{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T00:46:33Z","timestamp":1774053993027,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:p>It has been found that many real networks, such as power grids and the Internet, are non-robust, i.e., attacking a small set of nodes would cause the paralysis of the entire network. Thus, the Network Enhancement Problem~(NEP), i.e., improving the robustness of a given network by modifying its structure, has attracted increasing attention. Heuristics have been proposed to address NEP. However, a hand-engineered heuristic often has significant performance limitations. A recently proposed model solving NEP by reinforcement learning has shown superior performance than heuristics on in-distribution datasets. However, their model shows considerably inferior out-of-distribution generalization ability when enhancing networks against the degree-based targeted attack. In this paper, we propose a more effective model with stronger generalization ability by incorporating domain knowledge including measurements of local network structures and decision criteria of heuristics. We further design a hierarchical attention model to utilize the network structure directly, where the query range changes from local to global. Finally, we propose neural confined local search~(NCLS) to realize the effective search of a large neighborhood, which exploits a learned model to confine the neighborhood to avoid exhaustive enumeration. We conduct extensive experiments on synthetic and real networks to verify the ability of our models.<\/jats:p>","DOI":"10.24963\/ijcai.2023\/236","type":"proceedings-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:31:30Z","timestamp":1691742690000},"page":"2122-2132","source":"Crossref","is-referenced-by-count":3,"title":["Enhancing Network by Reinforcement Learning and Neural Confined Local Search"],"prefix":"10.24963","author":[{"given":"Qifu","family":"Hu","sequence":"first","affiliation":[{"name":"Inspur Electronic Information Industry Co., Ltd"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruyang","family":"Li","sequence":"additional","affiliation":[{"name":"Inspur Electronic Information Industry Co., Ltd"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Deng","sequence":"additional","affiliation":[{"name":"Inspur Electronic Information Industry Co., Ltd"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaqian","family":"Zhao","sequence":"additional","affiliation":[{"name":"Inspur Electronic Information Industry Co., Ltd"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rengang","family":"Li","sequence":"additional","affiliation":[{"name":"Inspur Electronic Information Industry Co., Ltd"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}","theme":"Artificial Intelligence","location":"Macau, SAR China","acronym":"IJCAI-2023","number":"32","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2023,8,19]]},"end":{"date-parts":[[2023,8,25]]}},"container-title":["Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:42:26Z","timestamp":1691743346000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2023\/236"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2023\/236","relation":{},"subject":[],"published":{"date-parts":[[2023,8]]}}}