Influence Maximization

Influence Maximization is an AI Engineering skill for Claude Code, published by asgard-ai-platform in skills.

228 stars28 forkson asgard-ai-platform/skillsAdded 2026/09/03Repository updated 2026/06/06

ai-agentanthropicclaudeclaude-agent-skillsclaude-codecoding-agentknowledge-basemcpmethodologyopen-sourceprompt-engineeringskillstaiwan

Install in seconds

Install Influence Maximization

Copy Influence Maximization into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/asgard-ai-platform/skills/tree/main/algo-net-influence ~/.claude/skills/algo-net-influence

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/asgard-ai-platform/skills.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: algo-net-influence/SKILL.md in asgard-ai-platform/skills
Installs to: ~/.claude/skills/algo-net-influence
Collection: One of 25 skills cataloged from this repository
Category: AI Engineering — 2793 skills

What Influence Maximization does

Influence Maximization selects k seed nodes in a network to maximize expected spread under Independent Cascade or Linear Threshold models. Use it to choose seeds for viral campaigns, maximize reach under a budget, or compare seeding strategies.

Influence Maximization is cataloged under AI Engineering on DirSkills. Influence Maximization comes from a repository tagged ai-agent, anthropic, claude, claude-agent-skills and claude-code.

Documentation

README

Excerpt

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

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Influence Maximization

What else does asgard-ai-platform publish alongside Influence Maximization?
Influence Maximization is one of 25 skills that DirSkills catalogs from asgard-ai-platform/skills, the repository it ships in. Its siblings there include ARIMA Time Series Model, Ad Bidding Strategies and Ad Budget Allocation. Each one is a separate skill with its own page in this directory, installs the same way Influence Maximization does, and is maintained by asgard-ai-platform in that same repository. The rest of the collection is listed on the asgard-ai-platform/skills page.
How does Influence Maximization compare to other AI Engineering skills?
Influence Maximization ranks #2504 by stars among the 2793 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Influence Maximization against them. Open each page to compare what they document and how they install.