{"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/node-clustering/papers/2","list_of":"/task/node-clustering","task":"Node Clustering","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":2,"pages_in_order":2,"rows_per_page":100,"rows":[101,130],"of":130,"counts":{"archive_papers_tagged":130,"with_a_code_link":72,"where_syntology_ran_a_sample":21,"not_listed_spam_title":0,"listed":130,"listed_where_code_ran":21,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":19,"every_run_a_failure_of_syntologys_instrument":2,"listed_with_a_run_with_no_instrument_failure":19,"listed_every_run_a_failure_of_syntologys_instrument":2,"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/node-clustering","prev":"/task/node-clustering","next":null,"papers":[{"url":null,"slug":"scalable-deep-graph-clustering-with-random","title":"Scalable Deep Graph Clustering with Random-walk based Self-supervised Learning","date":"2021-12-31","arxiv_id":"2112.15530","repositories_listed":0,"syntology":null},{"url":null,"slug":"network-representation-learning-from-1","title":"Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding","date":"2021-10-14","arxiv_id":"2110.07582","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-hierarchical-embeddings-of-complex","title":"Scalable Hierarchical Embeddings of Complex Networks","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hmsg-heterogeneous-graph-neural-network-based","title":"HMSG: Heterogeneous Graph Neural Network based on Metapath Subgraph Learning","date":"2021-09-07","arxiv_id":"2109.02868","repositories_listed":0,"syntology":null},{"url":null,"slug":"heterogeneous-graph-neural-network-with-multi","title":"Heterogeneous Graph Neural Network with Multi-view Representation Learning","date":"2021-08-31","arxiv_id":"2108.13650","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-label-smoothing-to-regularize-large","title":"Adaptive Label Smoothing To Regularize Large-Scale Graph Training","date":"2021-08-30","arxiv_id":"2108.13555","repositories_listed":0,"syntology":null},{"url":null,"slug":"coane-modeling-context-co-occurrence-for","title":"CoANE: Modeling Context Co-occurrence for Attributed Network Embedding","date":"2021-06-17","arxiv_id":"2106.09241","repositories_listed":0,"syntology":null},{"url":null,"slug":"seeing-all-from-a-few-nodes-selection-using","title":"Seeing All From a Few: Nodes Selection Using Graph Pooling for Graph Clustering","date":"2021-04-30","arxiv_id":"2105.05320","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-co-embedding-learning-for","title":"Variational Co-embedding Learning for Attributed Network Clustering","date":"2021-04-15","arxiv_id":"2104.07295","repositories_listed":0,"syntology":null},{"url":null,"slug":"weighted-graph-nodes-clustering-via-gumbel","title":"Weighted Graph Nodes Clustering via Gumbel Softmax","date":"2021-02-22","arxiv_id":"2102.10775","repositories_listed":0,"syntology":null},{"url":null,"slug":"dissecting-graph-measures-performance-for","title":"Dissecting graph measures performance for node clustering in LFR parameter space","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-heterogeneous-graph-embedding","title":"A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources","date":"2020-11-30","arxiv_id":"2011.14867","repositories_listed":0,"syntology":null},{"url":null,"slug":"cagnn-cluster-aware-graph-neural-networks-for","title":"CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning","date":"2020-09-03","arxiv_id":"2009.01674","repositories_listed":0,"syntology":null},{"url":null,"slug":"inverse-graph-identification-can-we-identify","title":"Inverse Graph Identification: Can We Identify Node Labels Given Graph Labels?","date":"2020-07-12","arxiv_id":"2007.05970","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-heterogeneous-information-networks","title":"Pre-Trained Models for Heterogeneous Information Networks","date":"2020-07-07","arxiv_id":"2007.03184","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-randomized-shortest-paths-routing-with","title":"Sparse Randomized Shortest Paths Routing with Tsallis Divergence Regularization","date":"2020-07-01","arxiv_id":"2007.00419","repositories_listed":0,"syntology":null},{"url":null,"slug":"bipartite-link-prediction-based-on","title":"Bipartite Link Prediction based on Topological Features via 2-hop Path","date":"2020-03-19","arxiv_id":"2003.08572","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-convolutional-auto-encoder-with-bi","title":"Unsupervised Graph Embedding via Adaptive Graph Learning","date":"2020-03-10","arxiv_id":"2003.04508","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-mixed-clustering-coefficient-centrality-for","title":"A mixed clustering coefficient centrality for identifying essential proteins","date":"2020-03-07","arxiv_id":"2003.05057","repositories_listed":0,"syntology":null},{"url":null,"slug":"network-clustering-via-kernel-arma-modeling","title":"Network Clustering Via Kernel-ARMA Modeling and the Grassmannian The Brain-Network Case","date":"2020-02-18","arxiv_id":"2002.09943","repositories_listed":0,"syntology":null},{"url":null,"slug":"effective-decoding-in-graph-auto-encoder","title":"Effective Decoding in Graph Auto-Encoder using Triadic Closure","date":"2019-11-26","arxiv_id":"1911.11322","repositories_listed":0,"syntology":null},{"url":null,"slug":"randomized-shortest-paths-with-net-flows-and","title":"Randomized Shortest Paths with Net Flows and Capacity Constraints","date":"2019-10-04","arxiv_id":"1910.01849","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-learning-of-node-embeddings-by","title":"Unsupervised Learning of Node Embeddings by Detecting Communities","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"brain-network-clustering-via-kernel-arma","title":"Brain-Network Clustering via Kernel-ARMA Modeling and the Grassmannian","date":"2019-06-05","arxiv_id":"1906.02292","repositories_listed":0,"syntology":null},{"url":null,"slug":"relation-structure-aware-heterogeneous","title":"Relation Structure-Aware Heterogeneous Information Network Embedding","date":"2019-05-15","arxiv_id":"1905.08027","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-graph-embedding-with-adversarial","title":"Learning Graph Embedding with Adversarial Training Methods","date":"2019-01-04","arxiv_id":"1901.01250","repositories_listed":0,"syntology":null},{"url":null,"slug":"multilayer-graph-signal-clustering","title":"OrthoNet: Multilayer Network Data Clustering","date":"2018-11-02","arxiv_id":"1811.00821","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-view-graph-embedding-with-hub-detection","title":"Multi-view Graph Embedding with Hub Detection for Brain Network Analysis","date":"2017-09-12","arxiv_id":"1709.03659","repositories_listed":0,"syntology":null},{"url":null,"slug":"developments-in-the-theory-of-randomized","title":"Developments in the theory of randomized shortest paths with a comparison of graph node distances","date":"2012-12-07","arxiv_id":"1212.1666","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-discover-social-circles-in-ego","title":"Learning to Discover Social Circles in Ego Networks","date":"2012-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"48710e49a6d62c97af435fb26fe173adb00e6882567f0fbd8116a6b93a5d4bac","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}