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Endorsement Deduction and Ranking in Social Networks

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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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Hebert Pérez-Rosés, Francesc Sebé, Josep Maria Ribó

Social and Information Networks · Submitted 7 Oct 2015

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

The authors' main claim In this paper we propose an authority score computation method that takes into account the relations existing among different skills.

how it is stated · asserted 0.99 read the record →

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arXiv ID1510.01997 (version 1)
Pith IDOFPKN4BQ, Pith's permanent record of this paper and its review
CitationFrancesc Seb\'e, Hebert P\'erez-Ros\'es, Josep Maria Rib\'o. "Endorsement Deduction and Ranking in Social Networks." arXiv:1510.01997 (2015). https://pith.science/paper/OFPKN4BQ
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,841 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.10 · human participants 0.14 · controlled comparison 0.04 · randomized assignment 0.01 · states quantitative result 0.50 · compares with prior results 0.08 · mentions code or data availability 0.03.

The abstract, sentence by sentence

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

Some social networks, such as LinkedIn and ResearchGate, allow user endorsements for specific skills. In this way, for each skill we get a directed graph where the nodes correspond to users' profiles and the arcs represent endorsement relations. From the number and quality of the endorsements received, an authority score can be assigned to each profile. In this paper we propose an authority score computation method that takes into account the relations existing among different skills. Our method is based on enriching the information contained in the digraph of endorsements corresponding to a specific skill, and then applying a ranking method admitting weighted digraphs, such as PageRank. We describe the method, and test it on a synthetic network of 1493 nodes, fitted with endorsements.

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 Some social networks, such as LinkedIn and ResearchGate, allow user endorsements for specific skills. background 1.00 0.04 0.02 asserted 0.98
s1 In this way, for each skill we get a directed graph where the nodes correspond to users' profiles and the arcs represent endorsement relations. background 0.90 0.16 0.02 asserted 1.00
s2 From the number and quality of the endorsements received, an authority score can be assigned to each profile. background 1.00 0.09 0.06 asserted 0.83
s3 In this paper we propose an authority score computation method that takes into account the relations existing among different skills. contribution 1.00 0.83 0.25 asserted 0.99
s4 Our method is based on enriching the information contained in the digraph of endorsements corresponding to a specific skill, and then applying a ranking method admitting weighted digraphs, such as PageRank. method 0.97 0.76 0.14 asserted 1.00
s5 We describe the method, and test it on a synthetic network of 1493 nodes, fitted with endorsements. method 0.81 0.96 0.05 asserted 0.98

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.99.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

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

Some social networks, such as LinkedIn and ResearchGate, allow user endorsements for specific skills. In this way, for each skill we get a directed graph where the nodes correspond to users' profiles and the arcs represent endorsement relations. From the number and quality of the endorsements received, an authority score can be assigned to each profile. In this paper we propose an authority score computation method that takes into account the relations existing among different skills. Our method is based on enriching the information contained in the digraph of endorsements corresponding to a specific skill, and then applying a ranking method admitting weighted digraphs, such as PageRank. We describe the method, and test it on a synthetic network of 1493 nodes, fitted with endorsements.

Human review

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How the literature uses it

What this paper does with the arXiv papers it cites

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

What it cites

Cites as background 1 paper
  • Vigna [ref] traces the origins of spectral ranking to the 1950's, with [ref] and [ref] .

    Spectral Ranking · in context · 0.94

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