Anomaly detection, aka outlier detection or novelty detection, has been a lasting yet active
research area in various research communities for several decades. There are still some …

Modeling sequential interactions between users and items/products is crucial in domains
such as e-commerce, social networking, and education. Representation learning presents …

False information can be created and spread easily through the web and social media
platforms, resulting in widespread real-world impact. Characterizing how false information …

Users organize themselves into communities on web platforms. These communities can
interact with one another, often leading to conflicts and toxic interactions. However, little is …

Users' polarization and confirmation bias play a key role in misinformation spreading on
online social media. Our aim is to use this information to determine in advance potential …

Many online applications, such as online social networks or knowledge bases, are often
attacked by malicious users who commit different types of actions such as vandalism on …

In online discussion communities, users can interact and share information and opinions on
a wide variety of topics. However, some users may create multiple identities, or sockpuppets …

Several network embedding models have been developed for unsigned networks. However,
these models based on skip-gram cannot be applied to signed networks because they can …

Anti-social behaviors in social media can happen both at user and community levels. While
a great deal of attention is on the individual as an 'aggressor,'the banning of entire Reddit …

Though the sheer volume of data that is collected is immense, it is the rare categories that
are often the most important in many high-impact domains, ranging from financial fraud …