node2vec: Scalable Feature Learning for Networks

Aditya Grover, Jure Leskovec · KDD / arXiv · 2016 · network-science · 10.1145/2939672.2939754 · record grover2016-node2vec
tag layer · index · concepts · authors · עברית

Where this record's text came from. faceted 2026-08 by reading the locally held text (pdftext); every evidence string verified as an exact substring of that text before merging. method is coded system-or-tool-building rather than 'simulation' because the deliverable is an algorithm plus released implementation, evaluated on real network datasets; the same record could defensibly carry a benchmark/experiment label, and the empirical evaluation is evidenced by 'We empirically evaluate node2vec for multi-label classification and link prediction on several real-world datasets.' The paper also contains an explicit critique of prior sampling strategies ('This is a major shortcoming of prior work which fail to offer any flexibility in sampling of nodes from a network [24, 28].'), but the contribution shape is a method, so critique was not chosen. Extraction is clean prose except for figure-axis number strings and (cid:NN) glyph artefacts in the equations, none of which appear in the quoted sentences. 2026-08-14 and 2026-08-25 schema-0.3 passes: every quote verified as an exact substring of the staged text and anchored at merge time; concept terms verified present; every edge endpoint an existing node.

How to read this page. The article itself is not reproduced here — this paper is not cleared for redistribution, so what follows is the tag layer alone. The evidence sentences are withheld with the article. The table at the foot lists every tag the record carries; each row names the facet it belongs to, and the whose words column says whether the tag records the paper's own wording or the tagger's sentence about the paper. Every tag links to each other paper in the corpus carrying it. The machine-readable edition of the same thing is the TEI file beside this one, where the tags live in standOff, and the article is at its publisher via the link below. When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote for that row; on this page it is withheld with the article, and the article is at its publisher. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.

Other fields define these differently the same word, defined another way in another discipline
representation 47 fields · sample-size 5 fields · homophily 3 fields · logistic regression 3 fields · graph 2 fields · statistical-significance 2 fields · and 1 more →

Tags on this paper

Every tag the record carries, each linking to every other paper in the corpus that carries it. A tag appears here whether or not its words occur in the article — the spans below are only the ones a sentence in this paper licenses. Of the 8 concept tags on this paper, 5 are grounded in a quoted sentence.

network embedding random walk graph word embedding node classification homophily structural equivalence network neighborhood biased (2nd-order) random-walk sampling of node neighborhoods node2vec network feature embedding neighborhood nodes representation biased Skip-gram model Breadth-first Sampling Depth-first Sampling p-value regression logistic regression sample-size sample size statistical-significance statistically significant system-or-tool-building

Legend

relation to other work	`claim_relations`	how this paper stands to another: agrees, contradicts, extends, qualifies
claim type	`claim_type`	the shape of the claim: existence, mechanism, method, critique, prediction, null
concept edge	`concept_edges`	an asserted relation between two concepts, with the sentence asserting it
concept	`concepts`	an idea the paper works with, quoted where the paper uses it
content tag	`content_tags`	a finding, definition, quantity, statistic or variable named in the paper
control	`controls`	an experiment run to eliminate a rival explanation
limitation	`limitations`	what the paper says its evidence cannot support
mechanism	`mechanisms`	how the reported effect is said to come about
method	`method`	how the paper produced its evidence
phenomenon	`phenomenon`	the effect or regularity under study
sense	`senses`	a distinct use of a term, quoted where the paper fixes it
transfer	`transfers`	a method or concept carried in from another field

The paper

The full text is not reproduced here. This article is not cleared for redistribution, so the page carries the tag layer only. Its evidence sentences are withheld; the grounding status remains with each tag, and the article itself is at https://doi.org/10.1145/2939672.2939754. The reading view exists for the papers whose licence permits it.

Every tag on this page

claim_relations 4 · claim_type 1 · concept_edges 2 · concepts 5 · content_tags 9 · controls 1 · limitations 6 · mechanisms 1 · method 1 · phenomenon 1 · senses 3 · transfers 1

Relations on this page carry a quotation for the claim. Where a relation was separately evidenced — and so tested against the vocabulary for its type — the row says so; none of 2 relations here carry that mark. The rest are the tagger’s reading of the sentence quoted beside them.

facet	tag	field	conf.	whose words
claim_type	method Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`claim_type`		the paper's words
phenomenon	network feature learning (node embeddings) Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`phenomenon`		the paper's words
method	system-or-tool-building Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: system-or-tool-building	`method`		the paper's words
mechanisms	mechanisms Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: biased (2nd-order) random-walk sampling of node neighborhoods	`mechanisms[0]`		the tagger's words
limitations	Reusing walk samples across source nodes trades statistical purity for efficiency and can bias the procedure. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: every sampled walk and the estimates built on it: SAMPLE REUSE CAN INTRODUCE SOME BIAS IN THE OVERALL PROCEDURE	`limitations[0]`		the tagger's words
limitations	When the search phase does not matter for a given network, node2vec collapses to the DeepWalk baseline and offers no gain. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: the claim that the search strategy contributes: IN THE WORST CASE, THE SEARCH PHASE HAS LITTLE BEARING ON LEARNED REPRESENTATIONS IN WHICH CASE NODE2VEC IS EQUIVALENT TO DEEPWALK	`limitations[1]`		the tagger's words
limitations	The paper does not explain why the Hadamard edge operator works best, nor give equivalence notions for edges. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: the recommendation of the Hadamard operator: WE WOULD LIKE TO EXPLORE THE REASONS BEHIND THE SUCCESS OF HADAMARD OPERATOR OVER OTHERS - it performs best and the paper cannot say why	`limitations[2]`		the tagger's words
limitations	The likelihood is factorised by assuming each neighbourhood node is observed independently of the others given the source, and the paper gives tractability as the reason. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: the learned embeddings as a model of a network neighbourhood: the likelihood is factorised by assuming each neighbourhood node is observed independently of every other one given the source, and the paper adopts this to make the optimisation tractable	`limitations[3]`	1.0	the tagger's words
limitations	The construct borrowed from language does not carry over, and the paper says why: text is linear and networks are not, so the neighbourhood had to be redefined. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: node2vec read as word2vec applied to graphs: the neighbourhood in language is a sliding window over consecutive words, which the paper states does not transfer because networks are not linear, so the neighbourhood is redefined as a sampled set	`limitations[4]`	1.0	the tagger's words
limitations	Prior methods are criticised for assuming a particular relationship between network structure and the prediction task, with spectral clustering named as an example. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: a comparison against spectral clustering or similar baselines: those methods assume a specific relationship between network structure and the task - graph cuts being useful for classification - which the paper calls reasonable in many scenarios but unsatisfactory for generalising across diverse networks	`limitations[5]`	1.0	the tagger's words
transfers	the Skip-gram word-embedding architecture (word2vec), applied to graph nodes Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`transfers[0]`		the tagger's words
controls	node2vec measured against state-of-the-art feature-learning algorithms on the same multi-label classification and link-prediction tasks Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. eliminates: an improvement that is really a difference in task, data or metric - the named prior methods are run on the same problems · outcome (stated by the tagger, no quotation): held	`controls[0]`	0.85	the tagger's words
concepts	random walk Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: random walk	`concepts[1]`		the paper's words
concepts	node classification Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: node classification	`concepts[4]`		the paper's words
concepts	homophily Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: homophily	`concepts[5]`		the paper's words
concepts	structural equivalence Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: structural equivalence	`concepts[6]`		the paper's words
concepts	network neighborhood Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: network neighborhood	`concepts[7]`		the paper's words
senses	node similarity - AS HOMOPHILY: two nodes count as alike when they sit in the same tightly knit community, which is one of the two organising principles the paper says networks mix Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`senses[0]`	1.0	the tagger's words
senses	node similarity - AS STRUCTURAL EQUIVALENCE: two nodes count as alike when they play the same role, EVEN IN DIFFERENT COMMUNITIES - the paper's own figure has u alike to s1 under the first sense and alike to s6 under this one, so the two senses pick out different pairs Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`senses[1]`	1.0	the tagger's words
senses	network neighborhood - AS A SAMPLED SET, NOT A RADIUS: not restricted to immediate neighbours, and its shape changes with the sampling strategy, so the neighbourhood is a design choice rather than a property of the graph Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`senses[2]`	1.0	the tagger's words
claim_relations	builds-on → the Skip-gram (word2vec) model from NLP Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`claim_relations[0]`		the tagger's words
claim_relations	builds-on → the Skip-gram model of Mikolov et al. [21] (and its network use in [24]) Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`claim_relations[1]`		the tagger's words
claim_relations	extends → DeepWalk [24] Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`claim_relations[2]`		the tagger's words
claim_relations	extends → DeepWalk [24] and LINE [28] (rigid sampling strategies) Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`claim_relations[3]`		the tagger's words
concept_edges	homophily —contrasted-with→ structural equivalence Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. strength of the relation: 1 also in: homophily · structural equivalence	`concept_edges[0]`		the tagger's words
concept_edges	network neighborhood —operationalised-as→ random walk Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. strength of the relation: 1 also in: network neighborhood · random walk	`concept_edges[1]`		the tagger's words
content_tags	node2vec maps nodes into a low-dimensional feature space chosen to keep their network neighborhoods as likely as possible, sampled with a biased random walk. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`content_tags.findings[0]`		the tagger's words
content_tags	node2vec beats prior state-of-the-art techniques on multi-label classification and link prediction across several real-world networks. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. about this quote: QUOTE REPAIRED: the extractor split a hyphen-space and the quote was recorded faithfully with the damage in it, which made it unanchorable. The repaired string is an EXACT SUBSTRING of the staged text - that is why it was accepted, not because a rule produced it. The text as originally recorded is kept in evidence as extracted.	`content_tags.findings[1]`		the tagger's words
content_tags	Skip-gram model Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. about this quote: QUOTE REPAIRED: the extractor split a hyphen-space and the quote was recorded faithfully with the damage in it, which made it unanchorable. The repaired string is an EXACT SUBSTRING of the staged text - that is why it was accepted, not because a rule produced it. The text as originally recorded is kept in evidence as extracted. also in: Skip-gram model	`content_tags.definitions[0]`		the tagger's words
content_tags	Breadth-first Sampling Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: Breadth-first Sampling	`content_tags.definitions[2]`		the tagger's words
content_tags	Depth-first Sampling Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: Depth-first Sampling	`content_tags.definitions[3]`		the tagger's words
content_tags	p-value Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. about this quote: QUOTE REPAIRED: the extractor split a hyphen-space and the quote was recorded faithfully with the damage in it, which made it unanchorable. The repaired string is an EXACT SUBSTRING of the staged text - that is why it was accepted, not because a rule produced it. The text as originally recorded is kept in evidence as extracted. also in: p-value	`content_tags.statistics[0]`		the tagger's words
content_tags	regression Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: regression	`content_tags.statistics[1]`		the tagger's words
content_tags	sample-size Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: sample-size	`content_tags.statistics[2]`		the tagger's words
content_tags	statistical-significance Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. about this quote: QUOTE REPAIRED: the extractor split a hyphen-space and the quote was recorded faithfully with the damage in it, which made it unanchorable. The repaired string is an EXACT SUBSTRING of the staged text - that is why it was accepted, not because a rule produced it. The text as originally recorded is kept in evidence as extracted. also in: statistical-significance	`content_tags.statistics[3]`		the tagger's words

Interpretive choices

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