In recent years, significant advances have been made in the design and evaluation of
balanced (hyper) graph partitioning algorithms. We survey trends of the past decade in …

Hypergraphs are a generalization of graphs where edges (aka nets) are allowed to connect
more than two vertices. They have a similarly wide range of applications as graphs. This …

We describe the engineering of the distributed-memory multilevel graph partitioner
dKaMinPar. It scales to (at least) 8192 cores while achieving partitioning quality comparable …

State-of-the-art hypergraph partitioners follow the multilevel paradigm that constructs
multiple levels of progressively coarser hypergraphs that are used to drive cut refinement on …

Graphs are ubiquitous in real-world applications, such as computation graphs and social
networks. Partitioning large graphs into smaller, balanced partitions is often essential, with …

Balanced hypergraph partitioning is a classical NP-hard optimization problem with
applications in various domains such as VLSI design, simulating quantum circuits …

We present new refinement heuristics for the balanced graph partitioning problem that break
with an age-old rule. Traditionally, local search only permits moves that keep the block sizes …

Distributed edge caching could address latency and congestion problems in large-scale
data access effectively, improving system throughput and performance. However, the lack of …

Local clustering consists of finding a good cluster around a seed node in a graph. Recently
local motif clustering has been proposed: it is a local clustering approach based on motifs …

A widely-used operation on graphs is local clustering, ie, extracting a well-characterized
community around a seed node without the need to process the whole graph. Recently local …