5 papers
SIGMA: A Versatile Streaming Graph Partitioner for Vertex- and Edge-Balanced Distributed GNN Training
Barbara Hoffmann, Shai Dorian Peretz, Adil Chhabra +3
Distributed Graph Neural Network (GNN) training depends critically on how the underlying graph is partitioned across compute resources. Existing graph partitioners focus either on…
Advances in Exact and Approximate Group Closeness Centrality Maximization
Christian Schulz, Jakob Ternes, Henning Woydt
In the NP-hard \textsc{Group Closeness Centrality Maximization} problem, the input is a graph and a positive integer , and the task is to find a set …
BuffCut: Prioritized Buffered Streaming Graph Partitioning
Linus Baumgärtner, Adil Chhabra, Marcelo Fonseca Faraj +1
Streaming graph partitioners enable resource-efficient and massively scalable partitioning, but one-pass assignment heuristics are highly sensitive to stream order and often yield…
Near-Optimal Minimum Cuts in Hypergraphs at Scale
Adil Chhabra, Christian Schulz, Bora Uçar +1
The hypergraph minimum cut problem aims to partition its vertices into two blocks while minimizing the total weight of the cut hyperedges. This fundamental problem arises in networ…
CluStRE: Streaming Graph Clustering with Multi-Stage Refinement
Adil Chhabra, Shai Dorian Peretz, Christian Schulz
We present CluStRE, a novel streaming graph clustering algorithm that balances computational efficiency with high-quality clustering using multi-stage refinement. Unlike traditiona…