9 papers
Agentic Algorithm Engineering: Improving Shared-Memory Exact Minimum Cuts
David A. Bader, Adil Chhabra, Ernestine Großmann +3
The minimum cut problem for an undirected edge-weighted graph asks us to divide its set of nodes into two blocks while minimizing the weighted sum of the cut edges. Over the last y…
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…
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…
Partitioning Trillion Edge Graphs on Edge Devices
Adil Chhabra, Florian Kurpicz, Christian Schulz +2
Processing large-scale graphs, containing billions of entities, is critical across fields like bioinformatics, high-performance computing, navigation and route planning, among othe…
Buffered Streaming Edge Partitioning
Adil Chhabra, Marcelo Fonseca Faraj, Christian Schulz +1
Addressing the challenges of processing massive graphs, which are prevalent in diverse fields such as social, biological, and technical networks, we introduce HeiStreamE and Freigh…
Faster Local Motif Clustering via Maximum Flows
Adil Chhabra, Marcelo Fonseca Faraj, Christian Schulz
Local clustering aims to identify a cluster within a given graph that includes a designated seed node or a significant portion of a group of seed nodes. This cluster should be well…
Local Motif Clustering via (Hyper)Graph Partitioning
Adil Chhabra, Marcelo Fonseca Faraj, Christian Schulz
A widely-used operation on graphs is local clustering, i.e., extracting a well-characterized community around a seed node without the need to process the whole graph. Recently loca…