Frequent Itemset-driven Search for Finding Minimum Node Separators in Complex Networks
This addresses network analysis challenges in fields like epidemic control and security, but it is an incremental improvement as it integrates existing data mining concepts into a known optimization framework.
The paper tackles the problem of finding a minimal node separator in complex networks to partition graphs into small components, proposing a frequent itemset-driven search method that outperforms state-of-the-art algorithms by discovering 29 new upper bounds and matching 18 previous best-known bounds on 50 benchmark instances.
Finding an optimal set of critical nodes in a complex network has been a long-standing problem in the fields of both artificial intelligence and operations research. Potential applications include epidemic control, network security, carbon emission monitoring, emergence response, drug design, and vulnerability assessment. In this work, we consider the problem of finding a minimal node separator whose removal separates a graph into multiple different connected components with fewer than a limited number of vertices in each component. To solve it, we propose a frequent itemset-driven search approach, which integrates the concept of frequent itemset mining in data mining into the well-known memetic search framework. Starting from a high-quality population built by the solution construction and population repair procedures, it iteratively employs the frequent itemset recombination operator (to generate promising offspring solution based on itemsets that frequently occur in high-quality solutions), tabu search-based simulated annealing (to find high-quality local optima), population repair procedure (to modify the population), and rank-based population management strategy (to guarantee a healthy population). Extensive evaluations on 50 widely used benchmark instances show that it significantly outperforms state-of-the-art algorithms. In particular, it discovers 29 new upper bounds and matches 18 previous best-known bounds. Finally, experimental analyses are performed to confirm the effectiveness of key algorithmic modules of the proposed method.