Network embedding methods map a network's nodes to vectors in an embedding space, in
such a way that these representations are useful for estimating some notion of similarity or …

With advances in technology, large amounts of streaming data can be generated
continuously by sensors in applications like environment surveillance. Due to the inherited …

Finding an efficient approach to incrementally update and maintain frequent itemsets is an
important aspect of data mining. Earlier incremental algorithms focused on reducing the …

Mining patterns from multi-relational data is a problem attracting increasing interest within
the data mining community. Traditional data mining approaches are typically developed for …

This paper suggests a framework for mining subjectively interesting pattern sets that is
based on two components:(1) the encoding of prior information in a model for the data …

Mining patterns from multi-relational data is a problem attracting increasing interest within
the data mining community. Traditional data mining approaches are typically developed for …

Popular data mining methods support knowledge discovery from patterns that hold in binary
relations. We study the generalization of association rule mining within arbitrary n-ary …

Node embedding methods map network nodes to low dimensional vectors that can be
subsequently used in a variety of downstream prediction tasks. The popularity of these …

Network representation learning methods map network nodes to vectors in an embedding
space that can preserve specific properties and enable traditional downstream prediction …