ArticleNature communications2025
Probabilistic alignment of multiple networks.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- Connectomes: a re-emerging hub dataset for neuroscience.Frontiers in neuroscience · 2026Review
- Probabilistic alignment of multiple networks.Nature communications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The network alignment problem appears in many areas of science and involves finding the optimal mapping between nodes in two or more networks, so as to identify corresponding entities across networks. We propose a probabilistic approach to the problem of network alignment, as well as the corresponding inference algorithms. Unlike heuristic approaches, our approach is transparent in that all model assumptions are explicit; therefore, it is susceptible of being extended and fine tuned by incorporating contextual information that is relevant to a given alignment problem. Also in contrast to current approaches, our method does not yield a single alignment, but rather the whole posterior distribution over alignments. We show that using the whole posterior leads to correct matching of nodes, even in situations where the single most plausible alignment mismatches them. Our approach opens the door to a whole new family of network alignment algorithms, and to their application to problems for which existing methods are perhaps inappropriate.
Identifiers
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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.