Online advertising, as a vast market, has gained significant attention in various platforms
ranging from search engines, third-party websites, social media, and mobile apps. The …

Dynamic networks are used in a wide range of fields, including social network analysis,
recommender systems and epidemiology. Representing complex networks as structures …

Representation learning over temporal networks has drawn considerable attention in recent
years. Efforts are mainly focused on modeling structural dependencies and temporal …

Although graph representation learning has been studied extensively in static graph
settings, dynamic graphs are less investigated in this context. This paper proposes a novel …

Dynamic social interaction networks are an important abstraction to model time-stamped
social interactions such as eye contact, speaking and listening between people. These …

With the rapid growth and prevalence of social network applications (Apps) in recent years,
understanding user engagement has become increasingly important, to provide useful …

Predicting groups of people who are jointly deceptive is critical in settings such as sales
pitches and negotiations. Past work on deception in videos focuses on detecting single …

Social platforms have paved the way in creating new, modern ways for users to
communicate with each other. In recent years, multiple platforms have introduced''Stories'' …

Unsupervised representation learning for dynamic graphs has attracted a lot of research
attention in recent years. Compared with static graph, the dynamic graph is a …

The accurate and interpretable prediction of future events in time-series data often requires
the capturing of representative patterns (or referred to as states) underpinning the observed …