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Knowl. Discov. Data"],"published-print":{"date-parts":[[2020,4,30]]},"abstract":"<jats:p>\n            Community detection (or graph clustering) is crucial for unraveling the structural properties of complex networks. As an important technique in community detection, label propagation has shown the advantage of finding a good community structure with nearly linear time complexity. However, despite the progress that has been made, there are still several important issues that have not been properly addressed. First, the label propagation typically proceeds over the lower order structure of the network and only the direct one-hop connections between nodes are taken into consideration. Unfortunately, the higher order structure that may encode design principle of the network and be crucial for community detection is neglected under this regime. Second, the stability of the identified community structure may also be seriously affected by the inherent randomness in the label propagation process. To tackle the above issues, this article proposes a\n            <jats:italic>Motif-Aware Weighted Label Propagation<\/jats:italic>\n            method for community detection. We focus on triangles within the network, but our technique extends to other kinds of motifs as well. Specifically, the motif-based higher order structure mining is conducted to capture structural characteristics of the network. First, the motif of interest (locally meaningful pattern) is identified, and then, the motif-based hypergraph can be constructed to encode the higher order connections. To further utilize the structural information of the network, a re-weighted network is designed, which unifies both the higher order structure and the original lower order structure. Accordingly, a novel voting strategy termed\n            <jats:italic>NaS<\/jats:italic>\n            (considering both &lt;underline&gt;N&lt;\/underline&gt;umber &lt;underline&gt;a&lt;\/underline&gt;nd &lt;underline&gt;S&lt;\/underline&gt;trength of connections) is proposed to update node labels during the label propagation process. In this way, the random label selection can be effectively eliminated, yielding more stable community structures. Experimental results on multiple real-world datasets have shown the superiority of the proposed method.\n          <\/jats:p>","DOI":"10.1145\/3378537","type":"journal-article","created":{"date-parts":[[2020,2,10]],"date-time":"2020-02-10T06:49:13Z","timestamp":1581317353000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":48,"title":["Community Detection by Motif-Aware Label Propagation"],"prefix":"10.1145","volume":"14","author":[{"given":"Pei-Zhen","family":"Li","sequence":"first","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, P.R. China"}]},{"given":"Ling","family":"Huang","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, P.R. China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5972-559X","authenticated-orcid":false,"given":"Chang-Dong","family":"Wang","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, P.R. China"}]},{"given":"Jian-Huang","family":"Lai","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, P.R. China"}]},{"given":"Dong","family":"Huang","sequence":"additional","affiliation":[{"name":"South China Agricultural University, Guangzhou, P.R. China"}]}],"member":"320","published-online":{"date-parts":[[2020,2,9]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1088\/1751-8113\/41\/22\/224001"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1178"},{"key":"e_1_2_1_3_1","volume-title":"Higher-order organization of complex networks. Science 353, 6295","author":"Benson Austin R.","year":"2016"},{"key":"e_1_2_1_4_1","volume-title":"Mining community structures in multidimensional networks. ACM Transactions on Knowledge Discovery from Data 11, 4","author":"Boutemine Oualid","year":"2017"},{"key":"e_1_2_1_5_1","volume-title":"Permanence and community structure in complex networks. 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Higher-order multi-layer community detection. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI\u201919) the 31st Innovative Applications of Artificial Intelligence Conference (IAAI\u201919) the 9th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI\u201919). 9945--9946.  Ling Huang Chang-Dong Wang and Hong-Yang Chao. 2019. Higher-order multi-layer community detection. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI\u201919) the 31st Innovative Applications of Artificial Intelligence Conference (IAAI\u201919) the 9th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI\u201919). 9945--9946.","DOI":"10.1609\/aaai.v33i01.33019945"},{"key":"e_1_2_1_16_1","volume-title":"Press","author":"Huang Ling","year":"2019"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-10-318"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bth163"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tcs.2008.07.017"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-018-9815-7"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2867549"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330882"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2566618"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2015.105"},{"key":"e_1_2_1_25_1","volume-title":"Detecting community structure using label propagation with weighted coherent neighborhood propinquity. 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