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Predicting node attributes in such graphs is an important task with applications in many domains like recommendation systems, privacy preservation, and targeted advertisement. Attribute values can be predicted by treating each node as a data point described by attributes and employing classification\/regression algorithms. However, in social networks, there is complex interdependence between node attributes and pairwise interaction. For instance, attributes of nodes are influenced by their neighbors (social influence), and neighborhoods (friendships) between nodes are established based on pairwise (dis)similarity between their attributes (social selection). In this article, we establish that information in network topology is extremely useful in determining node attributes. In particular, we use self- and cross-proclivity measures (quantitative measures of how much a node attribute depends on the same and other attributes of its neighbors) to predict node attributes. We propose a feature map to represent a node with respect to a specific attribute\n            <jats:italic>a<\/jats:italic>\n            , using all attributes of its\n            <jats:italic>h<\/jats:italic>\n            -hop neighbors. Different classifiers are then learned on these feature vectors to predict the value of attribute\n            <jats:italic>a<\/jats:italic>\n            . We perform extensive experimentation on 10 real-world datasets and show that the proposed method significantly outperforms known approaches in terms of prediction accuracy.\n          <\/jats:p>","DOI":"10.1145\/3442390","type":"journal-article","created":{"date-parts":[[2021,2,4]],"date-time":"2021-02-04T12:34:14Z","timestamp":1612442054000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":38,"title":["Predicting Attributes of Nodes Using Network Structure"],"prefix":"10.1145","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8121-2168","authenticated-orcid":false,"given":"Sarwan","family":"Ali","sequence":"first","affiliation":[{"name":"Lahore University of Management Sciences, Lahore, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Haroon","family":"Shakeel","sequence":"additional","affiliation":[{"name":"Lahore University of Management Sciences, Lahore, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6955-6168","authenticated-orcid":false,"given":"Imdadullah","family":"Khan","sequence":"additional","affiliation":[{"name":"Lahore University of Management Sciences, Lahore, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Safiullah","family":"Faizullah","sequence":"additional","affiliation":[{"name":"Islamic University, Madinah, Saudi Arabia, KSA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Asad","family":"Khan","sequence":"additional","affiliation":[{"name":"Hazara University, Mansehra, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,2,4]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2018.03.022"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2018.03.008"},{"key":"e_1_2_1_3_1","volume-title":"Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD\u201917)","author":"Rabbany R."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2819980"},{"key":"e_1_2_1_5_1","volume-title":"Joint European Conf. on Machine Learning and Knowledge Discovery in Databases. 601--616","author":"Ye W."},{"key":"e_1_2_1_6_1","unstructured":"W. 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