Hypergraph Partitioning

Problem Overview

A hypergraph is a generalization of a graph where edges, called hyperedges, can connect multiple nodes instead of just two. The balanced hypergraph partitioning problem (HGP) involves assigning the nodes of a hypergraph into a specific number of groups (parts) so that each part contains roughly the same number of nodes, while minimizing the connections (hyperedges) linking nodes across parts. This is important in various real-world applications such as parallel computing (to evenly distribute workloads), circuit design in VLSI (to optimize chip layout), and distributed training for AI models (to minimize communication overhead).

Our Challenge

For our challenge, the parameter $num\_hyperedges$ is the number of hyperedges in the hypergraph, and it is used to control the number of hyperedges in the hypergraph.

A hypergraph is a structure made up of:

• Nodes, each belonging to one or more hyperedges.
• Hyperedges, each linking two or more nodes.

TIG's generation method is such that:

• Nodes and hyperedges all have uniform weight (fixed at 1).
• The number of nodes is around 92% the number of hyperedges.
• Node degrees (number of hyperedges that a node belongs to) follow a power law distribution.
• Hyperedge sizes (number of nodes in a hyperedge) follow a power law distribution.

Objective:

Each node must be assigned to one of 64 parts (i.e. a 64-way partition). The quality of the solution (partition) is measured by the connectivity metric: the sum of the number of parts each hyperedge spans beyond one. The lower the connectivity metric, the better the partition.

Constraints:

1. Each node must be assigned to exactly one part.
2. Every part must contain at least one node.
3. The number of nodes assigned to each part cannot be larger than 1.03x the average part size.

In TIG, the quality of a solution is based on a baseline connectivity metric. This baseline connectivity metric is determined using a greedy bipartition approach. In particular a solution is given a quality score called better_than_baseline defined as better_than_baseline = 1 - connectivity_metric / baseline_connectivity_metric.

For precision, better_than_baseline is stored as an integer where each unit represents 0.1%. For example, a better_than_baseline value of 22 corresponds to 22/1000 = 2.2%.

Example

Consider an example instance with $num\_hyperedges = 16$ and $num\_nodes = 14$:

Edge ID: SIZE: NODES:
      0     2      8, 11
      1    12      0, 1, 2, 4, 5, 7, 8, 9, 10, 11, 12, 13
      2     2      8, 9
      3     8      0, 1, 2, 3, 4, 7, 8, 11
      4     4      8, 9, 10, 11
      5     1      13
      6     4      4, 5, 6, 7
      7     1      12
      8     9      1, 2, 4, 6, 7, 8, 9, 10, 11
      9     2      12, 13
     10     2      12, 13
     11     2      1, 2
     12     4      8, 12, 13
     13    10      0, 1, 2, 3, 4, 7, 8, 9, 10, 11
     14     4      0, 1, 2, 3
     15     3      8, 9, 10

baseline_connectivity_metric = 26

Now consider the following partition:

partition = [1, 3, 3, 0, 2, 0, 0, 2, 3, 2, 1, 2, 1, 1]
# nodes in part 0: [3, 5, 6]
# nodes in part 1: [0, 10, 12, 13] 
# nodes in part 2: [4, 7, 9, 11] 
# nodes in part 3: [1, 2, 8]

Evaluating the connectivity of each hyperedge:

Hyperedge ID:      0   1   2   3   4   5   6   7   8   9   10  11  12  13  14  15
Connectivity - 1:  1   3   1   3   2   0   1   0   3   0    2   0   1   3   2   2

connectivity_metric = 24

# explanation: 
hyperedge 4 contains nodes [8, 9, 10, 11], overlapping with 3 parts (connectivity = 3)
   node 8 is in part 3
   node 9 and 11 are in part 2
   node 10 is in part 1

This partition is ~7.7% better than the baseline:

Applications

Hypergraphs are a powerful tool for representing complex networks in which relationships may involve more than two elements simultaneously. Although the problem is computationally challenging (NP-hard), it has valuable applications across numerous fields:

• Parallel and Distributed Computing: Efficiently distributes computations across processors or GPUs, significantly reducing communication overhead and accelerating computational tasks^{[1][2][3][4][5][6][7]}.
• VLSI & Circuit Design: Optimizes chip layouts and reduces wiring complexity, power consumption, and latency in integrated circuits^[8][9].
• Data Analytics and Network Science: Enhances analysis in social networks, bioinformatics, and general machine learning by capturing intricate relationships, identifying hidden communities, and clustering data into meaningful groups^[10][11][12].
• Other Applications: From optimizing database sharding and segmenting GIS regions to modularizing software systems, hypergraph partitioning transforms large-scale challenges into more tractable problems^[1][7][4].

In the rapidly evolving field of Decentralized Physical Infrastructure Networks (DePIN) — which leverage blockchain technology and distributed nodes to manage physical assets — hypergraph partitioning plays an especially important role. By accurately modeling complex interactions, it can effectively group related tasks and resources across scenarios such as decentralized compute/storage, blockchain data sharding, IoT networks, or supply chain logistics^[16]. This grouping helps minimize cross-node communication and balances workloads, ultimately enhancing the scalability and performance of these decentralized systems^[15].

References

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4. Gottesbüren, L., & Hamann, M. (2022). Deterministic Parallel Hypergraph Partitioning. In Euro-Par 2022: Parallel Processing (pp. 301–316). Springer International Publishing.
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7. Catalyurek, U., Devine, K., Fonseca Faraj, M., Gottesbüren, L., Heuer, T., Meyerhenke, H., Sanders, P., Schlag, S., Schulz, C., & Seemaier, D. (2022). More Recent Advances in (Hyper)Graph Partitioning.
8. Papa, D., & Markov, I. (2007). Hypergraph Partitioning and Clustering. In Handbook of Approximation Algorithms and Metaheuristics.
9. Karypis, G., Aggarwal, R., Kumar, V., & Shekhar, S. (1999). Multilevel hypergraph partitioning: applications in VLSI domain. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 7(1), 69–79.
10. Zhang, C., Cheng, W., Li, F., & Wang, X. (2024). Hypergraph-Based Influence Maximization in Online Social Networks. Mathematics, 12(17), 2769.
11. Wang, S., Cui, H., Qu, Y., & Yijia, Z. (2025). Multi-source biological knowledge-guided hypergraph spatiotemporal subnetwork embedding for protein complex identification. Briefings in Bioinformatics, 26.
12. Zhou, D., Huang, J., & Schölkopf, B. (2006). Learning with Hypergraphs: Clustering, Classification, and Embedding. In Advances in Neural Information Processing Systems 19 (2006), 1601–1608.
13. Chodrow, P.S., Veldt, N., & Benson, A.R. (2021). Generative hypergraph clustering: From blockmodels to modularity. Science Advances, 7.
14. Kolodziej, S., Mahmoudi Aznaveh, M., Bullock, M., David, J., Davis, T., Henderson, M., Hu, Y., & Sandstrom, R. (2019). The SuiteSparse Matrix Collection Website Interface. Journal of Open Source Software, 4, 1244.
15. K. Kumar et al. "SWORD: workload-aware data placement and replica selection for cloud data management systems". In: The VLDB Journal 23 (Dec. 2014), pp. 845–870. doi: 10.1007/s00778-014-0362-1.
16. Qu C, Tao M, Yuan R. A Hypergraph-Based Blockchain Model and Application in Internet of Things-Enabled Smart Homes. Sensors (Basel). 2018 Aug 24;18(9):2784. doi: 10.3390/s18092784. PMID: 30149523; PMCID: PMC6164253.