Learning Objectives
285 words
1 min read
Visual companion
Python
Type and operator map
Python Week 1: the first filter for runtime behavior
View
Revision summary
What this note is really saying
Short form
# Learning Objectives - Calculate node and network metrics - Identify hubs, bottlenecks, and communities - Understand network models ## 1. Node-Level Metrics **Degree (k):** Number of edges.
Learning Objectives
- Calculate node and network metrics
- Identify hubs, bottlenecks, and communities
- Understand network models
1. Node-Level Metrics
Degree (k): Number of edges. In-degree, out-degree for directed. Betweenness Centrality: Fraction of shortest paths through node. High = bridge between regions. Closeness Centrality: Average shortest path to all others. High = quickly reaches network. Clustering Coefficient: How connected neighbors are to each other. High = locally dense.
2. Network-Level Metrics
Degree Distribution P(k): Probability node has degree k. Biological networks are scale-free (power-law: P(k) ~ k^(-gamma)) - most nodes have few connections, few hubs have many.
Average Path Length: Short paths between nodes. "Small-world" property.
Modularity: How well network divides into communities.
3. Network Models
Random (Erdos-Renyi): Poisson degree distribution. Not biological. Scale-Free (Barabasi-Albert): Preferential attachment. Power-law degree distribution. Small-World (Watts-Strogatz): High clustering + short paths.
Q1: What does scale-free mean?Degree distribution follows power law: few hubs with many connections, most nodes with few. P(k) ~ k^(-gamma). No "typical" degree. Q2: Why are hubs important?Essential for network connectivity. Deleting hub fragments network. In PPI: hubs are often essential genes. In disease: hubs are common drug targets. Q3: What is the small-world property?Short average path lengths + high clustering. Any two nodes connected by few steps. Enables efficient signal propagation. Q4: What is clustering coefficient?How many actual triangles / possible triangles among neighbors. High = locally dense connections = functional modules. Q5: What is modularity?Measure of community structure. Compares edge density within vs between communities. High modularity (0.3-0.7) = strong community structure. Join Discord PreviousIntroduction to Biological NetworksNextProtein-Protein Interaction Networks