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Shards UI Kit VS NetworkX

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

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Shards UI Kit logo Shards UI Kit

A free and modern UI kit based on Bootstrap 4.

NetworkX logo NetworkX

NetworkX is a Python language software package for the creation, manipulation, and study of the...
Not present
  • NetworkX Landing page
    Landing page //
    2023-09-14

Shards UI Kit features and specs

  • Modern Design
    Shards UI Kit offers a sleek and modern design that is appealing and fits well with contemporary web projects.
  • Component Variety
    It provides a comprehensive set of components, which makes it easy to build different parts of a website without needing external resources.
  • Lightweight
    The UI kit is lightweight, ensuring that it doesn't significantly increase the load time of web projects, which is vital for user experience and SEO.
  • Responsive
    Shards UI Kit is designed to be fully responsive, allowing components to look and function well on devices of all sizes.
  • Customization
    It offers good flexibility for customization, enabling developers to adapt components to meet the specific branding and style needs of their projects.

Possible disadvantages of Shards UI Kit

  • Limited Documentation
    Some users may find the documentation lacking in depth, which can lead to a steeper learning curve for those unfamiliar with the tool.
  • Dependency on Bootstrap
    Shards UI Kit is built on top of Bootstrap, which could be a limitation for projects that require a design completely free from Bootstrap's styling influences.
  • Community Support
    There might be limited community support compared to more established frameworks, potentially making it harder to find solutions to specific problems.
  • Updates and Maintenance
    The frequency of updates and ongoing maintenance could be less compared to larger-scale UI kits, which may affect long-term project sustainability.
  • Feature Limitations
    Shards UI Kit may not have as many advanced features or pre-built themes as some other UI kits, potentially requiring more development work for feature-heavy applications.

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.

Shards UI Kit videos

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NetworkX videos

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

Category Popularity

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

Shards UI Kit

NetworkX

Design Tools
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Graph Databases
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Graph Databases
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Developer Tools
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Databases
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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.

Shards UI Kit mentions (0)

We have not tracked any mentions of Shards UI Kit yet. Tracking of Shards 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 Shards 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.

Dashboard UI Kit - A modern & responsive dashboard UI kit for designers.

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

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