Embedding Complex Networks

<p>Graph embedding is a transformation of nodes of a graph into a set of vectors. A good embedding should capture the graph topology, node-to-node relationship, and other relevant information about the graph. The main challenge at hand is to ensure that embeddings describe the properties of the graph well. As a result, selecting the best embedding is a challenging task and very often requires domain experts.</p> <p>In this thesis, we implement a series of extensive experiments with selected graph embedding algorithms, both on real-world and artificial networks. We conclude from these experiments that <strong>Node2Vec </strong>is the general best choice of algorithm, but that there is no single winner in all tests. Therefore, our main recommendation for practitioners is, if possible, to generate several embeddings for a problem at hand and use a general framework that provides a tool for an unsupervised graph embedding comparison.</p>