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Dashboard UI Kit VS NetworkX

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

Kimp
Kimp is an unlimited design company, specializing in graphic design (including print and digital designs, custom illustrations, landing page designs and email designs) and video design. featured
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Dashboard UI Kit logo Dashboard UI Kit

A modern & responsive dashboard UI kit for designers.

NetworkX logo NetworkX

NetworkX is a Python language software package for the creation, manipulation, and study of the...
  • Dashboard UI Kit Landing page
    Landing page //
    2019-01-23
  • NetworkX Landing page
    Landing page //
    2023-09-14

Dashboard UI Kit features and specs

  • Comprehensive Components
    Dashboard UI Kit offers a wide array of pre-designed elements such as charts, tables, forms, and widgets, which can significantly speed up the development process and ensure consistency.
  • Customizability
    The UI Kit allows for extensive customization of elements, providing designers and developers the flexibility to tailor components to fit their specific project needs and branding guidelines.
  • Responsive Design
    The components in the Dashboard UI Kit are designed to be fully responsive, ensuring a seamless user experience across different devices and screen sizes.
  • User-Friendly Documentation
    The kit comes with detailed documentation that helps users understand how to effectively use and customize components, reducing the learning curve.
  • Regular Updates
    Frequent updates and additions to the Dashboard UI Kit mean users can benefit from the latest design trends and new functionalities.

Possible disadvantages of Dashboard UI Kit

  • Price
    Dashboard UI Kit is a premium product, and its cost might be a barrier for small businesses or individual developers looking for budget-friendly solutions.
  • Learning Curve
    For beginners or those unfamiliar with design systems, there might be a learning curve associated with fully utilizing the kit's features and customizing components.
  • Dependency on Updates
    While regular updates are a positive aspect, they can also lead to dependency issues where projects may need adjustment to accommodate changes made in newer versions of the kit.
  • Limited Unique Customization
    Despite the customizability, heavily relying on a UI kit can sometimes result in designs that lack uniqueness, making multiple projects look similar if not adequately personalized.
  • Potential Overhead
    Including all components from the UI kit, even the ones not being used, could add unnecessary overhead to the project, impacting performance.

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.

Analysis of Dashboard UI Kit

Overall verdict

  • Dashboard UI Kit is considered a good choice for designers and developers looking to expedite their workflow without sacrificing quality. Its versatile components and robust design language make it a valuable asset for creating intuitive and visually appealing dashboards.

Why this product is good

  • Dashboard UI Kit is known for providing a comprehensive set of design components and templates that streamline the process of building and designing dashboards. It's praised for its modern design principles, ease of use, and adaptability to various platforms and industries.

Recommended for

  • UI/UX designers
  • Front-end developers
  • Product managers working on dashboard projects
  • Startups needing quick prototyping for dashboards
  • Design teams focusing on efficiency and consistency in dashboard interfaces

Dashboard UI Kit videos

+ Add

Design of Product Detail Popup/Modal (Dashboard UI Kit 3.0)

NetworkX videos

+ Add

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

Category Popularity

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

Dashboard UI Kit

NetworkX

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

User comments

Share your experience with using Dashboard UI Kit and NetworkX. For example, how are they different and which one is better?
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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.

Dashboard UI Kit mentions (0)

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

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
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What are some alternatives?

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

Now UI Kit - A beautiful Bootstrap 4 UI kit. Yours free.

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

Bots UI Kit - Fully customizable Sketch UI Kit for Messenger Platform

Azure Cosmos DB - NoSQL JSON database for rapid, iterative app development.

lstore.graphic - Mockup Scene Creator

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

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