{"id":171496,"date":"2015-08-28T00:30:00","date_gmt":"2015-08-28T00:30:00","guid":{"rendered":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/project\/line-large-scale-information-network-embedding\/"},"modified":"2016-04-07T03:09:45","modified_gmt":"2016-04-07T03:09:45","slug":"line-large-scale-information-network-embedding","status":"publish","type":"msr-project","link":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/project\/line-large-scale-information-network-embedding\/","title":{"rendered":"LINE: Large-scale Information Network Embedding"},"content":{"rendered":"<div class=\"asset-content\">Embedding information networks into low-dimensional spaces is potentially useful in many applications such as visualization, node classification, link prediction and recommendation. In this project, we proposed a large-scale information network embedding model called the &#8220;LINE&#8221;, which is suitable for arbitrary types of information networks: undirected, directed, and\/or weighted.<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Embedding information networks into low-dimensional spaces is potentially useful in many applications such as visualization, node classification, link prediction and recommendation. In this project, we proposed a large-scale information network embedding model called the &#8220;LINE&#8221;, which is suitable for arbitrary types of information networks: undirected, directed, and\/or weighted.<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"research-area":[13556,13545,13559],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-171496","msr-project","type-msr-project","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-human-language-technologies","msr-research-area-social-sciences","msr-locale-en_us","msr-archive-status-active"],"msr_project_start":"8\/28\/2015","related-publications":[168679,168681],"related-downloads":[],"related-videos":[],"related-groups":[],"related-events":[],"related-opportunities":[],"related-posts":[],"related-articles":[],"tab-content":[],"slides":[],"related-researchers":[],"msr_research_lab":[199560],"msr_impact_theme":[],"_links":{"self":[{"href":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/171496","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-json\/wp\/v2\/msr-project"}],"about":[{"href":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-project"}],"version-history":[{"count":0,"href":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/171496\/revisions"}],"wp:attachment":[{"href":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-json\/wp\/v2\/media?parent=171496"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=171496"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=171496"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=171496"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=171496"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}