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This calls for a community detection technique that is able to handle both attribute and topological information on large scale networks, and also is incremental. In this article, we propose inc-AGGMMR, an incremental community detection framework that is able to effectively address the challenges that come from scalability, mixed attributes, incomplete values, and evolving of the network. Through construction of augmented graph, we map attributes into the network by introducing attribute centers and belongingness edges. The communities are then detected by modularity maximization. During this process, we adjust the weights of belongingness edges to balance the contribution between attribute and topological information to the detection of communities. The weight adjustment mechanism enables incremental updates of community membership of all vertices. We evaluate inc-AGGMMR on five benchmark datasets against eight strong baselines. We also provide a case study to incrementally detect communities on a PayPal payment network which contains\n            <jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>\n                  \n                <\/jats:tex-math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula>\n            users with\n            <jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>\n                  \n                <\/jats:tex-math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula>\n            transactions. The results demonstrate inc-AGGMMR\u2019s effectiveness and practicability.\n          <\/jats:p>","DOI":"10.1145\/3451216","type":"journal-article","created":{"date-parts":[[2021,5,19]],"date-time":"2021-05-19T17:08:53Z","timestamp":1621444133000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Incremental Community Detection on Large Complex Attributed Network"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5902-4045","authenticated-orcid":false,"given":"Zhe","family":"Chen","sequence":"first","affiliation":[{"name":"PayPal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aixin","family":"Sun","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaokui","family":"Xiao","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,5,19]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/1283383.1283494"},{"key":"e_1_2_1_2_1","volume-title":"2013 IEEE 13th International Conference on Data Mining. 955\u2013960","author":"Barbieri N.","unstructured":"N. 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