Listing all maximal cliques in large graphs on vertex-centric model

Brighen, Assia; Slimani, Hachem; Rezgui, Abdelmounaam; Kheddouci, Hamamache

doi:10.1007/s11227-019-02770-4

Cited by 7 publications

(2 citation statements)

References 51 publications

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“…The author also discusses the limitations and challenges of these approaches and proposes some directions for future research. Brighen et al [20] address the problem of listing all maximal cliques in large graphs on a vertex-centric model. They propose a novel approach based on a vertex-centric algorithm, which works by performing a depth-first search traversal of the graph to identify each vertex's neighbors and their neighbors, forming a clique if all vertices are adjacent.…”

Section: Related Workmentioning

confidence: 99%

Distributed Memory Implementation of Bron-Kerbosch Algorithm

Rote,

Krishnamoorthy,

Bhatia

et al. 2024

IEEE Access

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This paper proposes a parallel implementation of the Bron-Kerbosch algorithm, which finds all maximal cliques in large, complex graphs using CPU and thread-level parallelism and distributed memory with multiple cores. With the growing size and complexity of modern graphs, sequential algorithms can become impractical, making parallel computation a promising solution for reducing duration and enhancing scalability. Decomposing the graph into smaller components and distributing the workload across multiple threads using task parallelism is the proposed method. To optimise the algorithm's efficacy across processes, load-balancing techniques are also investigated. The modified Bron-Kerbosch algorithm is implemented and evaluated on a variety of large graphs, exhibiting significant runtime and scalability enhancements over the sequential version. The conclusion of the paper discusses the prospective applications of the parallel Bron-Kerbosch algorithm in a variety of domains, including social network analysis, bio informatics, and network security. Overall, this research contributes to the expanding body of work on parallel computing and graph analysis by emphasising the advantages of CPU and thread-level parallelism for efficiently solving complex computational problems.

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Section: Related Workmentioning

confidence: 99%

Distributed Memory Implementation of Bron-Kerbosch Algorithm

Rote,

Krishnamoorthy,

Bhatia

et al. 2024

IEEE Access

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“…Although reverse-search based algorithms can guarantee that the time cost between two consecutive outputs is bounded by a polynomial function of the input size (i.e., polynomial delay), which implies that the time complexity of these algorithm is proportional to the number of maximal cliques within the input graph (i.e., output-sensitive), their performances on real-world graphs are not as efficient as those of branch-and-bound based algorithms [44]. For the latter, there are also numerous studies that develop parallel algorithms for MCE, in which [64,71] target the data-level parallelization with SIMD instructions, [5,22,36,45,47,91,104,135,171,173] target the task-level parallelization with shared-memory setting and [24,32,69,148] target the tasklevel parallelization with distributed-memory setting. Basically, these parallel algorithms extend the techniques that are originally proposed for sequential MCE algorithms and aim to balance and reduce the overheads in task assignment and memory management to achieve higher degrees of parallelism.…”

Section: Algorithmmentioning

confidence: 99%

Efficient algorithms for subgraph counting and enumeration on large graphs

Wang

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Foremost, I would like to express my deepest gratitude to my supervisor, Prof. Cheng Long. His unparalleled expertise, passionate guidance, and relentless commitment have illuminated the path of my Ph. D journey. His insightful feedback, patient mentorship, and steadfast support have not only expanded my horizon but also cultivated a spirit of curiosity, rigor, and excellence that will resonate within me throughout my future career.To my cherished colleagues, their camaraderie, collaboration, and collective passion for research have transformed challenges into opportunities, fostering an environment of innovation, exploration, and growth. The invaluable insights, constructive discussions, and shared experiences have not only enriched this thesis but also cultivated enduring friendships and memories that I will forever treasure.

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