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NetworkX VS Soft UI Dashboard

Compare NetworkX VS Soft UI Dashboard and see what are their differences

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NetworkX logo NetworkX

NetworkX is a Python language software package for the creation, manipulation, and study of the...

Soft UI Dashboard logo Soft UI Dashboard

Admin dashboard template for Bootstrap 5
  • NetworkX Landing page
    Landing page //
    2023-09-14
  • Soft UI Dashboard Landing page
    Landing page //
    2023-10-22

NetworkX features and specs

  • Ease of Use
    NetworkX provides a simple and intuitive API that makes it easy for both novices and experienced users to create, manipulate, and study the structure and dynamics of complex networks.
  • Comprehensive Documentation
    The library is well-documented with a vast number of examples and tutorials, aiding users in understanding and applying the features effectively.
  • Rich Functionality
    NetworkX offers numerous built-in functions to analyze network properties, perform algorithms like shortest path and clustering, and handle various graph types such as directed, undirected, and multigraphs.
  • Integration with Python Ecosystem
    Being a Python library, NetworkX integrates seamlessly with other scientific computing libraries like NumPy, SciPy, and Matplotlib, allowing for extensive data analysis and visualization.
  • Active Community
    NetworkX's active community of users and developers means continuous improvements and updates, as well as a wealth of shared knowledge and code to draw upon.

Possible disadvantages of NetworkX

  • Performance Limitations
    NetworkX may suffer from performance issues with extremely large graphs due to its in-memory data storage and Python's inherent single-threaded execution, making it less suitable for handling very large-scale networks.
  • Lack of Parallel Processing
    NetworkX does not natively support parallel processing within its operations, which can be a drawback when working with complex computations or very large graphs.
  • Memory Consumption
    Graphs and network data structures in NetworkX may consume a substantial amount of memory, especially with large datasets, potentially leading to inefficiencies.
  • Visualization Limitations
    While NetworkX provides basic plotting capabilities, for more advanced and interactive visualizations, additional libraries like Matplotlib or Plotly might be needed.
  • Scalability Constraints
    The library is not designed to work efficiently with very large networks compared to other frameworks specialized for scalability, such as Graph-tool or igraph.

Soft UI Dashboard features and specs

  • Aesthetic Design
    Soft UI Dashboard offers a modern and appealing visual design that resembles soft materials, providing an attractive interface for users.
  • Customization
    It provides easy customization options, allowing developers to tailor components and elements to match their project requirements.
  • Responsive Layout
    The dashboard ensures a responsive layout, making it accessible and visually consistent across different devices and screen sizes.
  • Comprehensive Documentation
    Creative Tim provides thorough documentation and tutorials, which makes it easier for developers to integrate and utilize the components.
  • Rich UI Components
    The dashboard includes a variety of UI components like cards, forms, and charts, enabling the creation of feature-rich user interfaces.

Possible disadvantages of Soft UI Dashboard

  • Learning Curve
    Users unfamiliar with the Soft UI design approach or Creative Tim products might face a learning curve in effectively leveraging the dashboard.
  • Limited Free Features
    While the basic version is free, more advanced features and additional components require purchasing the PRO version, which might be a drawback for developers on a budget.
  • Dependency Management
    Developers need to manage dependencies carefully, as issues may arise from conflicting libraries or outdated packages used in the dashboard.
  • Performance Overhead
    The inclusion of numerous components and design features can lead to performance overhead, particularly if not optimized for large-scale applications.
  • Browser Compatibility
    While generally compatible with modern browsers, specific design elements might not render perfectly on older browser versions, requiring extra testing and possible adjustments.

NetworkX videos

+ Add

Directed Network Analysis - Simulating a Social Network Using Networkx in Python - Tutorial 28

Soft UI Dashboard videos

+ Add

Soft UI Dashboard - Open-Source Flask Dashboard | AppSeed

More videos:

  • Review - Create Dashboard Pages in Minutes - Soft UI Dashboard Builder | Low Code Dashboard Builder

Category Popularity

0-100% (relative to NetworkX and Soft UI Dashboard)

NetworkX

Soft UI Dashboard

Graph Databases
100 100%
Graph Databases
0% 0
Developer Tools
0 0%
Developer Tools
100% 100
Databases
100 100%
Databases
0% 0
Design Tools
0 0%
Design Tools
100% 100

User comments

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Social recommendations and mentions

Based on our record, NetworkX seems to be more popular. It has been mentiond 35 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

NetworkX mentions (35)

  • Representing Graphs in PostgreSQL
    If you are interested in the subject, also take a look at NetworkDisk[1] which enable users of NetworkX[2] which maps graphs to databases. [1] https://networkdisk.inria.fr/ [2] https://networkx.org/. - Source: Hacker News / 3 months ago
  • Build the dependency graph of your BigQuery pipelines at no cost: a Python implementation
    In the project we used Python lib networkx and a DiGraph object (Direct Graph). To detect a table reference in a Query, we use sqlglot, a SQL parser (among other things) that works well with Bigquery. - Source: dev.to / over 1 year ago
  • Custom libraries and utility tools for challenges
    If you program in Python, can use NetworkX for that. But it's probably a good idea to implement the basic algorithms yourself at least one time. Source: over 1 year ago
  • Google open-sources their graph mining library
    For those wanting to play with graphs and ML I was browsing the arangodb docs recently and I saw that it includes integrations to various graph libraries and machine learning frameworks [1]. I also saw a few jupyter notebooks dealing with machine learning from graphs [2]. Integrations include: * NetworkX -- https://networkx.org/ * DeepGraphLibrary -- https://www.dgl.ai/ * cuGraph (Rapids.ai Graph) --... - Source: Hacker News / over 1 year ago
  • org-roam-pygraph: Build a graph of your org-roam collection for use in Python
    Org-roam-ui is a great interactive visualization tool, but its main use is visualization. The hope of this library is that it could be part of a larger graph analysis pipeline. The demo provides an example graph visualization, but what you choose to do with the resulting graph certainly isn't limited to that. See for example networkx. Source: about 2 years ago
View more

Soft UI Dashboard mentions (0)

We have not tracked any mentions of Soft UI Dashboard yet. Tracking of Soft UI Dashboard recommendations started around Apr 2021.

What are some alternatives?

When comparing NetworkX and Soft UI Dashboard, you can also consider the following products

neo4j - Meet Neo4j: The graph database platform powering today's mission-critical enterprise applications, including artificial intelligence, fraud detection and recommendations.

ArchitectUI - Modern dashboard template for bootstrap 4

RedisGraph - A high-performance graph database implemented as a Redis module.

Flatlogic - AI-Powered Software Development for Startups and Businesses

ArangoDB - A distributed open-source database with a flexible data model for documents, graphs, and key-values.

PlainAdmin - PlainAdmin is an Open-source freemium Bootstrap 5 based vanilla JS multipurpose admin template comes with - all essential dashboard components, pages, UI elements, charts, graphs, libraries and everything you may need for a data-rich backends.

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