Social and information networking activities such as on Facebook, Twitter, WeChat, and
Weibo have become an indispensable part of our everyday life, where we can easily access …

Recommender systems are vital ingredients for many e-commerce services. In the literature,
two of the most popular approaches are based on factorization and graph-based models; …

Graph embedding methods represent nodes in a continuous vector space, preserving
different types of relational information from the graph. There are many hyper-parameters to …

User experience in modern content discovery applications critically depends on high-quality
personalized recommendations. However, building systems that provide such …

Entity Linking aims to link entity mentions in texts to knowledge bases, and neural models
have achieved recent success in this task. However, most existing methods rely on local …

We propose new privacy attacks to infer attributes (eg, locations, occupations, and interests)
of online social network users. Our attacks leverage seemingly innocent user information …

Abstract Named Entity Disambiguation is the task of assigning entities from a Knowledge
Graph (KG) to mentions of such entities in a textual document. The state-of-the-art for this …

Given a stream of edges from a time-evolving (un) weighted (un) directed graph, we
consider the problem of detecting anomalous edges in near real-time using sublinear …

Analyzing time series from the perspective of complex network has interested many
scientists. In this paper, based on visibility graph theory a novel method of constructing …

Network embedding aims to learn a low-dimensional vector representation for each node in
the social and information networks, with the constraint to preserve network structures. Most …