In many domains where data are represented as graphs, learning a similarity metric among
graphs is considered a key problem, which can further facilitate various learning tasks, such …

Analytics over big graphs is becoming a first-class challenge in database research, with fast-
growing interest from both the academia and the industrial community. This problem arises …

“Ogni lassada xe persa.” 1--Proverb from Trieste, Italy. We present trièst, a suite of one-pass
streaming algorithms to compute unbiased, low-variance, high-quality approximations of the …

Graph pattern matching, which aims to discover structural patterns in graphs, is considered
one of the most fundamental graph mining problems in many real applications. Despite …

We consider the problem of estimating the number of triangles in a graph. This problem has
been extensively studied in both theory and practice, but all existing algorithms read the …

While there has been a tremendous interest in processing data that has an underlying graph
structure, existing distributed graph processing systems take several minutes or even hours …

How can we estimate local triangle counts accurately in a graph stream without storing the
whole graph? The local triangle counting which counts triangles for each node in a graph is …

Graphlets are induced subgraph patterns and have been frequently applied to characterize
the local topology structures of graphs across various domains, eg, online social networks …

Pattern counting in graphs is fundamental to several network sci-ence tasks, and there is an
abundance of scalable methods for estimating counts of small patterns, often called motifs …

From social science to biology, numerous applications often rely on graphlets for intuitive
and meaningful characterization of networks. While graphlets have witnessed a tremendous …