DANI: fast diffusion aware network inference with preserving topological structure property Article Swipe
Maryam Ramezani
,
Aryan Ahadinia
,
Erfan Farhadi
,
Hamid R. Rabiee
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-024-82286-x
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-024-82286-x
Numerous algorithms have been proposed to infer the underlying structure of the social networks via observed information propagation. The previously proposed algorithms concentrate on inferring accurate links and neglect preserving the essential topological properties of the underlying social networks. In this paper, we propose a novel method called DANI to infer the underlying network while preserving its structural properties. DANI is constructed using the Markov transition matrix, which is derived from the analysis of time series cascades and the observation of node-node similarity in cascade behavior from a structural perspective. The presented method has linear time complexity. This means that it increases with the number of nodes, cascades, and the square of the average length of cascades. Moreover, its distributed version in the MapReduce framework is scalable. We applied the proposed approach to both real and synthetic networks. The experimental results indicated DANI exhibits higher accuracy and lower run time compared to well-known network inference methods. Furthermore, DANI preserves essential structural properties such as modular structure, degree distribution, connected components, density, and clustering coefficients. Our source code is available on GitHub ( https://github.com/AryanAhadinia/DANI ).
Related Topics
Concepts
Computer science
Node (physics)
Inference
Scalability
Cluster analysis
Markov chain
Clustering coefficient
Complex network
Theoretical computer science
Algorithm
Topology (electrical circuits)
Data mining
Artificial intelligence
Mathematics
Machine learning
Engineering
Combinatorics
Structural engineering
World Wide Web
Database
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-024-82286-x
- https://www.nature.com/articles/s41598-024-82286-x.pdf
- OA Status
- gold
- Cited By
- 2
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405843671
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405843671Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1038/s41598-024-82286-xDigital Object Identifier
- Title
-
DANI: fast diffusion aware network inference with preserving topological structure propertyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-28Full publication date if available
- Authors
-
Maryam Ramezani, Aryan Ahadinia, Erfan Farhadi, Hamid R. RabieeList of authors in order
- Landing page
-
https://doi.org/10.1038/s41598-024-82286-xPublisher landing page
- PDF URL
-
https://www.nature.com/articles/s41598-024-82286-x.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.nature.com/articles/s41598-024-82286-x.pdfDirect OA link when available
- Concepts
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Computer science, Node (physics), Inference, Scalability, Cluster analysis, Markov chain, Clustering coefficient, Complex network, Theoretical computer science, Algorithm, Topology (electrical circuits), Data mining, Artificial intelligence, Mathematics, Machine learning, Engineering, Combinatorics, Structural engineering, World Wide Web, DatabaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2Per-year citation counts (last 5 years)
- References (count)
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43Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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