| Download | - View accepted manuscript: Competitive analysis with graph embedding on patent networks (PDF, 886 KiB)
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| DOI | Resolve DOI: https://doi.org/10.1109/CBI49978.2020.00009 |
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| Author | Search for: Wang, Yunli1; Search for: Richard, Rene1; Search for: Mcdonald, Daniel1 |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Conference | 2020 IEEE 22nd Conference on Business Informatics (CBI), June 22-24, 2020, Antwerp, Belgium |
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| Subject | competitive analysis; representation learning; topic modeling |
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| Abstract | Advanced competitive analysis is increasingly becoming important in business analytics. A key component of strategic research is to collect and review information from multiple unstructured sources to identify major competitors and their technology development trends. Topic modeling techniques such as Latent Dirichlet Allocation (LDA) have been applied to competitive analysis, which mainly use the semantic similarities between documents to infer competitive relationships. In this study, we propose using graph embedding methods to learn the implicit competitive relationships between firms. Using patent networks with patents and organizations as nodes, we learn the embeddings of nodes which are then used to cluster organizations into groups. Organizations within the same groups are considered competitors. We applied three graph embedding methods: node2vec, metapath2vec, and GraphSAGE to learn node embeddings. Two of these methods use the structural information in patent networks: node2vec for homogeneous networks and metapath2vec for heterogeneous networks. While, GraphSAGE uses both the structure and content information in the patent network. The results are compared with a baseline author-topic modeling method. The graph embedding methods outperform the author-topic modeling approach in learning the competitive relationships. A case study, examining the evolution of competitors over multiple years, shows the graph embedding method learns meaningful node embeddings. |
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| Publication date | 2020-07-15 |
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| Publisher | IEEE |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 9d9f6e4f-884a-44c9-be45-227954ad80b1 |
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| Record created | 2020-11-12 |
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| Record modified | 2021-02-08 |
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