Claim · 2016-09-09 · publication · Stated in source · Supported

The paper presents an approach to semi-supervised learning on graph-structured data based on a variant of convolutional neural networks that operate directly on graphs.

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What it connects

Semi-Supervised Classification with Graph Convolutional Networks → states its contribution → The paper presents an approach to semi-supervised learning on graph-structured data based on a variant of convolutional neural networks that operate directly on graphs.

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Claim and evidence

Semi-Supervised Classification with Graph Convolutional Networksstates its contribution ↓The paper presents an approach to semi-supervised learning on graph-structured data based on a variant of convolutional neural networks that operate directly on graphs.

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Date: 2016-09-09 · publication Records dated 2016 →

Scope and limitsThe paper's own description of its contribution; not an assessment of it.

The evidence 01

primary sourceSupports the claim

Semi-Supervised Classification with Graph Convolutional Networks

arXiv · 2016-09-09

Cite the original source

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Source excerpt

“We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs.”

Location in source arXiv v1, PDF p. 1, abstract

Machine-checked · 2026-10-31

Source details and check notes

Authors: Thomas N. Kipf; Max Welling
Version: arXiv:1609.02907v1, submitted 9 September 2016 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 f5915f5b72296512e82531186500a9022761198816b5c5fd60eecf7084d178f5). The author list is the byline as printed on the first page.
Version date: 2016-09-09
Canonical address: Open the canonical source ↗
Accessed: 2026-10-28
Language: English (en)
Attribution: Thomas N. Kipf, Max Welling, “Semi-Supervised Classification with Graph Convolutional Networks”, 2016-09-09.
Reuse: No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution.
Check notes: Candidate pack passage, key papers (research/key-papers). The quotation (27 words) matched the text retrieved for the candidate pack on 2026-10-28 (research/key-papers/verification.json). Matching ignored differences of whitespace, case, quotation marks, dashes and line-break hyphens, so the source’s typography can differ. No human editorial review.

Status and meaning

Editorial status. Not reviewed by an editor. An exploratory record that no editor has accepted. This status does not say whether a person has read any linked passage. Each evidence record above shows how it was checked.

Relation. A paper or other publication describes its own contribution, quoted from the expression the record names, usually its abstract or introduction. The record is the document’s own description, not an assessment: a claim of priority or novelty it contains is the document’s claim, flagged as such, and says nothing about earlier or later work.

Date. Date of the arXiv version quoted, submitted 9 September 2016 (UTC); later versions may word it differently.

Record ID kp-states-semi-supervised-classification-graph-convolutional-2016-1