{"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/neighborhood-aware-scalable-temporal-network","title":"Neighborhood-aware Scalable Temporal Network Representation Learning","arxiv_id":"2209.01084","date":"2022-09-02","proceeding":null,"authors":["Yuhong Luo","Pan Li"],"abstract":"Temporal networks have been widely used to model real-world complex systems such as financial systems and e-commerce systems. In a temporal network, the joint neighborhood of a set of nodes often provides crucial structural information useful for predicting whether they may interact at a certain time. However, recent representation learning methods for temporal networks often fail to extract such information or depend on online construction of structural features, which is time-consuming. To address the issue, this work proposes Neighborhood-Aware Temporal network model (NAT). For each node in the network, NAT abandons the commonly-used one-single-vector-based representation while adopting a novel dictionary-type neighborhood representation. Such a dictionary representation records a downsampled set of the neighboring nodes as keys, and allows fast construction of structural features for a joint neighborhood of multiple nodes. We also design a dedicated data structure termed N-cache to support parallel access and update of those dictionary representations on GPUs. NAT gets evaluated over seven real-world large-scale temporal networks. NAT not only outperforms all cutting-edge baselines by averaged 1.2% and 4.2% in transductive and inductive link prediction accuracy, respectively, but also keeps scalable by achieving a speed-up of 4.1-76.7x against the baselines that adopt joint structural features and achieves a speed-up of 1.6-4.0x against the baselines that cannot adopt those features. The link to the code: https: //github.com/Graph-COM/Neighborhood-Aware-Temporal-Network.","url_abs":"https://arxiv.org/abs/2209.01084v3","url_pdf":"https://arxiv.org/pdf/2209.01084v3.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":"neighborhood-aware-scalable-temporal-network","repo_url":"https://github.com/graph-com/neighborhood-aware-temporal-network","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"neighborhood-aware-scalable-temporal-network","repo_url":"https://github.com/graph-com/nlb","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"inductive-link-prediction","task_name":"Inductive Link Prediction"},{"task_slug":"link-prediction","task_name":"Link Prediction"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2209.01084","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.01084"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/graph-com/nlb","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/graph-com/neighborhood-aware-temporal-network","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":8},"by_repo_kind":{"official":{"samples":6,"ran":0,"repositories":1},"listed":{"samples":2,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"7878b72c4ebda48a","entry":"get_layer_type","repo":"graph-com/neighborhood-aware-temporal-network","repo_kind":"official","path":"GAT.py","file_url":"https://github.com/graph-com/neighborhood-aware-temporal-network/blob/HEAD/GAT.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"7878b72c4ebda48a"}},{"code_sha256_prefix":"ff96493f152a3c72","entry":"get_layer_type","repo":"graph-com/nlb","repo_kind":"listed","path":"GAT.py","file_url":"https://github.com/graph-com/nlb/blob/HEAD/GAT.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ff96493f152a3c72"}},{"code_sha256_prefix":"8ca7afb49dfbccb7","entry":"get_one_hot","repo":"graph-com/neighborhood-aware-temporal-network","repo_kind":"official","path":"process.py","file_url":"https://github.com/graph-com/neighborhood-aware-temporal-network/blob/HEAD/process.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"8ca7afb49dfbccb7"}},{"code_sha256_prefix":"a79be74d482b75c9","entry":"preprocess","repo":"graph-com/neighborhood-aware-temporal-network","repo_kind":"official","path":"process.py","file_url":"https://github.com/graph-com/neighborhood-aware-temporal-network/blob/HEAD/process.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"a79be74d482b75c9"}},{"code_sha256_prefix":"ece6ee2b33ca01af","entry":"process_sampling_numbers","repo":"graph-com/neighborhood-aware-temporal-network","repo_kind":"official","path":"utils.py","file_url":"https://github.com/graph-com/neighborhood-aware-temporal-network/blob/HEAD/utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ece6ee2b33ca01af"}},{"code_sha256_prefix":"e5a738d195716118","entry":"reindex","repo":"graph-com/neighborhood-aware-temporal-network","repo_kind":"official","path":"process.py","file_url":"https://github.com/graph-com/neighborhood-aware-temporal-network/blob/HEAD/process.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"e5a738d195716118"}},{"code_sha256_prefix":"45261928956e5e41","entry":"set_up_logger","repo":"graph-com/neighborhood-aware-temporal-network","repo_kind":"official","path":"log.py","file_url":"https://github.com/graph-com/neighborhood-aware-temporal-network/blob/HEAD/log.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"45261928956e5e41"}},{"code_sha256_prefix":"c126bd4eb97d7fb7","entry":"set_up_logger","repo":"graph-com/nlb","repo_kind":"listed","path":"log.py","file_url":"https://github.com/graph-com/nlb/blob/HEAD/log.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c126bd4eb97d7fb7"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}