{"about":{"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.","site":"https://codewithpapers.app","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","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/graph-embedding/papers/5","list_of":"/task/graph-embedding","task":"Graph Embedding","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":5,"pages_in_order":12,"rows_per_page":100,"rows":[401,500],"of":1192,"counts":{"archive_papers_tagged":1192,"with_a_code_link":533,"where_syntology_ran_a_sample":92,"not_listed_spam_title":0,"listed":1192,"listed_where_code_ran":92,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":83,"every_run_a_failure_of_syntologys_instrument":9,"listed_with_a_run_with_no_instrument_failure":83,"listed_every_run_a_failure_of_syntologys_instrument":9,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/graph-embedding","prev":"/task/graph-embedding/papers/4","next":"/task/graph-embedding/papers/6","papers":[{"url":"/paper/efficient-simple-and-automated-negative","slug":"efficient-simple-and-automated-negative","title":"Efficient, Simple and Automated Negative Sampling for Knowledge Graph Embedding","date":"2020-10-24","arxiv_id":"2010.14227","repositories_listed":1,"syntology":null},{"url":"/paper/explaining-neural-matrix-factorization-with","slug":"explaining-neural-matrix-factorization-with","title":"Explaining Neural Matrix Factorization with Gradient Rollback","date":"2020-10-12","arxiv_id":"2010.05516","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-complementary-nature-of-knowledge","slug":"on-the-complementary-nature-of-knowledge","title":"On the Complementary Nature of Knowledge Graph Embedding, Fine Grain Entity Types, and Language Modeling","date":"2020-10-12","arxiv_id":"2010.05732","repositories_listed":1,"syntology":null},{"url":"/paper/detect-all-abuse-toward-universal-abusive","slug":"detect-all-abuse-toward-universal-abusive","title":"Detect All Abuse! Toward Universal Abusive Language Detection Models","date":"2020-10-08","arxiv_id":"2010.03776","repositories_listed":1,"syntology":null},{"url":"/paper/disentangle-based-continual-graph","slug":"disentangle-based-continual-graph","title":"Disentangle-based Continual Graph Representation Learning","date":"2020-10-06","arxiv_id":"2010.02565","repositories_listed":1,"syntology":null},{"url":"/paper/libkge-a-knowledge-graph-embedding-library","slug":"libkge-a-knowledge-graph-embedding-library","title":"LibKGE - A knowledge graph embedding library for reproducible research","date":"2020-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/inductively-representing-out-of-knowledge","slug":"inductively-representing-out-of-knowledge","title":"Inductively Representing Out-of-Knowledge-Graph Entities by Optimal Estimation Under Translational Assumptions","date":"2020-09-27","arxiv_id":"2009.12765","repositories_listed":1,"syntology":null},{"url":"/paper/multi-view-brain-hyperconnectome-autoencoder","slug":"multi-view-brain-hyperconnectome-autoencoder","title":"Multi-View Brain HyperConnectome AutoEncoder For Brain State Classification","date":"2020-09-24","arxiv_id":"2009.11553","repositories_listed":1,"syntology":null},{"url":"/paper/scalable-recommendation-of-wikipedia-articles","slug":"scalable-recommendation-of-wikipedia-articles","title":"Scalable Recommendation of Wikipedia Articles to Editors Using Representation Learning","date":"2020-09-24","arxiv_id":"2009.11771","repositories_listed":1,"syntology":null},{"url":"/paper/force2vec-parallel-force-directed-graph","slug":"force2vec-parallel-force-directed-graph","title":"Force2Vec: Parallel force-directed graph embedding","date":"2020-09-17","arxiv_id":"2009.10035","repositories_listed":1,"syntology":null},{"url":"/paper/rdf2vec-light-a-lightweight-approachfor","slug":"rdf2vec-light-a-lightweight-approachfor","title":"RDF2Vec Light -- A Lightweight Approach for Knowledge Graph Embeddings","date":"2020-09-16","arxiv_id":"2009.07659","repositories_listed":1,"syntology":null},{"url":"/paper/understanding-coarsening-for-embedding-large","slug":"understanding-coarsening-for-embedding-large","title":"Understanding Coarsening for Embedding Large-Scale Graphs","date":"2020-09-10","arxiv_id":"2009.04925","repositories_listed":1,"syntology":null},{"url":"/paper/torchkge-knowledge-graph-embedding-in-python","slug":"torchkge-knowledge-graph-embedding-in-python","title":"TorchKGE: Knowledge Graph Embedding in Python and PyTorch","date":"2020-09-07","arxiv_id":"2009.02963","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-privacy-preserving-graph","slug":"adversarial-privacy-preserving-graph","title":"Adversarial Privacy Preserving Graph Embedding against Inference Attack","date":"2020-08-30","arxiv_id":"2008.13072","repositories_listed":1,"syntology":null},{"url":"/paper/deep-hypergraph-u-net-for-brain-graph","slug":"deep-hypergraph-u-net-for-brain-graph","title":"Deep Hypergraph U-Net for Brain Graph Embedding and Classification","date":"2020-08-30","arxiv_id":"2008.13118","repositories_listed":1,"syntology":null},{"url":"/paper/nase-learning-knowledge-graph-embedding-for","slug":"nase-learning-knowledge-graph-embedding-for","title":"NASE: Learning Knowledge Graph Embedding for Link Prediction via Neural Architecture Search","date":"2020-08-18","arxiv_id":"2008.07723","repositories_listed":1,"syntology":null},{"url":"/paper/quaternion-graph-neural-networks","slug":"quaternion-graph-neural-networks","title":"Quaternion Graph Neural Networks","date":"2020-08-12","arxiv_id":"2008.05089","repositories_listed":1,"syntology":{"n":13,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/quaternion-graph-neural-networks#ran","syntology_url":"https://syntology.ai/paper/2008.05089","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.05089"}},"official":{"repos":["daiquocnguyen/QGNN"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/dense-an-enhanced-non-abelian-group","slug":"dense-an-enhanced-non-abelian-group","title":"DensE: An Enhanced Non-commutative Representation for Knowledge Graph Embedding with Adaptive Semantic Hierarchy","date":"2020-08-11","arxiv_id":"2008.04548","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-directed-graph-embedding","slug":"adversarial-directed-graph-embedding","title":"Adversarial Directed Graph Embedding","date":"2020-08-09","arxiv_id":"2008.03667","repositories_listed":1,"syntology":null},{"url":"/paper/funcgnn-a-graph-neural-network-approach-to","slug":"funcgnn-a-graph-neural-network-approach-to","title":"funcGNN: A Graph Neural Network Approach to Program Similarity","date":"2020-07-26","arxiv_id":"2007.13239","repositories_listed":1,"syntology":null},{"url":"/paper/graph-based-prediction-of-protein-protein","slug":"graph-based-prediction-of-protein-protein","title":"Graph-based prediction of Protein-protein interactions with attributed signed graph embedding","date":"2020-07-21","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/integrating-network-embedding-and-community","slug":"integrating-network-embedding-and-community","title":"Integrating Network Embedding and Community Outlier Detection via Multiclass Graph Description","date":"2020-07-20","arxiv_id":"2007.10231","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-graph-encoder-for-attributed-graph","slug":"adaptive-graph-encoder-for-attributed-graph","title":"Adaptive Graph Encoder for Attributed Graph Embedding","date":"2020-07-03","arxiv_id":"2007.01594","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adaptive-graph-encoder-for-attributed-graph#ran","syntology_url":"https://syntology.ai/paper/2007.01594","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.01594"}},"official":{"repos":["thunlp/AGE"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/can-we-predict-new-facts-with-open-knowledge","slug":"can-we-predict-new-facts-with-open-knowledge","title":"Can We Predict New Facts with Open Knowledge Graph Embeddings? A Benchmark for Open Link Prediction","date":"2020-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/approximating-network-centrality-measures","slug":"approximating-network-centrality-measures","title":"Approximating Network Centrality Measures Using Node Embedding and Machine Learning","date":"2020-06-29","arxiv_id":"2006.16392","repositories_listed":1,"syntology":null},{"url":"/paper/characterizing-the-expressive-power-of","slug":"characterizing-the-expressive-power-of","title":"Expressive Power of Invariant and Equivariant Graph Neural Networks","date":"2020-06-28","arxiv_id":"2006.15646","repositories_listed":1,"syntology":{"n":14,"n_ran":8,"n_constructed":4,"n_ran_checked":6,"n_instrument":2,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"8 ran (of which 4 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/characterizing-the-expressive-power-of#ran","syntology_url":"https://syntology.ai/paper/2006.15646","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.15646"}},"official":{"repos":["mlelarge/graph_neural_net"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":3,"n_ran_no_instrument_failure":4,"n_unverified":6,"ran_from_kinds":["community","official"]}}},{"url":"/paper/benchmark-and-best-practices-for-biomedical-1","slug":"benchmark-and-best-practices-for-biomedical-1","title":"Benchmark and Best Practices for Biomedical Knowledge Graph Embeddings","date":"2020-06-24","arxiv_id":"2006.13774","repositories_listed":1,"syntology":null},{"url":"/paper/treernn-topology-preserving-deep","slug":"treernn-topology-preserving-deep","title":"TreeRNN: Topology-Preserving Deep GraphEmbedding and Learning","date":"2020-06-21","arxiv_id":"2006.11825","repositories_listed":1,"syntology":null},{"url":"/paper/wasserstein-embedding-for-graph-learning","slug":"wasserstein-embedding-for-graph-learning","title":"Wasserstein Embedding for Graph Learning","date":"2020-06-16","arxiv_id":"2006.09430","repositories_listed":1,"syntology":null},{"url":"/paper/generalized-multi-relational-graph","slug":"generalized-multi-relational-graph","title":"Knowledge Embedding Based Graph Convolutional Network","date":"2020-06-12","arxiv_id":"2006.07331","repositories_listed":1,"syntology":{"n":12,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":7,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/generalized-multi-relational-graph#ran","syntology_url":"https://syntology.ai/paper/2006.07331","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.07331"}},"official":{"repos":["Maysir/GEM-GCN"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/little-ball-of-fur-a-python-library-for-graph","slug":"little-ball-of-fur-a-python-library-for-graph","title":"Little Ball of Fur: A Python Library for Graph Sampling","date":"2020-06-08","arxiv_id":"2006.04311","repositories_listed":1,"syntology":null},{"url":"/paper/deep-graph-generators","slug":"deep-graph-generators","title":"DeepGG: a Deep Graph Generator","date":"2020-06-07","arxiv_id":"2006.04159","repositories_listed":1,"syntology":null},{"url":"/paper/persona2vec-a-flexible-multi-role","slug":"persona2vec-a-flexible-multi-role","title":"Persona2vec: A Flexible Multi-role Representations Learning Framework for Graphs","date":"2020-06-04","arxiv_id":"2006.04941","repositories_listed":1,"syntology":null},{"url":"/paper/learning-representations-using-spectral","slug":"learning-representations-using-spectral","title":"Learning Representations using Spectral-Biased Random Walks on Graphs","date":"2020-05-19","arxiv_id":"2005.09752","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-for-community-detection","slug":"deep-learning-for-community-detection","title":"Deep Learning for Community Detection: Progress, Challenges and Opportunities","date":"2020-05-17","arxiv_id":"2005.08225","repositories_listed":1,"syntology":null},{"url":"/paper/deepsocs-a-neural-scheduler-for-heterogeneous","slug":"deepsocs-a-neural-scheduler-for-heterogeneous","title":"DeepSoCS: A Neural Scheduler for Heterogeneous System-on-Chip (SoC) Resource Scheduling","date":"2020-05-15","arxiv_id":"2005.07666","repositories_listed":1,"syntology":null},{"url":"/paper/asymmetric-transitivity-preserving-graph","slug":"asymmetric-transitivity-preserving-graph","title":"Asymmetric Transitivity Preserving Graph Embedding","date":"2020-05-13","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-graph-embeddings-for-fair","slug":"adversarial-graph-embeddings-for-fair","title":"Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks","date":"2020-05-08","arxiv_id":"2005.04074","repositories_listed":1,"syntology":null},{"url":"/paper/machine-learning-on-graphs-a-model-and","slug":"machine-learning-on-graphs-a-model-and","title":"Machine Learning on Graphs: A Model and Comprehensive Taxonomy","date":"2020-05-07","arxiv_id":"2005.03675","repositories_listed":1,"syntology":{"n":8,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/machine-learning-on-graphs-a-model-and#ran","syntology_url":"https://syntology.ai/paper/2005.03675","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.03675"}},"official":{"repos":["google/gcnn-survey-paper"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-embedding-empowered-entity-retrieval","slug":"graph-embedding-empowered-entity-retrieval","title":"Graph-Embedding Empowered Entity Retrieval","date":"2020-05-06","arxiv_id":"2005.02843","repositories_listed":1,"syntology":null},{"url":"/paper/learning-geo-contextual-embeddings-for","slug":"learning-geo-contextual-embeddings-for","title":"Learning Geo-Contextual Embeddings for Commuting Flow Prediction","date":"2020-05-04","arxiv_id":"2005.01690","repositories_listed":1,"syntology":null},{"url":"/paper/seek-segmented-embedding-of-knowledge-graphs","slug":"seek-segmented-embedding-of-knowledge-graphs","title":"SEEK: Segmented Embedding of Knowledge Graphs","date":"2020-05-02","arxiv_id":"2005.00856","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-triple-encoder-for-fast-open-set","slug":"semantic-triple-encoder-for-fast-open-set","title":"Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion","date":"2020-04-30","arxiv_id":"2004.14781","repositories_listed":1,"syntology":null},{"url":"/paper/se-kge-a-location-aware-knowledge-graph","slug":"se-kge-a-location-aware-knowledge-graph","title":"SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Lifting","date":"2020-04-25","arxiv_id":"2004.14171","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/se-kge-a-location-aware-knowledge-graph#ran","syntology_url":"https://syntology.ai/paper/2004.14171","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.14171"}},"official":null}},{"url":"/paper/supervised-domain-adaptation-were-we-doing","slug":"supervised-domain-adaptation-were-we-doing","title":"Supervised Domain Adaptation: A Graph Embedding Perspective and a Rectified Experimental Protocol","date":"2020-04-23","arxiv_id":"2004.11262","repositories_listed":1,"syntology":null},{"url":"/paper/graph-convolutional-subspace-clustering-a","slug":"graph-convolutional-subspace-clustering-a","title":"Graph Convolutional Subspace Clustering: A Robust Subspace Clustering Framework for Hyperspectral Image","date":"2020-04-22","arxiv_id":"2004.10476","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-graph-embedding-with-linear","slug":"knowledge-graph-embedding-with-linear","title":"LineaRE: Simple but Powerful Knowledge Graph Embedding for Link Prediction","date":"2020-04-21","arxiv_id":"2004.10037","repositories_listed":1,"syntology":null},{"url":"/paper/network-principled-deep-generative-models-for","slug":"network-principled-deep-generative-models-for","title":"Network-principled deep generative models for designing drug combinations as graph sets","date":"2020-04-16","arxiv_id":"2004.07782","repositories_listed":1,"syntology":null},{"url":"/paper/vgcn-bert-augmenting-bert-with-graph","slug":"vgcn-bert-augmenting-bert-with-graph","title":"VGCN-BERT: Augmenting BERT with Graph Embedding for Text Classification","date":"2020-04-12","arxiv_id":"2004.05707","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/vgcn-bert-augmenting-bert-with-graph#ran","syntology_url":"https://syntology.ai/paper/2004.05707","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.05707"}},"official":{"repos":["Louis-udm/VGCN-BERT"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/iterative-context-aware-graph-inference-for","slug":"iterative-context-aware-graph-inference-for","title":"Iterative Context-Aware Graph Inference for Visual Dialog","date":"2020-04-05","arxiv_id":"2004.02194","repositories_listed":1,"syntology":null},{"url":"/paper/k-core-based-temporal-graph-convolutional","slug":"k-core-based-temporal-graph-convolutional","title":"K-Core based Temporal Graph Convolutional Network for Dynamic Graphs","date":"2020-03-22","arxiv_id":"2003.09902","repositories_listed":1,"syntology":null},{"url":"/paper/supervised-domain-adaptation-using-graph","slug":"supervised-domain-adaptation-using-graph","title":"Supervised Domain Adaptation using Graph Embedding","date":"2020-03-09","arxiv_id":"2003.04063","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-graph-auto-encoder-for-general-data","slug":"adaptive-graph-auto-encoder-for-general-data","title":"Adaptive Graph Auto-Encoder for General Data Clustering","date":"2020-02-20","arxiv_id":"2002.08648","repositories_listed":1,"syntology":null},{"url":"/paper/learning-graph-representations-of-biochemical","slug":"learning-graph-representations-of-biochemical","title":"Learning graph representations of biochemical networks and its application to enzymatic link prediction","date":"2020-02-09","arxiv_id":"2002.03410","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-graph-embedding-for-link-prediction","slug":"knowledge-graph-embedding-for-link-prediction","title":"Knowledge Graph Embedding for Link Prediction: A Comparative Analysis","date":"2020-02-03","arxiv_id":"2002.00819","repositories_listed":1,"syntology":null},{"url":"/paper/a-survey-on-knowledge-graphs-representation","slug":"a-survey-on-knowledge-graphs-representation","title":"A Survey on Knowledge Graphs: Representation, Acquisition and Applications","date":"2020-02-02","arxiv_id":"2002.00388","repositories_listed":1,"syntology":null},{"url":"/paper/louvainne-hierarchical-louvain-method-for-1","slug":"louvainne-hierarchical-louvain-method-for-1","title":"LouvainNE: Hierarchical Louvain Method for High Quality and Scalable Network Embedding","date":"2020-02-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/which-way-direction-aware-attributed-graph","slug":"which-way-direction-aware-attributed-graph","title":"Which way? Direction-Aware Attributed Graph Embedding","date":"2020-01-30","arxiv_id":"2001.11297","repositories_listed":1,"syntology":null},{"url":"/paper/the-keen-universe-an-ecosystem-for-knowledge","slug":"the-keen-universe-an-ecosystem-for-knowledge","title":"The KEEN Universe: An Ecosystem for Knowledge Graph Embeddings with a Focus on Reproducibility and Transferability","date":"2020-01-28","arxiv_id":"2001.10560","repositories_listed":1,"syntology":null},{"url":"/paper/gl2vec-graph-embedding-enriched-by-line","slug":"gl2vec-graph-embedding-enriched-by-line","title":"GL2vec: Graph Embedding Enriched by Line Graphs with Edge Features","date":"2020-01-23","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-physical-embedding-model-for-knowledge","slug":"a-physical-embedding-model-for-knowledge","title":"A Physical Embedding Model for Knowledge Graphs","date":"2020-01-21","arxiv_id":"2001.07418","repositories_listed":1,"syntology":null},{"url":"/paper/can-x2vec-save-lives-integrating-graph-and","slug":"can-x2vec-save-lives-integrating-graph-and","title":"Can x2vec Save Lives? Integrating Graph and Language Embeddings for Automatic Mental Health Classification","date":"2020-01-04","arxiv_id":"2001.01126","repositories_listed":1,"syntology":null},{"url":"/paper/an-attention-based-graph-neural-network-for","slug":"an-attention-based-graph-neural-network-for","title":"An Attention-based Graph Neural Network for Heterogeneous Structural Learning","date":"2019-12-19","arxiv_id":"1912.10832","repositories_listed":1,"syntology":{"n":3,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 3 unverified","sample_list":"/paper/an-attention-based-graph-neural-network-for#ran","syntology_url":"https://syntology.ai/paper/1912.10832","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.10832"}},"official":{"repos":["didi/hetsann"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"url":"/paper/bridging-the-gap-between-community-and-node","slug":"bridging-the-gap-between-community-and-node","title":"Bridging the Gap between Community and Node Representations: Graph Embedding via Community Detection","date":"2019-12-17","arxiv_id":"1912.08808","repositories_listed":1,"syntology":null},{"url":"/paper/gaussian-embedding-of-large-scale-attributed","slug":"gaussian-embedding-of-large-scale-attributed","title":"Gaussian Embedding of Large-scale Attributed Graphs","date":"2019-12-02","arxiv_id":"1912.00536","repositories_listed":1,"syntology":null},{"url":"/paper/conditional-structure-generation-through","slug":"conditional-structure-generation-through","title":"Conditional Structure Generation through Graph Variational Generative Adversarial Nets","date":"2019-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/generalizable-resource-allocation-in-stream","slug":"generalizable-resource-allocation-in-stream","title":"Generalizable Resource Allocation in Stream Processing via Deep Reinforcement Learning","date":"2019-11-19","arxiv_id":"1911.08517","repositories_listed":1,"syntology":null},{"url":"/paper/decompressing-knowledge-graph-representations","slug":"decompressing-knowledge-graph-representations","title":"Decompressing Knowledge Graph Representations for Link Prediction","date":"2019-11-11","arxiv_id":"1911.04053","repositories_listed":1,"syntology":null},{"url":"/paper/time2graph-revisiting-time-series-modeling","slug":"time2graph-revisiting-time-series-modeling","title":"Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets","date":"2019-11-11","arxiv_id":"1911.04143","repositories_listed":1,"syntology":null},{"url":"/paper/is-performance-of-scholars-correlated-to","slug":"is-performance-of-scholars-correlated-to","title":"Is Performance of Scholars Correlated to Their Research Collaboration Patterns?","date":"2019-11-05","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/191013314","slug":"191013314","title":"Symbolic Graph Embedding using Frequent Pattern Mining","date":"2019-10-29","arxiv_id":"1910.13314","repositories_listed":1,"syntology":null},{"url":"/paper/graphzoom-a-multi-level-spectral-approach-for","slug":"graphzoom-a-multi-level-spectral-approach-for","title":"GraphZoom: A multi-level spectral approach for accurate and scalable graph embedding","date":"2019-10-06","arxiv_id":"1910.02370","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/graphzoom-a-multi-level-spectral-approach-for#ran","syntology_url":"https://syntology.ai/paper/1910.02370","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.02370"}},"official":{"repos":["cornell-zhang/GraphZoom"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/attpool-towards-hierarchical-feature","slug":"attpool-towards-hierarchical-feature","title":"AttPool: Towards Hierarchical Feature Representation in Graph Convolutional Networks via Attention Mechanism","date":"2019-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/deep-clustering-by-gaussian-mixture","slug":"deep-clustering-by-gaussian-mixture","title":"Deep Clustering by Gaussian Mixture Variational Autoencoders With Graph Embedding","date":"2019-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-universal-self-attention-network","slug":"unsupervised-universal-self-attention-network","title":"Universal Graph Transformer Self-Attention Networks","date":"2019-09-26","arxiv_id":"1909.11855","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unsupervised-universal-self-attention-network#ran","syntology_url":"https://syntology.ai/paper/1909.11855","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.11855"}},"official":{"repos":["daiquocnguyen/Graph-Transformer"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/190910086","slug":"190910086","title":"Learning Universal Graph Neural Network Embeddings With Aid Of Transfer Learning","date":"2019-09-22","arxiv_id":"1909.10086","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-scholarly-data-by-semantic-query-on","slug":"exploring-scholarly-data-by-semantic-query-on","title":"Exploring Scholarly Data by Semantic Query on Knowledge Graph Embedding Space","date":"2019-09-17","arxiv_id":"1909.08191","repositories_listed":1,"syntology":null},{"url":"/paper/graph-representation-ensemble-learning","slug":"graph-representation-ensemble-learning","title":"Graph Representation Ensemble Learning","date":"2019-09-06","arxiv_id":"1909.02811","repositories_listed":1,"syntology":null},{"url":"/paper/graph-representation-learning-a-survey","slug":"graph-representation-learning-a-survey","title":"Graph Representation Learning: A Survey","date":"2019-09-03","arxiv_id":"1909.00958","repositories_listed":1,"syntology":null},{"url":"/paper/semantically-regularized-logic-graph","slug":"semantically-regularized-logic-graph","title":"Embedding Symbolic Knowledge into Deep Networks","date":"2019-09-03","arxiv_id":"1909.01161","repositories_listed":1,"syntology":null},{"url":"/paper/detect-camouflaged-spam-content-via","slug":"detect-camouflaged-spam-content-via","title":"Detect Camouflaged Spam Content via StoneSkipping: Graph and Text Joint Embedding for Chinese Character Variation Representation","date":"2019-08-30","arxiv_id":"1908.11561","repositories_listed":1,"syntology":null},{"url":"/paper/bayes-embedding-bem-refining-representation","slug":"bayes-embedding-bem-refining-representation","title":"Bayes EMbedding (BEM): Refining Representation by Integrating Knowledge Graphs and Behavior-specific Networks","date":"2019-08-28","arxiv_id":"1908.10611","repositories_listed":1,"syntology":null},{"url":"/paper/molecule-property-prediction-based-on-spatial","slug":"molecule-property-prediction-based-on-spatial","title":"Molecule Property Prediction Based on Spatial Graph Embedding","date":"2019-08-22","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/benchmarks-for-graph-embedding-evaluation","slug":"benchmarks-for-graph-embedding-evaluation","title":"Benchmarks for Graph Embedding Evaluation","date":"2019-08-19","arxiv_id":"1908.06543","repositories_listed":1,"syntology":null},{"url":"/paper/invariant-embedding-for-graph-classification","slug":"invariant-embedding-for-graph-classification","title":"Invariant embedding for graph classification","date":"2019-08-18","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/the-general-black-box-attack-method-for-graph","slug":"the-general-black-box-attack-method-for-graph","title":"A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models","date":"2019-08-04","arxiv_id":"1908.01297","repositories_listed":1,"syntology":null},{"url":"/paper/relational-memory-based-knowledge-graph","slug":"relational-memory-based-knowledge-graph","title":"A Relational Memory-based Embedding Model for Triple Classification and Search Personalization","date":"2019-07-13","arxiv_id":"1907.06080","repositories_listed":1,"syntology":null},{"url":"/paper/graph-representation-learning-via-hard-and","slug":"graph-representation-learning-via-hard-and","title":"Graph Representation Learning via Hard and Channel-Wise Attention Networks","date":"2019-07-05","arxiv_id":"1907.04652","repositories_listed":1,"syntology":null},{"url":"/paper/network-embedding-on-compression-and-learning","slug":"network-embedding-on-compression-and-learning","title":"Network Embedding: on Compression and Learning","date":"2019-07-05","arxiv_id":"1907.02811","repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-word-sense-disambiguation-using","slug":"zero-shot-word-sense-disambiguation-using","title":"Zero-shot Word Sense Disambiguation using Sense Definition Embeddings","date":"2019-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/ego-cnn-distributed-egocentric","slug":"ego-cnn-distributed-egocentric","title":"Ego-CNN: Distributed, Egocentric Representations of Graphs for Detecting Critical Structures","date":"2019-06-23","arxiv_id":"1906.09602","repositories_listed":1,"syntology":{"n":6,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/ego-cnn-distributed-egocentric#ran","syntology_url":"https://syntology.ai/paper/1906.09602","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.09602"}},"official":{"repos":["rutzeng/EgoCNN"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-embedding-based-wireless-link","slug":"graph-embedding-based-wireless-link","title":"Graph Embedding based Wireless Link Scheduling with Few Training Samples","date":"2019-06-07","arxiv_id":"1906.02871","repositories_listed":1,"syntology":null},{"url":"/paper/multi-view-knowledge-graph-embedding-for","slug":"multi-view-knowledge-graph-embedding-for","title":"Multi-view Knowledge Graph Embedding for Entity Alignment","date":"2019-06-06","arxiv_id":"1906.02390","repositories_listed":1,"syntology":{"n":21,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":11,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 11 unverified","sample_list":"/paper/multi-view-knowledge-graph-embedding-for#ran","syntology_url":"https://syntology.ai/paper/1906.02390","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.02390"}},"official":{"repos":["nju-websoft/MultiKE"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":11,"ran_from_kinds":["official"]}}},{"url":"/paper/pykg2vec-a-python-library-for-knowledge-graph","slug":"pykg2vec-a-python-library-for-knowledge-graph","title":"Pykg2vec: A Python Library for Knowledge Graph Embedding","date":"2019-06-04","arxiv_id":"1906.04239","repositories_listed":1,"syntology":null},{"url":"/paper/compositional-fairness-constraints-for-graph","slug":"compositional-fairness-constraints-for-graph","title":"Compositional Fairness Constraints for Graph Embeddings","date":"2019-05-25","arxiv_id":"1905.10674","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/compositional-fairness-constraints-for-graph#ran","syntology_url":"https://syntology.ai/paper/1905.10674","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.10674"}},"official":{"repos":["joeybose/Flexible-Fairness-Constraints"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/speed-up-and-multi-view-extensions-to","slug":"speed-up-and-multi-view-extensions-to","title":"Speed-up and multi-view extensions to Subclass Discriminant Analysis","date":"2019-05-02","arxiv_id":"1905.00794","repositories_listed":1,"syntology":null},{"url":"/paper/quaternion-knowledge-graph-embedding","slug":"quaternion-knowledge-graph-embedding","title":"Quaternion Knowledge Graph Embeddings","date":"2019-04-23","arxiv_id":"1904.10281","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-graph-classification-a","slug":"semi-supervised-graph-classification-a","title":"Semi-Supervised Graph Classification: A Hierarchical Graph Perspective","date":"2019-04-10","arxiv_id":"1904.05003","repositories_listed":1,"syntology":null},{"url":"/paper/incremental-embedding-for-temporal-networks","slug":"incremental-embedding-for-temporal-networks","title":"FILDNE: A Framework for Incremental Learning of Dynamic Networks Embeddings","date":"2019-04-06","arxiv_id":"1904.03423","repositories_listed":1,"syntology":null},{"url":"/paper/dagcn-dual-attention-graph-convolutional","slug":"dagcn-dual-attention-graph-convolutional","title":"DAGCN: Dual Attention Graph Convolutional Networks","date":"2019-04-04","arxiv_id":"1904.02278","repositories_listed":1,"syntology":null}],"record_sha256":"302405ce40df80117fe4718d72eec7b4d2b8e3b652990799174993e9ab2132ed","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}