BalNode2Vec: Balanced Random Walk based Versatile Feature Learning for Networks

Research on social networks and understanding the interactions of the users can be modeled as a task of graph mining, such as predicting nodes and edges in networks. The challenges of building the graph representation include engineering features used by learning algorithms. Recent research in representation learning can automate the prediction by learning the features themselves. Many types of research have performed graph sampling using random walks or it's derivatives. However, the random walk sometimes can not represent the features of the graph accurately enough. In this research, we propose BalNode2Vec - a new sampling algorithm for learning feature representations for nodes in networks by using balanced random walks. We define a notion of a nodes network neighborhood and design a balanced random walk procedure, which adapts to the graph topology. We show that through exploring the graph through a balanced random walk can generate richer representations. Efficacy of BalNode2vec over existing state-of-the-art techniques on link prediction is demonstrated by using several real-world networks from different domains.