The algorithms community has been modeling the underlying key features and constraints of
massively parallel frameworks and using these models to discover new algorithmic …

Graph clustering is a fundamental task in many data-mining and machine-learning
pipelines. In particular, identifying a good hierarchical structure is at the same time a …

Many modern parallel systems, such as MapReduce, Hadoop and Spark, can be modeled
well by the MPC model. The MPC model captures well coarse-grained computation on large …

In this paper, we present new randomized algorithms that improve the complexity of the
classic (Δ+ 1)-coloring problem, and its generalization (Δ+ 1)-list-coloring, in three well …

The edit distance between two strings is defined as the smallest number of insertions,
deletions, and substitutions that need to be made to transform one of the strings to another …

Identifying the connected components of a graph, apart from being a fundamental problem
with countless applications, is a key primitive for many other algorithms. In this paper, we …

We design a generic method to reduce the task of finding weighted matchings to that of
finding short augmenting paths in unweighted graphs. This method enables us to provide …

We introduce a method for “sparsifying” distributed algorithms and exhibit how it leads to
improvements that go past known barriers in two algorithmic settings of large-scale graph …

We present the first conditional hardness results for massively parallel algorithms for some
central graph problems including (approximating) maximum matching, vertex cover …

Some recent papers showed that many sequential iterative algorithms can be directly
parallelized, by identifying the dependences between the input objects. This approach …