More Recent Advances in (Hyper)Graph Partitioning

Çatalyürek, Ümit V.; Devine, Karen Dragon; Faraj, Marcelo Fonseca; Gottesbüren, Lars; Heuer, Tobias; Meyerhenke, Henning; Sanders, Peter; Schlag, Sebastian; Schulz, Christian; Seemaier, Daniel; Wagner, Dorothea

doi:10.1145/3571808

Cited by 25 publications

(6 citation statements)

References 127 publications

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“…Such graph descriptors allow us to understand how species properties impact their interactions and to highlight scale-independent properties . Graph partitioning might find applications for hierarchical networks due to its favorable scaling . Uncertainties related to CRN may be minimized further after combining FNN (modeling observable response) with Wiener polynomial expansion-based surrogate model, propagating kinetic parameters uncertainties to observables …”

Section: Ann For Nanoscale Mixturesmentioning

confidence: 99%

“…251 Graph partitioning might find applications for hierarchical networks due to its favorable scaling. 252 Uncertainties related to CRN may be minimized further after combining FNN (modeling observable response) with Wiener polynomial expansion-based surrogate model, propagating kinetic parameters uncertainties to observables. 253 Beside the above-mentioned expert systems, recent advancements have led to the development of data-driven kinetic models using deep neural networks (DNNs) to automatically develop kinetic models from experimental data.…”

Section: H C C H C C C C Cos( )( )mentioning

confidence: 99%

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Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

Nicolle,

Deng,

Ihme

et al. 2024

J. Chem. Inf. Model.

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Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures, providing an expressive view of the chemical space and multiscale processes. Their hybridization with physical knowledge can bridge the gap between predictivity and understanding of the underlying processes. This overview explores recent progress in ANNs, particularly their potential in the ’recomposition’ of chemical mixtures. Graph-based representations reveal patterns among mixture components, and deep learning models excel in capturing complexity and symmetries when compared to traditional Quantitative Structure–Property Relationship models. Key components, such as Hamiltonian networks and convolution operations, play a central role in representing multiscale mixtures. The integration of ANNs with Chemical Reaction Networks and Physics-Informed Neural Networks for inverse chemical kinetic problems is also examined. The combination of sensors with ANNs shows promise in optical and biomimetic applications. A common ground is identified in the context of statistical physics, where ANN-based methods iteratively adapt their models by blending their initial states with training data. The concept of mixture recomposition unveils a reciprocal inspiration between ANNs and reactive mixtures, highlighting learning behaviors influenced by the training environment.

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Section: Ann For Nanoscale Mixturesmentioning

confidence: 99%

Section: H C C H C C C C Cos( )( )mentioning

confidence: 99%

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

Nicolle,

Deng,

Ihme

et al. 2024

J. Chem. Inf. Model.

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“…The more general case of partitioning graphs is a well-studied problem. 21,22 For arbitrary graphs and reasonably balanced parts, the corresponding decision problem is  -complete. 23 The same holds true for partitioning DAGs into acyclic parts, for which Herrmann et al 15 recently proposed multilevel heuristics.…”

Section: Related Workmentioning

confidence: 99%

“…Our heuristics split the input trees into smaller subtrees. The more general case of partitioning graphs is a well‐studied problem 21,22 . For arbitrary graphs and reasonably balanced parts, the corresponding decision problem is

𝒩 𝒫

‐complete 23 .…”

Section: Related Workmentioning

confidence: 99%

Mapping tree‐shaped workflows on systems with different memory sizes and processor speeds

Kulagina

Meyerhenke

Benoît

2023

Concurrency and Computation

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Directed acyclic graphs are commonly used to model scientific workflows, by expressing dependencies between tasks, as well as the resource requirements of the workflow. As a special case, rooted directed trees occur in several applications, for instance in sparse matrix computations. Since typical workflows are modeled by large trees, it is crucial to schedule them efficiently, so that their execution time (or makespan) is minimized. Furthermore, it is usually beneficial to distribute the execution on several compute nodes, hence increasing the available memory, and allowing us to parallelize parts of the execution. To exploit the heterogeneity of modern clusters in this context, we investigate the partitioning and mapping of tree‐shaped workflows on two types of target architecture models: in AM1, each processor can have a different memory size, and in AM2, each processor can also have a different speed (in addition to a different memory size). We design a three‐step heuristic for AM1, which adapts and extends previous work for homogeneous clusters [Gou C, Benoit A, Marchal L. Partitioning tree‐shaped task graphs for distributed platforms with limited memory. IEEE Trans Parallel Dist Syst 2020; 31(7): 1533–1544]. The changes we propose concern the assignment to processors (accounting for the different memory sizes) and the availability of suitable processors when splitting or merging subtrees. For AM2, we extend the heuristic for AM1 with a two‐phase local search approach. Phase A is a swap‐based hill climber, while (the optional) Phase B is inspired by iterated local search. We evaluate our heuristics for AM1 and AM2 with extensive simulations, and we demonstrate that exploiting the heterogeneity in the cluster significantly reduces the makespan, compared to the state of the art for homogeneous processors.

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“…Graph partitioning refers to the process of dividing a graph into smaller, interconnected subsets known as partitions or communities. This division aims to reveal the underlying structure of the graph by grouping nodes that share similar characteristics or functions [1,2]. In this study, we address the critical necessity for efficient graph partitioning [3,4] and community detection algorithms [5] to unravel the structural intricacies of large-scale networks spanning diverse domains such as social sciences [6], biology [7], and computer science [8].…”

Section: -Introductionmentioning

confidence: 99%

Efficient Graph Partitioning using LouvainSplit Algorithm

Javadi,

Aghighi,

Azadbakht

2024

Preprint

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Graph partitioning is essential for uncovering cohesive communities within complex networks. This paper introduces LouvainSplit, an innovative algorithm designed to enhance graph partitioning efficiency and accuracy. LouvainSplit leverages advanced techniques in feature representation, community detection, and evaluation, providing a robust framework for addressing challenges inherent in graph partitioning tasks across diverse domains. At its core, LouvainSplit utilizes a feature pyramid representation approach to extract both basic and summary features from input graphs at multiple granularity levels. This methodology ensures a subtle evaluation of graph data by capturing fundamental graph information alongside intricate structural patterns, thus offering a comprehensive representation of underlying community structures. A key innovation of our approach is the integration of the Louvain algorithm, renowned for its efficacy in community detection. By leveraging pairwise cosine similarities computed from node feature vectors, the Louvain algorithm optimizes modularity iteratively, effectively partitioning graphs into cohesive communities. This iterative process facilitates the identification of significant network structures within complex networks, providing valuable insights into their organizational dynamics. To comprehensively evaluate LouvainSplit's performance, we integrated diverse graph partitioning algorithms, including PyMetis, Genetic Algorithm (GA), DBSCAN, KMeans, OPTICS, and spectral clustering to evaluate the effectiveness of LouvainSplit and using popular evaluation metrics across diverse benchmarks spanning various domains and graphs. The results demonstrate LouvainSplit's superiority in terms of recall score, modularity, and scalability compared to existing methods. Moreover, LouvainSplit maintains competitive average runtime values, ensuring efficient processing even with large-scale datasets.

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More Recent Advances in (Hyper)Graph Partitioning

Cited by 25 publications

References 127 publications

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

Mapping tree‐shaped workflows on systems with different memory sizes and processor speeds

Efficient Graph Partitioning using LouvainSplit Algorithm

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