{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/attention-models-in-graphs-a-survey","title":"Attention Models in Graphs: A Survey","arxiv_id":"1807.07984","date":"2018-07-20","proceeding":null,"authors":["John Boaz Lee","Ryan A. Rossi","Sungchul Kim","Nesreen K. Ahmed","Eunyee Koh"],"abstract":"Graph-structured data arise naturally in many different application domains.\nBy representing data as graphs, we can capture entities (i.e., nodes) as well\nas their relationships (i.e., edges) with each other. Many useful insights can\nbe derived from graph-structured data as demonstrated by an ever-growing body\nof work focused on graph mining. However, in the real-world, graphs can be both\nlarge - with many complex patterns - and noisy which can pose a problem for\neffective graph mining. An effective way to deal with this issue is to\nincorporate \"attention\" into graph mining solutions. An attention mechanism\nallows a method to focus on task-relevant parts of the graph, helping it to\nmake better decisions. In this work, we conduct a comprehensive and focused\nsurvey of the literature on the emerging field of graph attention models. We\nintroduce three intuitive taxonomies to group existing work. These are based on\nproblem setting (type of input and output), the type of attention mechanism\nused, and the task (e.g., graph classification, link prediction, etc.). We\nmotivate our taxonomies through detailed examples and use each to survey\ncompeting approaches from a unique standpoint. Finally, we highlight several\nchallenges in the area and discuss promising directions for future work.","url_abs":"http://arxiv.org/abs/1807.07984v1","url_pdf":"http://arxiv.org/pdf/1807.07984v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"attention-models-in-graphs-a-survey","repo_url":"https://github.com/zhliping/Deep-Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"graph-attention","task_name":"Graph Attention"},{"task_slug":"graph-classification","task_name":"Graph Classification"},{"task_slug":"graph-mining","task_name":"Graph Mining"},{"task_slug":"link-prediction","task_name":"Link Prediction"},{"task_slug":"survey","task_name":"Survey"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.07984","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}