Learning graph representations is a fundamental task aimed at capturing various properties
of graphs in vector space. The most recent methods learn such representations for static …

A network is a typical expressive form of representing complex systems in terms of vertices
and links, in which the pattern of interactions amongst components of the network is intricate …

Spam Bots have become a threat to online social networks with their malicious behavior,
posting misinformation messages and influencing online platforms to fulfill their motives. As …

Dealing with relational data always required significant computational resources, domain
expertise and task-dependent feature engineering to incorporate structural information into a …

Predicting cyber threats is crucial for uncovering underlying security risks and proactively
preventing malicious attacks. However, predicting cyber threats and demystifying the …

Recently, graph embedding models significantly improved the quality of graph machine
learning tasks, such as node classification and link prediction. In this work, we propose a …

This study aims to learn representations of stories in narrative works (ie, creative works that
contain stories) using fixed-length vectors. Vector representations of stories enable us to …

Community detection is an important task in social network analysis, allowing us to identify
and understand the communities within the social structures provided by the network …

Methods and systems for dynamic network link prediction include generating a dynamic
graph embedding model for capturing temporal patterns of dynamic graphs, each of the …

This book provides a new perspective on modeling cyber-physical systems (CPS), using a
data-driven approach. The authors cover the use of state-of-the-art machine learning and …