  Graphs are a popular data type found in many domains. Numerous techniques
have been proposed to find interesting patterns in graphs to help understand
the data and support decision-making. However, there are generally two
limitations that hinder their practical use: (1) they have multiple parameters
that are hard to set but greatly influence results, (2) and they generally
focus on identifying complex subgraphs while ignoring relationships between
attributes of nodes.Graphs are a popular data type found in many domains.
Numerous techniques have been proposed to find interesting patterns in graphs
to help understand the data and support decision-making. However, there are
generally two limitations that hinder their practical use: (1) they have
multiple parameters that are hard to set but greatly influence results, (2) and
they generally focus on identifying complex subgraphs while ignoring
relationships between attributes of nodes. To address these problems, we
propose a parameter-free algorithm named CSPM (Compressing Star Pattern Miner)
which identifies star-shaped patterns that indicate strong correlations among
attributes via the concept of conditional entropy and the minimum description
length principle. Experiments performed on several benchmark datasets show that
CSPM reveals insightful and interpretable patterns and is efficient in runtime.
Moreover, quantitative evaluations on two real-world applications show that
CSPM has broad applications as it successfully boosts the accuracy of graph
attribute completion models by up to 30.68\% and uncovers important patterns in
telecommunication alarm data.
