, and docs under a permissive license.

Abstract: In the investment industry, it is often essential to carry out fine-grained company similarity quantification for a range of purposes, including market mapping, competitor analysis, and mergers and acquisitions. We propose and publish a knowledge graph, named CompanyKG, to represent and learn diverse company features and relations. Specifically, 1.17 million companies are represented as nodes enriched with company description embeddings; and 15 different inter-company relations result in 51.06 million weighted edges. To enable a comprehensive assessment of methods for company similarity quantification, we have devised and compiled three evaluation tasks with annotated test sets: similarity prediction, competitor retrieval and similarity ranking. We present extensive benchmarking results for 11 reproducible predictive methods categorized into three groups: node-only, edge-only, and node+edge. To the best of our knowledge, CompanyKG is the first large-scale heterogeneous graph dataset originating from a real-world investment platform, tailored for quantifying inter-company similarity.

Primary Area: datasets and benchmarks

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Submission Number: 7157