{"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/an-attention-based-collaboration-framework","title":"An Attention-based Collaboration Framework for Multi-View Network Representation Learning","arxiv_id":"1709.06636","date":"2017-09-19","proceeding":null,"authors":["Meng Qu","Jian Tang","Jingbo Shang","Xiang Ren","Ming Zhang","Jiawei Han"],"abstract":"Learning distributed node representations in networks has been attracting\nincreasing attention recently due to its effectiveness in a variety of\napplications. Existing approaches usually study networks with a single type of\nproximity between nodes, which defines a single view of a network. However, in\nreality there usually exists multiple types of proximities between nodes,\nyielding networks with multiple views. This paper studies learning node\nrepresentations for networks with multiple views, which aims to infer robust\nnode representations across different views. We propose a multi-view\nrepresentation learning approach, which promotes the collaboration of different\nviews and lets them vote for the robust representations. During the voting\nprocess, an attention mechanism is introduced, which enables each node to focus\non the most informative views. Experimental results on real-world networks show\nthat the proposed approach outperforms existing state-of-the-art approaches for\nnetwork representation learning with a single view and other competitive\napproaches with multiple views.","url_abs":"http://arxiv.org/abs/1709.06636v1","url_pdf":"http://arxiv.org/pdf/1709.06636v1.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":"an-attention-based-collaboration-framework","repo_url":"https://github.com/mnqu/MVE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1709.06636","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}