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Knowl. Discov. Data"],"published-print":{"date-parts":[[2018,6,30]]},"abstract":"<jats:p>The social triad\u2014a group of three people\u2014is one of the simplest and most fundamental social groups. Extensive network and social theories have been developed to understand its structure, such as triadic closure and social balance. Over the course of a triadic closure\u2014the transition from two ties to three among three users, the strength dynamics of its social ties, however, are much less well understood. Using two dynamic networks from social media and mobile communication, we examine how the formation of the third tie in a triad affects the strength of the existing two ties. Surprisingly, we find that in about 80% social triads, the strength of the first two ties is weakened although averagely the tie strength in the two networks maintains an increasing or stable trend. We discover that (1) the decrease in tie strength among three males is more sharply than that among females, and (2) the tie strength between celebrities is more likely to be weakened as the closure of a triad than those between ordinary people. Furthermore, we formalize a triadic tie strength dynamics prediction problem to infer whether social ties of a triad will become weakened after its closure. We propose a TRIST method\u2014a kernel density estimation (KDE)-based graphical model\u2014to solve the problem by incorporating user demographics, temporal effects, and structural information. Extensive experiments demonstrate that TRIST offers a greater than 82% potential predictability for inferring triadic tie strength dynamics in both networks. The leveraging of the KDE and structural correlations enables TRIST to outperform baselines by up to 30% in terms of F1-score.<\/jats:p>","DOI":"10.1145\/3154399","type":"journal-article","created":{"date-parts":[[2018,1,23]],"date-time":"2018-01-23T14:53:36Z","timestamp":1516719216000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":31,"title":["Will Triadic Closure Strengthen Ties in Social Networks?"],"prefix":"10.1145","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5282-551X","authenticated-orcid":false,"given":"Hong","family":"Huang","sequence":"first","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxiao","family":"Dong","sequence":"additional","affiliation":[{"name":"Microsoft Research Redmond, Redmond, WA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Tang","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongxia","family":"Yang","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hanzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nitesh V.","family":"Chawla","sequence":"additional","affiliation":[{"name":"University of Notre Dame, Norte Dame, IN"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoming","family":"Fu","sequence":"additional","affiliation":[{"name":"University of Goettingen, Goettingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,1,23]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390160"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401898"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2556288.2557094"},{"key":"e_1_2_1_4_1","volume-title":"Two against one: Coalitions in triads","author":"Caplow Theodore","unstructured":"Theodore Caplow . 1968. 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