Evidence map›Paper›PMID 35174275›Full record

ArticlePeerJ. Computer science2022

Temporal network embedding framework with causal anonymous walks representations.

Ilya Makarov, Andrey Savchenko, Arseny Korovko, Leonid Sherstyuk, Nikita Severin, Dmitrii Kiselev, Aleksandr Mikheev, Dmitrii Babaev

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ. Computer science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Ilya MakarovHSE University, Moscow, Russia.
Andrey SavchenkoLaboratory of Algorithms and Technologies for Network Analysis, HSE University, Nizhny Novgorod, Russia.
Arseny KorovkoHSE University, Moscow, Russia.
Leonid SherstyukHSE University, Moscow, Russia.
Nikita SeverinMoscow Institute of Physics and Technology, Moscow, Russia.
Dmitrii KiselevHSE University, Moscow, Russia.
Aleksandr MikheevHSE University, Moscow, Russia.
Dmitrii BabaevArtificial Intelligence Research Institute (AIRI), Moscow, Russia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many tasks in graph machine learning, such as link prediction and node classification, are typically solved using representation learning. Each node or edge in the network is encoded via an embedding. Though there exists a lot of network embeddings for static graphs, the task becomes much more complicated when the dynamic (

Indexed as

Dynamic networksTemporal graph attentionTemporal network embeddingTemporal networksTemporal random walks

Identifiers

PMID35174275
PMCPMC8802774

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.