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 …

Randomized experiments, or “A/B” tests, remain the gold standard for evaluating the causal
effect of a policy intervention or product change. However, experimental settings, such as …

We present MaNIACS, a sampling-based randomized algorithm for computing high-quality
approximations of the collection of the subgraph patterns that are frequent in a single, large …

Bipartite graphs characterize relationships between two different sets of entities, like actor-
movie, user-item, and author-paper. The butterfly, a 4-vertices 4-edges (2, 2)-biclique, is the …

In recent years, there has been a surge of interest in applying machine learning to graphs
and networks that already exist in the world around us. The approach has been successfully …

Symmetry plays a major role in subgraph matching both in the description of the graphs in
question and in how it confounds the search process. This work addresses how to quantify …

Graph embedding approaches have been attracting increasing attention in recent years
mainly due to their universal applicability. They convert network data into a vector space in …

In this article, we introduce a generalization of graphlets to heterogeneous networks called
typed graphlets. Informally, typed graphlets are small typed induced subgraphs. Typed …

Triangle counting is a fundamental technique in network analysis, that has received much
attention in various input models. The vast majority of triangle counting algorithms are …

Many real-world applications give rise to large heterogeneous networks where nodes and
edges can be of any arbitrary type (eg, user, web page, location). Special cases of such …