{"type":"ScholarlyArticle","title":"Dynamic Seed-GrowthCM: A Dynamic Benefit-Oriented Algorithm for Core Maximization on Large Graphs","authors":[{"name":"Dongyuan Ma","slug":"dongyuan-ma","given":"Dongyuan","family":"Ma","affiliation":""},{"name":"Dongxiao He","slug":"dongxiao-he","given":"Dongxiao","family":"He","affiliation":""},{"name":"Xin Huang","slug":"xin-huang","given":"Xin","family":"Huang","affiliation":""}],"abstract":"The k-core has garnered significant attention in recent research as an effective measure of node importance within a graph. A k-core is defined as the maximal induced subgraph where each node has a degree of at least k. This paper addresses the core maximization problem: given a graph G, an integer k, and a budget b, the objective is to insert b new distinct edges into G to maximize the size of its k-core. This problem is theoretically proven to be NP-hard and APX-hard. However, the existing heuristic methods often struggle to achieve a good balance between efficiency and answer quality. In this paper, we propose a novel dynamic approach that, for the first time, uncovers the dynamic changes in node degrees. We introduce a new concept using the contribution of edges across different λ-shell components to the final solution. Based on these findings, we present the Dynamic Seed-GrowthCM method. This method selects the λ-shell component with the largest estimated benefit as the initial seed. In each iteration, depending on complete/partial growth, either a new seed is incorporated into the solution, or an existing seed undergoes growth, becoming a larger seed by adding connected components of the λ-shell component to the solution. Experimental results on ten datasets demonstrate that our algorithm significantly outperforms state-of-the-art methods in terms of solution quality on large graphs, while achieving a high computational efficiency.","doi":"10.24963/ijcai.2025/352","url":"https://ijcai.org/proceedings/2025/352","pdf":"https://ijcai.org/proceedings/2025/0352.pdf","markdown":"https://ijcai.org/proceedings/2025/352.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/352","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Data Mining","track":"Main Track","number":352,"pages":{"first":3162,"last":3170},"keywords":["Data Mining: DM: Networks","Data Mining: DM: Big data and scalability","Data Mining: DM: Mining graphs"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}