Technical Implementation: Diving Deep: My Social Information Network Project for Spotting Crypto Scams

System Architecture

The system is built using a microservices architecture with the following key components:

Data Collection Services

class DataCollector:
    def __init__(self):
        self.blockchain_collector = BlockchainCollector()
        self.social_media_collector = SocialMediaCollector()
        self.memecoin_collector = MemecoinCollector()

    async def collect_data(self):
        blockchain_data = await self.blockchain_collector.fetch_transactions()
        social_data = await self.social_media_collector.fetch_posts()
        memecoin_data = await self.memecoin_collector.fetch_market_data()
        return self.merge_data(blockchain_data, social_data, memecoin_data)

Risk Detection System

def detect_rug_pull_risk(self):
    risk_metrics = {
        'centralization_risk': min(1.0, top_holders_concentration * 2),
        'community_isolation_risk': community_size_ratio,
        'network_resilience_risk': min(1.0, critical_nodes / 5)
    }
    return self._calculate_weighted_risk(risk_metrics)

Key Technical Innovations

1. Hybrid Graph Analysis

   • Combines blockchain transaction patterns with social media influence
   • Uses spectral clustering for community detection
   • Implements adaptive betweenness calculation

2. Temporal Resilience Analysis

   def get_subgraph_for_timewindow(self, start, end):
       return self.blockchain_graph.edge_subgraph([
           (u, v) for u, v, d in self.blockchain_graph.edges(data=True)
           if start <= d['timestamp'] < end
       ])
   

3. Synthetic Data Augmentation

   • Generates realistic market cap distributions
   • Creates plausible coin naming patterns
   • Maintains statistical properties of real data

4. Robust Correlation Handling

   try:
       correlation = np.corrcoef([tx_volume, social_influence])[0,1]
   except:
       correlation = 0.0
   

5. Market Influence Modeling

   • Implements ERGM with robust regression

   model = sm.RLM(y, X).fit()
   

   • Calculates Gini coefficients for market concentration

Visualization System

Multi-Panel Network Visualization

1. Community Structure
2. Market Cap Relationships
3. Combined Force-Directed Layout

Implementation Features

• Adaptive node sizing by market cap
• Edge coloring by similarity weight
• Automated label placement
• Community color coding

def visualize_network_models(self):
    plt.figure(figsize=(20, 10))
    # Community visualization
    nx.draw_networkx(..., node_color=node_colors)
    # Market relationships
    nx.draw_networkx(..., edge_cmap=plt.cm.YlOrRd)
    # Combined layout
    nx.draw_networkx(..., edge_width=combined_weights)

Testing Framework

Synthetic Test Scenarios

• High/Medium/Low risk configurations
• Automated explanation generation
• Validation against known patterns

Test Implementation

def test_rug_pull_detection(self):
    scenarios = {
        'high_risk': self._generate_high_risk_scenario(),
        'medium_risk': self._generate_medium_risk_scenario(),
        'low_risk': self._generate_low_risk_scenario()
    }
    for name, metrics in scenarios.items():
        self.explain_rug_pull_risk(metrics)

Performance Considerations

1. Graph Optimization

   • Uses sparse matrix representations
   • Implements batched edge processing
   • Memoizes centrality calculations

2. API Rate Limiting

   time.sleep(1)  # Respect CoinMarketCap rate limits
   

3. Numerical Stability

   • Adds epsilon to denominators
   • Implements safe division
   • Handles NaN/Inf edge cases

Error Handling System

1. Graph Metric Fallbacks

   if len(undirected_graph) < 3:
       features['betweenness'] = 0.0
   

2. API Error Recovery

   try:
       # API call here
   except Exception as e:
       print(f"API Error: {str(e)}")
       return pd.DataFrame()
   

3. Numerical Safety

   engagement_score = value / (social_influence + 1)
   

Future Improvements

1. Enhanced Graph Analysis

   • Implement more sophisticated network metrics
   • Add support for temporal graph analysis
   • Incorporate multi-layer network analysis

2. Machine Learning Enhancements

   • Experiment with deep learning models
   • Implement online learning for model updates
   • Add support for semi-supervised learning

3. Scalability Improvements

   • Implement data sharding
   • Add support for distributed processing
   • Optimize memory usage for large graphs

4. Additional Features

   • Add support for more blockchain networks
   • Implement cross-chain analysis
   • Add advanced visualization tools