{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T00:40:35Z","timestamp":1702600835486},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684703","type":"print"},{"value":"9781643684710","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T00:00:00Z","timestamp":1702339200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,12,12]]},"abstract":"<jats:p>Meta-path-based methods for measuring the similarities between nodes in Heterogeneous Information Networks (HINs) have attracted attention from researchers due to excellent performance. However, these methods suffer from some issues: (1) it is difficult for users to provide effective meta-paths of complex HINs; (2) it is inefficient to enumerate all instances of meta-paths. In order to solve the above issues, this paper proposes a novel method, K-order Neighbors based Heterogeneous Graph Neural Network (KN-HGNN), for measuring node similarity. Firstly, KN-HGNN generates meta-paths based on network schema. Then, KN-HGNN obtains the features of nodes by aggregating itself type feature and its k-order neighbors\u2019 type features under the constraints of meta-paths. Finally, KN-HGNN calculates the similarities between nodes based on the features of nodes. The experimental results on real datasets show that KN-HGNN outperforms the baselines.<\/jats:p>","DOI":"10.3233\/faia231016","type":"book-chapter","created":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T15:05:06Z","timestamp":1702566306000},"source":"Crossref","is-referenced-by-count":0,"title":["A Method for Measuring Node Similarity in Heterogeneous Information Networks"],"prefix":"10.3233","author":[{"given":"Liudi","family":"Zhan","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Yunnan University, Kunming, China"}]},{"given":"Hongmei","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Yunnan University, Kunming, China"},{"name":"Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University, Kunming, China"}]},{"given":"Lihua","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Yunnan University, Kunming, China"},{"name":"Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University, Kunming, China"}]},{"given":"Qing","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Yunnan University, Kunming, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining IX"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA231016","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T15:05:07Z","timestamp":1702566307000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA231016"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,12]]},"ISBN":["9781643684703","9781643684710"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia231016","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,12]]}}}