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Abstract
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Graph embedding is an advantageous technique for reducing computational costs and effectively using graph information in machine learning tasks like classification, clustering, and link prediction. As a result, it has become a key method in various research areas. However, different embedding methods may be used depending on the variety of graphs available. One of the most commonly used graph types is the heterogeneous graph (HG) or heterogeneous information network (HIN), which presents unique challenges for graph embedding approaches due to its diverse set of nodes and edges. Several methods have been proposed for heterogeneous graph embedding in recent years to overcome these challenges. This paper aims to review the latest techniques used for this purpose, divided into two main parts: the first part describes the fundamental concepts and obstacles in heterogeneous graph embedding, while the second part compares the most critical methods. Finally, the results are summarized, outlining the challenges and opportunities for future directions.
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