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Many useful insights can be derived from graph-structured data as demonstrated by an ever-growing body of work focused on graph mining. However, in the real-world, graphs can be both large\u2014with many complex patterns\u2014and noisy, which can pose a problem for effective graph mining. An effective way to deal with this issue is to incorporate \u201cattention\u201d into graph mining solutions. An attention mechanism allows a method to focus on task-relevant parts of the graph, helping it to make better decisions. In this work, we conduct a comprehensive and focused survey of the literature on the emerging field of graph attention models. We introduce three intuitive taxonomies to group existing work. These are based on problem setting (type of input and output), the type of attention mechanism used, and the task (e.g., graph classification, link prediction). We motivate our taxonomies through detailed examples and use each to survey competing approaches from a unique standpoint. Finally, we highlight several challenges in the area and discuss promising directions for future work.<\/jats:p>","DOI":"10.1145\/3363574","type":"journal-article","created":{"date-parts":[[2019,11,12]],"date-time":"2019-11-12T21:41:21Z","timestamp":1573594881000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":189,"title":["Attention Models in Graphs"],"prefix":"10.1145","volume":"13","author":[{"given":"John Boaz","family":"Lee","sequence":"first","affiliation":[{"name":"WPI, MA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryan A.","family":"Rossi","sequence":"additional","affiliation":[{"name":"Adobe Research, San Jose, CA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sungchul","family":"Kim","sequence":"additional","affiliation":[{"name":"Adobe Research, San Jose, CA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nesreen K.","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Intel Labs, Santa Clara, CA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eunyee","family":"Koh","sequence":"additional","affiliation":[{"name":"Adobe Research, San Jose, CA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2019,11,11]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proc. 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