ArticleScientific reports2013
Reciprocity of weighted networks.
Article in Scientific reports, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
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- Autism shapes social integration and reciprocity in elementary classrooms.Scientific reports · 2025Article
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- Extending the social cohesion hypothesis: is group social structure associated with dispersal in yellow-bellied marmots (Current zoology · 2024Article
- Compression-based inference of network motif sets.PLoS computational biology · 2024Article
- Inferring Dealer Networks in the Foreign Exchange Market Using Conditional Transfer Entropy: Analysis of a Central Bank Announcement.Entropy (Basel, Switzerland) · 2024Article
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- Stingy bots can improve human welfare in experimental sharing networks.Scientific reports · 2023Article
- Evolvement patterns of usage in a medium-sized bike-sharing system during the COVID-19 pandemic.Sustainable cities and society · 2023Article
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- Emergent social structure is typically not associated with survival in a facultatively social mammal.Biology letters · 2023Article
- Dynamic structure of motor cortical neuron coactivity carries behaviorally relevant information.Network neuroscience (Cambridge, Mass.) · 2023Article
- The Fitness-Corrected Block Model, or how to create maximum-entropy data-driven spatial social networks.Scientific reports · 2022Article
- Bow-tie structures of twitter discursive communities.Scientific reports · 2022Article
- Complete Connectomic Reconstruction of Olfactory Projection Neurons in the Fly Brain.Current biology : CB · 2020Article
- Article
- Average trapping time on weighted directed Koch network.Scientific reports · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In directed networks, reciprocal links have dramatic effects on dynamical processes, network growth, and higher-order structures such as motifs and communities. While the reciprocity of binary networks has been extensively studied, that of weighted networks is still poorly understood, implying an ever-increasing gap between the availability of weighted network data and our understanding of their dyadic properties. Here we introduce a general approach to the reciprocity of weighted networks, and define quantities and null models that consistently capture empirical reciprocity patterns at different structural levels. We show that, counter-intuitively, previous reciprocity measures based on the similarity of mutual weights are uninformative. By contrast, our measures allow to consistently classify different weighted networks according to their reciprocity, track the evolution of a network's reciprocity over time, identify patterns at the level of dyads and vertices, and distinguish the effects of flux (im)balances or other (a)symmetries from a true tendency towards (anti-)reciprocation.
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Registered trials
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.