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Identifying Transition States of Chemical Kinetic Systems using Network Embedding Techniques

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Paula Mercurio, Di Liu

Numerical Analysis · Submitted 29 Oct 2020

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We then demonstrate the effectiveness of the method on dimension reduction through several examples regarding identification of transition states of chemical reactions, especially for entropic systems.

how it is stated · asserted 1.00 read the record →

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arXiv ID2010.15760 (version 1)
Pith ID24R4URUG, Pith's permanent record of this paper and its review
CitationDi Liu, Paula Mercurio. "Identifying Transition States of Chemical Kinetic Systems using Network Embedding Techniques." arXiv:2010.15760 (2020). https://pith.science/paper/24R4URUG
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,576 input tokens.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.07 · human participants 0.02 · controlled comparison 0.05 · randomized assignment 0.02 · states quantitative result 0.05 · compares with prior results 0.13 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Using random walk sampling methods for feature learning on networks, we develop a method for generating low-dimensional node embeddings for directed graphs and identifying transition states of stochastic chemical reacting systems. We modified objective functions adopted in existing random walk based network embedding methods to handle directed graphs and neighbors of different degrees. Through optimization via gradient ascent, we embed the weighted graph vertices into a low-dimensional vector space Rd while preserving the neighborhood of each node. We then demonstrate the effectiveness of the method on dimension reduction through several examples regarding identification of transition states of chemical reactions, especially for entropic systems.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Using random walk sampling methods for feature learning on networks, we develop a method for generating low-dimensional node embeddings for directed graphs and identifying transition states of stochastic chemical reacting systems. contribution 0.98 0.93 0.29 asserted 1.00
s1 We modified objective functions adopted in existing random walk based network embedding methods to handle directed graphs and neighbors of different degrees. method 0.71 0.93 0.04 asserted 1.00
s2 Through optimization via gradient ascent, we embed the weighted graph vertices into a low-dimensional vector space Rd while preserving the neighborhood of each node. method 1.00 0.94 0.08 asserted 1.00
s3 We then demonstrate the effectiveness of the method on dimension reduction through several examples regarding identification of transition states of chemical reactions, especially for entropic systems. result 0.86 0.94 0.66 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 1.00.

What was examined, and what was not

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  • full text
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  • references and citing sentences
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  • S sections
  • T body sentences
  • C citations
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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Using random walk sampling methods for feature learning on networks, we develop a method for generating low-dimensional node embeddings for directed graphs and identifying transition states of stochastic chemical reacting systems. We modified objective functions adopted in existing random walk based network embedding methods to handle directed graphs and neighbors of different degrees. Through optimization via gradient ascent, we embed the weighted graph vertices into a low-dimensional vector space Rd while preserving the neighborhood of each node. We then demonstrate the effectiveness of the method on dimension reduction through several examples regarding identification of transition states of chemical reactions, especially for entropic systems.

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