Methods › Graphs › Graph Embeddings › LINE

Large-scale Information Network Embedding

LINE

9 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

LINE is a novel network embedding method which is suitable for arbitrary types of information networks: undirected, directed, and/or weighted. The method optimizes a carefully designed objective function that preserves both the local and global network structures.

Source: Tang et al.

Image source: Tang et al.

Source: LINE: Large-scale Information Network Embedding

Papers archive 2025-07-28

9 shown of 9, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

14 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Graph Embedding5
Link Prediction3
Network Embedding3
Node Classification2
Chatbot1
Community Detection1
Decision Making1
GPU1
Graph Neural Network1
Graph Sampling1
Privacy Preserving1
Recommendation Systems1
Representation Learning1
Self-Supervised Learning1

Usage over time archive 2025-07-28

Papers per year tagged with LINE: 2015 to 2024, peak 3 3 0 2015: 1 paper 2015 2016: 0 papers 2016 2017: 0 papers 2017 2018: 0 papers 2018 2019: 0 papers 2019 2020: 0 papers 2020 2021: 2 papers 2021 2022: 1 paper 2022 2023: 2 papers 2023 2024: 3 papers 2024
Papers per year the archive tags with this method, by the paper's archive date (9 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Graph Embeddings

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