Networks represent relationships between entities in many complex systems, spanning from
online social interactions to biological cell development and brain connectivity. In many …

The privacy of individuals does not only depend on their own actions and data but may also
be affected by the privacy decisions and by the data shared by other individuals. This …

Recommender system is an important component of many web services to help users locate
items that match their interests. Several studies showed that recommender systems are …

Graph data, such as chemical networks and social networks, may be deemed
confidential/private because the data owner often spends lots of resources collecting the …

Real world complex networks are indirect representation of complex systems. They grow
over time. These networks are fragmented and raucous in practice. An important concern …

Existing embedding methods for attributed networks aim at learning low-dimensional vector
representations for nodes only but not for both nodes and attributes, resulting in the fact that …

In recent years, recommender systems play a pivotal role in helping users identify the most
suitable items that satisfy personal preferences. As user-item interactions can be naturally …

Users in various web and mobile applications are vulnerable to attribute inference attacks, in
which an attacker leverages a machine learning classifier to infer a target user's private …

Differential privacy provides strong privacy preservation guarantee in information sharing.
As social network analysis has been enjoying many applications, it opens a new arena for …

Graph-based classification methods are widely used for security analytics. Roughly
speaking, graph-based classification methods include collective classification and graph …