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Cover image for Graphs + AI : Why CDA Algorithms so Critical & Relevant in Today’s Complex World ?
Aniket Hingane
Aniket Hingane

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Graphs + AI : Why CDA Algorithms so Critical & Relevant in Today’s Complex World ?

Birds of a feather flock together | Shine a Light on Connectivity in Today’s Complex World with Community Detection Algorithms!

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Why Bother spending time learn CDA?

  • Understand the real-world business value of Community Detection Algorithms.
  • Learn how communities serve as building blocks for complex systems.

But, What really is Community Detection?

  • Demystify the concept of community detection through a simple analogy.
  • Discover how it helps identify naturally occurring groups within networks.

Why read this article

  • Gain insights into the hidden connections within financial systems and social circles.
  • Unlock the power of Community Detection Algorithms for understanding complex networks.

Graph Generated from code in this project

  • Witness the visualization of a simulated financial network.
  • Explore the interactive graph showcasing transaction patterns.

Real-world superpowers

  • Discover the practical applications of CDA in fraud detection, marketing strategies, and scientific analysis.

Generating Simulated Data

  • Increase complexity in entity generation for a more realistic financial network simulation.
  • Emulate real-world financial interactions through random transactions.

Creating and Visualizing the Network

  • Utilize NetworkX to construct a multi-directed graph representing the financial network.
  • Visualize the network using the Pyvis library, exported to an interactive HTML file.

Loading Data into Neo4j

  • Represent entities and transactions in Neo4j for further analysis.
  • Prepare data for graph algorithm analysis through data loading.

Running the Louvain Algorithm

  • Optimize modularity with the Louvain algorithm to detect communities.
  • Obtain results indicating detected communities and their modularity scores.

Future work (for next part)

  • Visualize communities within the network to gain deeper insights into transaction patterns and network organization.

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