algo-net-influence
Influence Maximization
Overview
Influence maximization selects k seed nodes in a network to maximize expected spread under a diffusion model (Independent Cascade or Linear Threshold). NP-hard, but the greedy algorithm achieves (1-1/e) ≈ 63% approximation guarantee due to submodularity. Practical for networks up to millions of nodes with CELF optimization.
When to Use
Trigger conditions:
- Selecting k influencers/users to seed a viral marketing campaign
- Maximizing information spread under a fixed budget (k seeds)
- Comparing seeding strategies (degree-based vs greedy vs random)
When NOT to use:
- When measuring existing influence (use centrality metrics)
- For community structure analysis (use community detection)
Algorithm
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