Prediction of Collaborative Relationships by Using Network Representation Learning

In recent years, network representation learning (NRL) has been increasingly applied into web data analysis, such as video, image and text. Most of NRL methods can widely pursue nodes classification, community detection and link prediction tasks. Due to the nodes in these kinds of networks mostly contain the common attributes and share the same neighbors, we identify them as homogeneous networks, also including real-world networks such as social network, citation network and collaborative network. Therefore, it is nature that nodes tends to connect densely with high similarity. Supply network is one of the most typical collaborative network. It is not a homogeneous network, due to the nodes present two roles - supplier and customer. As the importance of supplier-customer relationships has become increasingly apparent that guides modern research and practice, the main impact of such researches is poured into the field of business management and operation research. However, prior studies have indicated that firms tended to manage their relationships in a more structural and relational approach, no existing literature have applied NRL into predicting business relationships. This paper proposes a novel NRL method and presents two contributions. First, we employ network analysis to extract three centralities, which can represent both local and global context. Second, we also include firm profiles as node contents to train the model by using machine learning techniques. To compare with other state-of-the-art NRL methods, our proposal assesses the concepts surrounding structural and relational characteristics and the extension of firm profiles to supply network, and also represents the infrastructure of the social science of business.