DeepWalk: Online Learning of Social Representations
Abstract
tent representations of vertices in a network. These latent
representations encode social relations in a continuous vector
space, which is easily exploited by statistical models. DeepWalk
generalizes recent advancements in language modeling
and unsupervised feature learning (or deep learning)
from sequences of words to graphs.
DeepWalk uses local information obtained from truncated
random walks to learn latent representations by treating
walks as the equivalent of sentences. We demonstrate
DeepWalk’s latent representations on several multi-label
network classification tasks for social networks such as BlogCatalog,
Flickr, and YouTube. Our results show that DeepWalk
outperforms challenging baselines which are allowed
a global view of the network, especially in the presence of
missing information. DeepWalk’s representations can provide
F1 scores up to 10% higher than competing methods
when labeled data is sparse. In some experiments, DeepWalk’s
representations are able to outperform all baseline
methods while using 60% less training data.
DeepWalk is also scalable. It is an online learning algorithm
which builds useful incremental results, and is trivially
parallelizable. These qualities make it suitable for a broad
class of real world applications such as network classification,
and anomaly detection.