{"work":{"id":"2e8716eb-69bd-4617-8256-8543ba2c215c","openalex_id":"https://openalex.org/W2554952599","doi":"10.48550/arxiv.1611.07308","arxiv_id":"1611.07308","raw_key":null,"title":"Variational Graph Auto-Encoders","authors":null,"authors_text":"Thomas N","year":2016,"venue":"stat.ML","abstract":"We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE). This model makes use of latent variables and is capable of learning interpretable latent representations for undirected graphs. We demonstrate this model using a graph convolutional network (GCN) encoder and a simple inner product decoder. Our model achieves competitive results on a link prediction task in citation networks. In contrast to most existing models for unsupervised learning on graph-structured data and link prediction, our model can naturally incorporate node features, which significantly improves predictive performance on a number of benchmark datasets.","external_url":"https://arxiv.org/abs/1611.07308","cited_by_count":897,"metadata_source":"pith","metadata_fetched_at":"2026-08-05T02:28:24.338817+00:00","pith_arxiv_id":"1611.07308","created_at":"2026-05-09T06:10:42.231700+00:00","updated_at":"2026-08-05T02:28:24.338817+00:00","title_quality_ok":true,"display_title":"Variational Graph Auto-Encoders","render_title":"Variational Graph Auto-Encoders"},"hub":{"state":{"work_id":"2e8716eb-69bd-4617-8256-8543ba2c215c","tier":"hub","tier_reason":"10+ Pith inbound or 1,000+ external citations","pith_inbound_count":51,"external_cited_by_count":897,"distinct_field_count":13,"first_pith_cited_at":"2019-07-03T02:02:39+00:00","last_pith_cited_at":"2026-07-01T09:17:28+00:00","author_build_status":"not_needed","summary_status":"needed","contexts_status":"needed","graph_status":"needed","ask_index_status":"not_needed","reader_status":"not_needed","recognition_status":"not_needed","updated_at":"2026-08-21T06:19:29.372408+00:00","tier_text":"hub"},"tier":"hub","role_counts":[{"context_role":"background","n":4}],"polarity_counts":[{"context_polarity":"background","n":3},{"context_polarity":"support","n":1}],"runs":{},"summary":{},"graph":{},"authors":[]}}