Representation Learning Using RandomWalks for Dynamic Graphs
An important part of many machine learning workows on graphs is vertex representation learning, i.e. learning a low dimensional vector representation for each vertex in the graph. Recently, several powerful techniques for unsupervised representation learning have been demonstrated to give the state of the art performance in downstream tasks such as vertex classication and edge prediction. ese techniques rely on random walks performed on the graph in order to capture its structural properties. ese structural properties are then encoded in the vector representation space. However, most contemporary representation learning methods only apply to static graphs while real world graphs are oen dynamic and change over time. Static representation learning methods are not able to update the vector representations when the graph changes; therefore, they must re generate the vector representations on an updated static snapshot of the graph regardless of the extent of the change in the graph. In this work, we propose computationally ecient algorithms for vertex representation learning that extend random walk based methods to dynamic graphs. e computation complexity of our algorithms depends upon the extent and rate of changes the number of edges changed per update and on the density of the graph. We empirically evaluate our algorithms on real world datasets for downstream machine learning tasks of multi class and multi label vertex classication. e results show that our algorithms can achieve competitive results to the state of the art methods while being computationally ecient.