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