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Fix position of table describing algorithms
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‎publication/jats/paper.jats‎

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@@ -121,9 +121,9 @@ a Creative Commons Attribution 4.0 International License (CC BY
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</sec>
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<sec id="statement-of-need">
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<title>Statement of need</title>
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<p>Network analysis is central to social sciences, biology, and fraud
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detection, where understanding relationships is essential.
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Probabilistic generative models
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<p>Network analysis is central to disciplines such as social sciences,
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biology, and fraud detection, where understanding relationships is
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essential. Probabilistic generative models
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(<xref alt="Contisciani et al., 2020" rid="ref-contisciani2020community" ref-type="bibr">Contisciani
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et al., 2020</xref>,
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<xref alt="2022" rid="ref-contisciani2022community" ref-type="bibr">2022</xref>;
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<list list-type="bullet">
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<list-item>
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<p><bold>Diverse Network Models</bold>: Integration of generative
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models for various network types and goals:</p>
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models for various network types and goals (see table below).</p>
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</list-item>
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<list-item>
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<p><bold>Synthetic Network Generation</bold>: Ability to generate
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synthetic networks that closely resemble real ones for further
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analyses (e.g., testing hypotheses).</p>
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</list-item>
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<list-item>
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<p><bold>Simplified Parameter Selection</bold>: A cross-validation
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module to optimize key parameters, providing performance results
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in a clear dataframe.</p>
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</list-item>
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<list-item>
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<p><bold>Rich Set of Metrics for Analysis</bold>: Advanced metrics
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(e.g., F1 scores, Jaccard index) for link and covariate prediction
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performance.</p>
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</list-item>
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<list-item>
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<p><bold>Powerful Visualization Tools</bold>: Functions for
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plotting community memberships and performance metrics.</p>
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</list-item>
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<list-item>
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<p><bold>User-Friendly Command-Line Interface</bold>: An intuitive
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interface for easy access.</p>
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</list-item>
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<list-item>
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<p><bold>Extensible and Modular Codebase</bold>: Future
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integration of additional models possible.</p>
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</list-item>
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</list>
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<table-wrap>
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</tbody>
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</table>
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</table-wrap>
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<list list-type="bullet">
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<list-item>
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<p><bold>Synthetic Network Generation</bold>: Ability to generate
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synthetic networks that closely resemble real ones for further
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analyses (e.g., testing hypotheses).</p>
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</list-item>
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<list-item>
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<p><bold>Simplified Parameter Selection</bold>: A cross-validation
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module to optimize key parameters, providing performance results
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in a clear dataframe.</p>
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</list-item>
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<list-item>
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<p><bold>Rich Set of Metrics for Analysis</bold>: Advanced metrics
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(e.g., F1 scores, Jaccard index) for link and covariate prediction
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performance.</p>
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</list-item>
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<list-item>
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<p><bold>Powerful Visualization Tools</bold>: Functions for
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plotting community memberships and performance metrics.</p>
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</list-item>
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<list-item>
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<p><bold>User-Friendly Command-Line Interface</bold>: An intuitive
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interface for easy access.</p>
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</list-item>
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<list-item>
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<p><bold>Extensible and Modular Codebase</bold>: Future
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integration of additional models possible.</p>
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</list-item>
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</list>
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<p>The <bold>Usage</bold> section below illustrates these features
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with a real-world example.</p>
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</sec>

‎publication/paper.md‎

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@@ -43,8 +43,7 @@ to analyze and model complex network data. The package integrates code implement
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# Statement of need
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Network analysis is central to social sciences, biology, and fraud detection, where
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understanding relationships is essential. Probabilistic generative models
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Network analysis is central to disciplines such as social sciences, biology, and fraud detection, where understanding relationships is essential. Probabilistic generative models
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[@safdari2021generative; @contisciani2022community; @safdari2022anomaly; @safdari2022reciprocity; @contisciani2020community
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] reveal hidden patterns, detect communities, identify anomalies, and generate synthetic data. Their broader use is limited by fragmented implementations that hinder comparisons and reproducibility.
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ProbINet addresses this gap by unifying recent approaches in a single framework, improving accessibility and usability across disciplines.
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generative models. Key features include:
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- **Diverse Network Models**: Integration of generative models for various network types
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and goals:
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| **Algorithm's Name**&nbsp; | **Description** | **Network Properties** |
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|----------------------------|-------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------|
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| **CRep** | Models directed networks with communities and reciprocity [@safdari2021generative]. | Directed, Weighted, Communities, Reciprocity |
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| | | |
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| **JointCRep** | Captures community structure and reciprocity with a joint edge distribution [@contisciani2022community]. | Directed, Communities, Reciprocity |
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| | | |
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| **DynCRep** | Extends CRep for dynamic networks [@safdari2022reciprocity]. | Directed, Weighted, Dynamic, Communities, Reciprocity |
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| | | |
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| **ACD** | Identifies anomalous edges and node community memberships in weighted networks [@safdari2022anomaly]. | Directed, Weighted, Communities, Anomalies |
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| | | |
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| **MTCOV** | Extracts overlapping communities in multilayer networks using topology and node attributes [@contisciani2020community]. | Weighted, Multilayer, Attributes, Communities |
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and goals (see table below).
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- **Synthetic Network Generation**: Ability to generate synthetic networks that closely resemble real ones for further analyses (e.g., testing hypotheses).
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- **Extensible and Modular Codebase**: Future integration of additional models possible.
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| **Algorithm's Name**&nbsp; | **Description** | **Network Properties** |
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|----------------------------|-------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------|
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| **CRep** | Models directed networks with communities and reciprocity [@safdari2021generative]. | Directed, Weighted, Communities, Reciprocity |
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| | | |
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| **JointCRep** | Captures community structure and reciprocity with a joint edge distribution [@contisciani2022community]. | Directed, Communities, Reciprocity |
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| | | |
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| **DynCRep** | Extends CRep for dynamic networks [@safdari2022reciprocity]. | Directed, Weighted, Dynamic, Communities, Reciprocity |
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| | | |
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| **ACD** | Identifies anomalous edges and node community memberships in weighted networks [@safdari2022anomaly]. | Directed, Weighted, Communities, Anomalies |
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| | | |
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| **MTCOV** | Extracts overlapping communities in multilayer networks using topology and node attributes [@contisciani2020community]. | Weighted, Multilayer, Attributes, Communities |
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The **Usage** section below illustrates these features with a real-world example.
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# Usage

‎publication/paper.pdf‎

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