Community detection is a fundamental problem in social network analysis consisting,
roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social …

Graphs are data structures that effectively represent relational data in the real world. Graph
representation learning is a significant task since it could facilitate various downstream …

Abstract In 2014, the National Institutes of Health (NIH) initiated the Illuminating the
Druggable Genome (IDG) program to identify and improve our understanding of poorly …

Most current single-cell analysis pipelines are limited to cell embeddings and rely heavily on
clustering, while lacking the ability to explicitly model interactions between different feature …

The vast majority of digital currency transactions rely on a blockchain framework to ensure
quick and accurate execution. As such, understanding how a blockchain works is vital to …

Although there is considerable disagreement about the details, community detection in
social networks requires finding groups of nodes that are similar to one another, and …

Sentiment analysis of the text deals with the mining of the opinions of people from their
written communication. With the increasing usage of online social media platforms for user …

Network embedding aims at learning the low dimensional representation of nodes. These
representations can be widely used for network mining tasks, such as link prediction …

In addition to vaccines, the World Health Organization sees novel medications as an urgent
matter to fight the ongoing COVID-19 pandemic. One possible strategy is to identify target …

With the rapid development in artificial intelligence and mobile networks, the past decade
has witnessed the flourish of social media, and information diffusion popularity prediction in …