Network representation learning (NRL) advances the conventional graph mining of social
networks, knowledge graphs, and complex biomedical and physics information networks …

Social network analysis is an important problem in data mining. A fundamental step for
analyzing social networks is to encode network data into low-dimensional representations …

Graph-structured data arise naturally in many different application domains. By representing
data as graphs, we can capture entities (ie, nodes) as well as their relationships (ie, edges) …

Graphs can be seen as a universal language to describe and model a diverse set of
complex systems and data structures. However, efficiently extracting topological information …

Network representation learning, also called network embedding, aiming to learn low
dimensional vectors for nodes while preserving essential properties of the network, benefits …

Recently, Network Embedding (NE) has become one of the most attractive research topics in
machine learning and data mining. NE approaches have achieved promising performance …

Abstract Representation learning in graphs has proven useful for many predictive tasks. In
this paper we assess the feasibility of representation learning in a credit card fraud setting …

Understanding the complex process of information spread in online social networks (OSNs)
enables the efficient maximization/minimization of the spread of useful/harmful information …

Attributed network embedding (ANE) attempts to represent a network in short code, while
retaining information about node topological structures and node attributes. A node's feature …

Network embedding, which maps graphs to distributed representations, is a unified
framework for various graph inference tasks. According to the topology properties (eg …