Over the last decade, there has been considerable interest in designing algorithms for
processing massive graphs in the data stream model. The original motivation was two-fold …

This paper presents a new space-efficient algorithm for counting and sampling triangles--
and more generally, constant-sized cliques--in a massive graph whose edges arrive as a …

“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 …

Triangle counting in networks under LDP (Local Differential Privacy) is a fundamental task
for analyzing connection patterns or calculating a clustering coefficient while strongly …

We present space-efficient data stream algorithms for approximating the number of triangles
in a graph up to a factor 1+ ε. While it can be shown that determining whether a graph is …

Subgraph counting is fundamental for analyzing connection patterns or clustering
tendencies in graph data. Recent studies have applied LDP (Local Differential Privacy) to …

We study the subgraph counting problem in data streams. We provide the first non-trivial
estimator for approximately counting the number of occurrences of an arbitrary subgraph H …

Continuous subgraph matching (CSM) algorithms find the occurrences of a given pattern on
a stream of data graphs online. A number of incremental CSM algorithms have been …

We revisit the much-studied problem of space-efficiently estimating the number of triangles
in a graph stream, and extensions of this problem to counting fixed-sized cliques and cycles …

We consider the problem of counting all $ k $-vertex subgraphs in an input graph, for any
constant $ k $. This problem (denoted sub-cnt $ _k $) has been studied extensively in both …