We present DeepWalk, a novel approach for learning latent representations of vertices in a
network. These latent representations encode social relations in a continuous vector space …

We present WALKLETS, a novel approach for learning multiscale representations of vertices
in a network. In contrast to previous works, these representations explicitly encode multi …

Abstract Deep Random Walk (DeepWalk) can learn a latent space representation for
describing the topological structure of a network. However, for relational network …

DeepWalk is a typical representation learning method that learns low-dimensional
representations for vertices in social networks. Similar to other network representation …

In this paper, we present {GraRep}, a novel model for learning vertex representations of
weighted graphs. This model learns low dimensional vectors to represent vertices appearing …

Social networks are heterogeneous systems composed of different types of nodes (eg users,
content, groups, etc.) and relations (eg social or similarity relations). While learning and …

Recently, the Network Representation Learning (NRL) techniques, which represent graph
structure via low-dimension vectors to support social-oriented application, have attracted …

Online social networks are now a popular way for users to connect, express themselves, and
share content. Users in today's online social networks often post a profile, consisting of …

Social media such as blogs, Facebook, Flickr, etc., presents data in a network format rather
than classical IID distribution. To address the interdependency among data instances …

While node semantics have been extensively explored in social networks, little research
attention has been paid to pro le edge semantics, ie, social relations. Ideal edge semantics …