{"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/gpsp-graph-partition-and-space-projection","title":"GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network Embedding","arxiv_id":"1803.02590","date":"2018-03-07","proceeding":null,"authors":["Wenyu Du","Shuai Yu","Min Yang","Qiang Qu","Jia Zhu"],"abstract":"In this paper, we propose GPSP, a novel Graph Partition and Space Projection\nbased approach, to learn the representation of a heterogeneous network that\nconsists of multiple types of nodes and links. Concretely, we first partition\nthe heterogeneous network into homogeneous and bipartite subnetworks. Then, the\nprojective relations hidden in bipartite subnetworks are extracted by learning\nthe projective embedding vectors. Finally, we concatenate the projective\nvectors from bipartite subnetworks with the ones learned from homogeneous\nsubnetworks to form the final representation of the heterogeneous network.\nExtensive experiments are conducted on a real-life dataset. The results\ndemonstrate that GPSP outperforms the state-of-the-art baselines in two key\nnetwork mining tasks: node classification and clustering.","url_abs":"http://arxiv.org/abs/1803.02590v1","url_pdf":"http://arxiv.org/pdf/1803.02590v1.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":"gpsp-graph-partition-and-space-projection","repo_url":"https://github.com/Ange1o/GPSP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"classification","task_name":"General Classification"},{"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":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}