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oai:figshare.com:article/32805179 2026-10-01T16:17:44Z portal_1172 item_type_8 month_year_10_2026

A heuristic approach to optimise accuracy and performance in the maximum weight clique Qasim Abbas (8775134) PUREID: 665617486 Clique problem combinatorial optimisation graph analytics parallel computing graph algorithms Weight clique Solving real-world problems often leads to computationally challenging problems. Among them, computation on graph-structured data is the most challenging due to its combinatorial complexity. The Maximum Weighted Clique (MWC) is NP-hard problem that remains a challenging graph analytic due to its exponential worst-case time complexity. Approximate MWC algorithms accelerate the execution at the expense of accuracy, however, the rapid growth in size of data demands faster and more accurate solutions. In this thesis, the execution of exact search-based MWC is analysed to show that near-optimal weighted cliques can be discovered in the early stages of the execution if the search space is investigated utilising vertex ordering and heuristic approach. Based on these observations, QClique is introduced as an approximate MWC algorithm that processes the search space as long as unexplored branches and heuristic bounds suggest the potential for discovering larger cliques. A fundamental characteristic of QClique is its tunable parameter that trade-off between accuracy and execution time, making it flexible enough to accommodate wide-range application requirements and computational limitations. Three distinct variations of the MWC problem are examined using the QClique algorithm (QClique-V, QClique-E and QClique-T): The Maximum Vertex-Weighted Clique Problem (MVWCP), the Maximum Edge-Weighted Clique Problem (MEWCP)and the Maximum Total-Weighted Clique Problem (MTWCP). QClique-V applied to the MVWCP achieves an average speedup of 9.8 to 82.3 times compared to both the previous state-of-the-art approximate and exact MVWCP algorithms. It also maintains an accuracy range of 79% to 100%. In the MEWCP, QClique-E achieves an average speedup of 80.7-976.5 times while keeping an accuracy range of 70% to 100% compared to the current exact algorithms. QClique-T was introduced and further optimised in the MTWCP case. When compared to its initial version, the optimised version provides an average speedup of 3.8 times while maintaining100% accuracy. The limitations on the accuracy of QClique are addressed by introducing a technique to accelerate exact MWC algorithms. QClique significantly decreases runtime by providing a near-optimal initial clique, which primes exact MWC algorithms to explore only promising regions. This approach accelerates a state-of-the-art exact MWC algorithm, by 5.2 times, on average. The incorporation of QClique-V into the GABI algorithm reveals new factors in kidney, breast, and ovarian cancers, illustrating its practical importance. It helps to investigate the clinical outcomes that are important to do survival analysis of cancer patients. QClique solves the MVWCP instances that occur in GABI 12 times faster than the existing state-of-the-art exact algorithm while achieving 100% accuracy. 2026-10-01T16:17:44Z Text Thesis 10.17034/32805179.v1 https://figshare.com/articles/thesis/A_heuristic_approach_to_optimise_accuracy_and_performance_in_the_maximum_weight_clique/32805179 All Rights Reserved