Background In recent years, advancements in machine learning have revolutionized various domains, but concerns about bias and unfairness in automated decision-making systems have gained significant attention. One of the causes of unfairness in AI systems is the reliance on training data. Aleatoric uncertainty refers to the inherent randomness or variability...

In normal differentiated cells, most of the genome is densely methylated, except CpG islands near promoters of actively transcribed genes. Maintaining this boundary between unmethylated promoter-associated and adjacent methylated regions is crucial as loss of segregation can lead to disease. Unfortunately, it is not clear where these boundaries are,...

­Node classification in real-world attributed networks is a central analytical task that is a growing research area. In real-world networks, a large portion of node classes only contain limited labeled instances. Many prevailing graph machine learning methods typically rely upon the availability of sufficient labeled data. However, the long-tail...

Background Attributed networks provide high-impact data representation in many real-world complex systems, including social network analysis, financial fraud detection, and drug discovery. In these networks, users can be represented as nodes, which are classified based on the information available from the network. However, there are currently limited...

­Background Classification of social media content is often regarded as a text categorization problem and mainly focuses on content features, such as words and hashtags. However, for many emerging applications like fake news and rumor detection, it is very challenging, if not impossible, to identify useful features based on message content. A key...

­Background In a variety of real-world applications (e.g., social spam detection, financial fraud detection, and network intrusion detection), detecting anomalies from networked data plays a vital role in keeping malicious behaviors or attacks at bay. With the increasing usage of attributed networks for modeling information systems, anomaly detection...

Background Cyberbullying, defined as “aggressively intentional acts carried out by a group or an individual using electronic forms of contact, repeatedly or over time against victims who cannot easily defend themselves,” has been rising at an alarming rate. Previous research has found that nearly 43% of teens in the United States have been...

Background Social media has become an indispensable tool in the face of natural disasters due to its broad appeal and ability to quickly disseminate information. For instance, Twitter is an important source for disaster responders to search for (1) topics that have been identified as being of particular interest over time, i.e., common topics such as...

Background Social media users generate a tremendous amount of data in forms such as profile information, network connections, and online posts. Online vendors use this data to understand user preferences and further predict their future needs. However, because user-generated data is rich in content, the data can be used by malicious attackers to infer...

BackgroundTextual information is one of the most significant portions of data that users generate by participating in different online activities such as leaving online reviews and posting tweets. On one hand, textual data consists of abundant information about users’ behavior, preferences, and needs which is critical for understanding them. For...