A Multisource Data Fusion-based Heterogeneous Graph Attention Network for Competitor Prediction

Author:

Ye Xiaoqing1ORCID,Sun Yang2ORCID,Liu Dun2ORCID,Li Tianrui3ORCID

Affiliation:

1. School of Computing and Artificial lntelligence, and Key Laboratory of Service Science and Innovation of Sichuan Province, Southwest Jiaotong University, China

2. School of Economics and Management, Southwest Jiaotong University, China

3. School of Computing and Artificial lntelligence, Southwest Jiaotong University, China

Abstract

Competitor identification is an essential component of corporate strategy. With the rapid development of artificial intelligence, various data-mining methodologies and frameworks have emerged to identify competitors. In general, the competitiveness among companies is determined by both market commonality and resource similarity. However, because resource information is more difficult to obtain than market information, existing studies primarily identify competitors via market commonality. To address this limitation, we introduce multisource company descriptions as well as heterogeneous business relationships, and we propose a novel method for simultaneously mining the market commonality and resource similarity. First, we use multisource company descriptions to represent companies and transform the heterogeneous business relationships into a heterogeneous business network. Then, we propose a novel multisource data fusion-based heterogeneous graph attention network (MHGAT) to learn the pairwise competitive relationships between companies. Specifically, a graph neural network-based model is proposed to learn the embeddings of companies by preserving their competition, and a multilevel attention framework is designed to integrate the embeddings from neighboring company level, heterogeneous relationship level, and multisource description level. Finally, experiments on a real-world dataset verify the effectiveness of our proposed MHGAT and demonstrate the usefulness of company descriptions and business relationships in competitor identification.

Funder

National Science Foundation of China

Sichuan Science and Technology Program

China Postdoctoral Science Foundation

Sichuan Key Laboratory Project of Service Science and Innovation

Fundamental Research Funds for the Central Universities

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

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