{"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/neural-brane-neural-bayesian-personalized","title":"Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding","arxiv_id":"1804.08774","date":"2018-04-23","proceeding":null,"authors":["Vachik S. Dave","Baichuan Zhang","Pin-Yu Chen","Mohammad Al Hasan"],"abstract":"Network embedding methodologies, which learn a distributed vector\nrepresentation for each vertex in a network, have attracted considerable\ninterest in recent years. Existing works have demonstrated that vertex\nrepresentation learned through an embedding method provides superior\nperformance in many real-world applications, such as node classification, link\nprediction, and community detection. However, most of the existing methods for\nnetwork embedding only utilize topological information of a vertex, ignoring a\nrich set of nodal attributes (such as, user profiles of an online social\nnetwork, or textual contents of a citation network), which is abundant in all\nreal-life networks. A joint network embedding that takes into account both\nattributional and relational information entails a complete network information\nand could further enrich the learned vector representations. In this work, we\npresent Neural-Brane, a novel Neural Bayesian Personalized Ranking based\nAttributed Network Embedding. For a given network, Neural-Brane extracts latent\nfeature representation of its vertices using a designed neural network model\nthat unifies network topological information and nodal attributes; Besides, it\nutilizes Bayesian personalized ranking objective, which exploits the proximity\nordering between a similar node-pair and a dissimilar node-pair. We evaluate\nthe quality of vertex embedding produced by Neural-Brane by solving the node\nclassification and clustering tasks on four real-world datasets. Experimental\nresults demonstrate the superiority of our proposed method over the\nstate-of-the-art existing methods.","url_abs":"http://arxiv.org/abs/1804.08774v2","url_pdf":"http://arxiv.org/pdf/1804.08774v2.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":"neural-brane-neural-bayesian-personalized","repo_url":"https://github.com/Vachik-Dave/Neural-Brane-Neural-Bayesian-Personalized-Ranking-for-Attributed-Network-Embedding","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"community-detection","task_name":"Community Detection"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"link-prediction","task_name":"Link Prediction"},{"task_slug":"network-embedding","task_name":"Network Embedding"},{"task_slug":"node-classification","task_name":"Node Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1804.08774","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}