Computing subgraph frequencies is a fundamental task that lies at the core of several
network analysis methodologies, such as network motifs and graphlet-based metrics, which …

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 …

Temporal networks serve as abstractions of many real-world dynamic systems. These
networks typically evolve according to certain laws, such as the law of triadic closure, which …

Local graph clustering methods aim to find a cluster of nodes by exploring a small region of
the graph. These methods are attractive because they enable targeted clustering around a …

Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …

Counting the frequency of small subgraphs is a fundamental technique in network analysis
across various domains, most notably in bioinformatics and social networks. The special …

Motivated by recent studies in the data mining community which require to efficiently list all k-
cliques, we revisit the iconic algorithm of Chiba and Nishizeki and develop the most efficient …

Graph mining workloads aim to extract structural properties of a graph by exploring its
subgraph structures. General purpose graph mining systems provide a generic runtime to …

Subgraph isomorphism counting is an important problem on graphs, as many graph-based
tasks exploit recurring subgraph patterns. Classical methods usually boil down to a …

There is growing interest in graph pattern mining (GPM) problems such as motif counting.
GPM systems have been developed to provide unified interfaces for programming …