Browsing by Subject "Graph Embedding"

Node classification is an important problem on networks in many different contexts. Optimizing the graph embedding has great potential to help improve the classification accuracy. The purpose of this thesis is to explore how graph embeddings can be exploited in the node classification task in the context of citation networks. More specifically, this thesis looks into the impact of different kinds of embeddings on the node classification, comparing their performance. Using three different similarity functions and different dimensions for the embedding vector ranging from 1 to 800, we examined the impact of graph embeddings on accuracy in node classification using three benchmark datasets: Cora, Citeseer, and PubMed. Our experimental results indicate that there are some common tendencies in the way dimensionality impacts the graph embedding quality regardless of the graph. We also established that some network-specific hyperparameter tuning clearly affects classification accuracy.