The Social Network Intelligence Engine (SNIE) is a C++-based analytics framework that models a social network as a graph and performs deep structural intelligence. It detects communities, identifies influential nodes, generates personalized recommendations, and suggests cross-community bridge connections to improve global network connectivity.
This project applies the concepts of Graph Theory, Shortest Paths, Centrality Algorithms, and Semantic Scoring — making it a strong advanced DSA-based project.
The Social Network Intelligence Engine (SNIE) is an analytical system designed to understand, interpret, and optimize connectivity within social, professional, or collaborative networks. Nodes represent individuals or entities, while edges represent relationships with varying strength. Key Features:
● Offline Structural Intelligence: Community detection and Betweenness Centrality (BC)
● Real-Time Local Intelligence: Weighted Dijkstra paths and semantic scoring
● Global Optimization: Cross-community bridge recommendations
● Visualization: Communities, influence, weighted ties, and bridge simulations
● Information Spread
● Detect natural communities and disconnected clusters.
● Identify influential individuals using Betweenness Centrality.
● Recommend meaningful local and cross-community connections.
● Suggest high-impact global bridges that unify separated communities.
● Use semantic edge weights to capture relationship strength.
● Simulate and compare information spreading cost before and after bridging.
● Visualize the entire network along with recommended optimizations.
| Algorithm / Operation | Complexity | Notes |
|---|---|---|
| Community Detection | O(V + E) | DFS / Union-Find |
| Weighted Dijkstra | O((V + E) log V) | Shortest path with weighted edges |
| Betweenness Centrality | O(V × E) | Brandes Algorithm |
| Bridge Pairing | O(k²) | BC × BC heuristic |
| Information Spread Cost Calculation (BFS) | O(V + E) | Each node & edge processed once |
| CompareCost (Before/After Bridging) | O(V + E) | Runs CostCalculation() 3× |
Local Recommendation Score:
Score=α⋅DomainSim+β⋅Proximity+γ⋅StrengthFactor+δ⋅NormalizedBC_score
Bridge Recommendation Score:
BridgeScore=α⋅(1−DomainSim)+β⋅Proximity+γ⋅StrengthBalance+δ⋅NormalizedBC_score
Global Bridge Score (Heuristic):
GlobalScore=BCA(i)×BCB(j)×DomainComplementarity×StrengthBalance
- Python 3.8
- Manin
- Networkx
- numpy
- pandas
- Mingw-32bit
- ffmpeg
- To install Networkx pandas numpy
pip install networkx pandas numpy- Install manim
pip install manim- Install ffmpeg install ffmpeg from https://www.gyan.dev/ffmpeg/builds/ then paste the bin path of ffmpeg in system environmental variables then verify that ffmpeg is installed
ffmpeg -version- Git clone
git clone https://github.com/Ved-Mitra/DSA-Project.git- Exceute
g++ main.cpp -o snie- Execute Animation
python animation.py
manim -pql animation.py SNIEAnimation