Exploiting node metadata to predict interactions in bipartite networks using graph embedding and neural networks

Abstract

Networks are increasingly used in various fields to represent systems with the aim of understanding the underlying rules governing observed interactions, and hence predict how the system is likely to behave in the future. Recent developments in network science highlight that accounting for node metadata improves both our understanding of how nodes interact with one another, and the accuracy of link prediction. However, to predict interactions in a network within existing statistical and machine learning frameworks, we need to learn objects that rapidly grow in dimension with the number of nodes. Thus, the task becomes computationally and conceptually challenging for networks. Here, we present a new predictive procedure combining a statistical, low-rank graph embedding method with machine learning techniques which reduces substantially the complexity of the learning task and allows us to efficiently predict interactions from node metadata in bipartite networks. To illustrate its application on real-world data, we apply it to a large dataset of tourist visits across a country. We found that our procedure accurately reconstructs existing interactions and predicts new interactions in the network. Overall, both from a network science and data science perspective, our work offers a flexible and generalizable procedure for link prediction.

Design and Development of a Rural Tourism Marketing System Using Deep Learning

“…( 1): In a tourism attraction recommendation system, attractions evolve rapidly, making building a comprehensive knowledge graph of attractions challenging. Thus, this work introduces the use of a KGE [44] technology-based interest propagation framework, the RippleNet [45,46]. This framework incorporates the knowledge graph of attractions into the recommendation model.…”