Methods › Graphs › Graph Embeddings › node2vec

node2vec

104 papers tagged archive 2025-07-28

Introduced by Aditya Grover et al. in node2vec: Scalable Feature Learning for Networks

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

node2vec is a framework for learning graph embeddings for nodes in graphs. Node2vec maximizes a likelihood objective over mappings which preserve neighbourhood distances in higher dimensional spaces. From an algorithm design perspective, node2vec exploits the freedom to define neighbourhoods for nodes and provide an explanation for the effect of the choice of neighborhood on the learned representations.

For each node, node2vec simulates biased random walks based on an efficient network-aware search strategy and the nodes appearing in the random walk define neighbourhoods. The search strategy accounts for the relative influence nodes exert in a network. It also generalizes prior work alluding to naive search strategies by providing flexibility in exploring neighborhoods.

PaperSource

Papers archive 2025-07-28

30 shown of 104, 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

20 shown of 86 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
Link Prediction26
Representation Learning26
Graph Embedding19
Network Embedding19
Node Classification17
Community Detection8
Graph Representation Learning8
BIG-bench Machine Learning7
Clustering7
Recommendation Systems7
General Classification6
Prediction6
Knowledge Graphs4
Word Embeddings4
Anomaly Detection3
Attribute3
Decoder3
Graph Learning3
Graph Neural Network3
Self-Supervised Learning3

Usage over time archive 2025-07-28

Papers per year tagged with node2vec: 2016 to 2025, peak 19 19 0 2016: 1 paper 2016 2017: 4 papers 2017 2018: 6 papers 2018 2019: 17 papers 2019 2020: 19 papers 2020 2021: 18 papers 2021 2022: 9 papers 2022 2023: 14 papers 2023 2024: 12 papers 2024 2025: 4 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (104 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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