Recently, with the rapid developments of the Internet and social networks, there have been
tremendous increase in the amount of complex-structured text resources. These information …

Fair machine learning aims to mitigate the biases of model predictions against certain
subpopulations regarding sensitive attributes such as race and gender. Among the many …

To subvert recent advances in perimeter and host security, the attacker community has
developed and employed various attack vectors to make a malware much stealthier than …

Graphs have a superior ability to represent relational data, such as chemical compounds,
proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as …

Link prediction plays an important role in network analysis and applications. Recently,
approaches for link prediction have evolved from traditional similarity-based algorithms into …

Real-world networks exhibit prominent hierarchical and modular structures, with various
subgraphs as building blocks. Most existing studies simply consider distinct subgraphs as …

Weighted single large graphs are often used to simulate complex systems, and thus mining
frequent subgraphs in a weighted large graph is an important issue that has attracted the …

Graph is used to represent various complex relationships among objects and data entities.
One of the emerging and important problems is graph classification that has tremendous …

A hypergraph is a complex data structure capable of expressing associations among any
number of data entities. Overcoming the limitations of traditional graphs, hypergraphs are …

The ever-increasing importance of structured data in different applications, especially in the
biomedical field, has driven the need for reducing its complexity through projections into a …