Evidence map›Paper›PMID 34650814›Full record

ArticleNetwork science (Cambridge University Press)2021

Edge Overlap in Weighted and Directed Social Networks.

Heather Mattie, Jukka-Pekka Onnela

Open access · greenAbstract read
In one paragraph

Article in Network science (Cambridge University Press), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 42% of its field
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

1 citing paper in PubMed, 5 citations in OpenAlex.

  1. Article
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

2 authors at 1 institution in 1 country.

Heather MattieHarvard T.H. Chan School of Public Health, Boston, MA 02115.
Jukka-Pekka OnnelaHarvard T.H. Chan School of Public Health, Boston, MA 02115.
Harvard University · US

Funding

Training Program in Psychiatric Genetics and Translational ResearchT32MH017119 · NIMH · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI KARESTAN C KOENEN, Jukka-Pekka Onnela · 1985 to 2026
$8.9M
Bridging Statistical Inference and Mechanistic Network Models for HIV/AIDSR01AI138901 · NIAID · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI Ravi Goyal, Jukka-Pekka Onnela · 2019 to 2026
$3.4M
NIAID NIH HHS R01 AI138901NIMH NIH HHS T32 MH017119
6 · The paper itself

Abstract

With the increasing availability of behavioral data from diverse digital sources, such as social media sites and cell phones, it is now possible to obtain detailed information about the structure, strength, and directionality of social interactions in varied settings. While most metrics of network structure have traditionally been defined for unweighted and undirected networks only, the richness of current network data calls for extending these metrics to weighted and directed networks. One fundamental metric in social networks is edge overlap, the proportion of friends shared by two connected individuals. Here we extend definitions of edge overlap to weighted and directed networks, and present closed-form expressions for the mean and variance of each version for the Erdős-Rényi random graph and its weighted and directed counterparts. We apply these results to social network data collected in rural villages in southern Karnataka, India. We use our analytical results to quantify the extent to which the average overlap of the empirical social network deviates from that of corresponding random graphs and compare the values of overlap across networks. Our novel definitions allow the calculation of edge overlap for more complex networks and our derivations provide a statistically rigorous way for comparing edge overlap across networks.

Identifiers

PMID34650814
PMCPMC8507499
OpenAlexW3133276276

What OpenQuestion holds

Textmetadata
LicenceTDM
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.