Myopic Hybrid Seed Selection Algorithm
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Myopic‑Hybrid Algorithm
Overview
A concise academic animation that walks through the deterministic Myopic‑Hybrid algorithm for selecting seed nodes in influence outreach. The viewer sees a dark‑background social network, the step‑by‑step computation of degrees, influence probabilities, underserved node identification, and the degree‑based tiebreaker, culminating in the final seed set and performance metrics.
Phases
| # | Phase Name | Duration | Description |
|---|---|---|---|
| 1 | Scene 1 – Network & Goal | ~4 s | Show a dark‑background graph of ~35 nodes, highlight the goal equation . |
| 2 | Scene 2 – Initial Seed | ~5 s | Compute degrees, highlight the highest‑degree node, display and seed set , then animate a diffusion wave from that seed. |
| 3 | Scene 3 – Influence Probabilities | ~5 s | After diffusion, assign probabilities , colour‑code nodes (strong colour = high probability, faint colour = low). |
| 4 | Scene 4 – Underserved Nodes | ~5 s | Compute , highlight the least‑influenced nodes, show candidate set with . |
| 5 | Scene 5 – Degree‑Based Tiebreaker | ~5 s | Show average degree , list candidate nodes with their degrees, highlight the node as the next seed. |
| 6 | Scene 6 – Add Seed & Diffuse | ~4 s | Update seed set , animate a second diffusion wave from the enlarged seed set. |
| 7 | Scene 7 – Iterative Loop | ~8 s | Briefly show the loop of steps (estimate → find → build → compute → select → add seed) with a fast‑forward visual, repeating until . The network updates after each iteration (node colours change). |
| 8 | Scene 8 – Final Evaluation | ~5 s | Display final seed set , animate influence spreading from all seeds, and show performance metrics , , and Jain’s Fairness Index together with min probability, influence gap, and disparity. |
| 9 | Final Scene – Message | ~3 s | Fade in the title MYOPIC‑HYBRID and tagline "Target the underserved. Choose a structurally reliable seed." |
Layout
┌─────────────────────────────────────────────┐
│ │
│ MAIN (graph) │
│ │
├─────────────────────────────────────────────┤
│ Caption / equation (small, persistent) │
└─────────────────────────────────────────────┘
Area Descriptions
| Area | Content | Notes |
|---|---|---|
| Main | Dark‑background network graph, diffusion waves, highlighted nodes, candidate sets, and any dynamic visual cues. | Occupies ~85 % of frame height. |
| Caption | Current key equation or short label (e.g., "Goal: Select k seeds", "Initial seed", "Candidate set C", etc.). | Small footer; updates each phase. |
Notes
- Assumptions made due to missing specifics:
- Number of nodes = 35, edge density moderate to keep the graph readable.
- Diffusion is visualised as a radial colour‑pulse expanding from seed nodes.
- Probability values are abstract; colour intensity is used instead of numeric labels.
- The loop in Scene 7 is shown as a fast‑forward montage rather than a full‑length repetition to keep total runtime ≈ 45 s.
- All equations are displayed in LaTeX format in the caption area; no additional on‑screen text is used.
- Dark background with light‑coloured nodes for high contrast.
- Smooth easing for all transitions (fade‑in, highlight, diffusion pulse).
- The entire animation fits within a single Manim
Sceneclass. - No audio or interactive elements are required.
- The final message uses bold typography and stays on screen for a brief pause before fading out.
Créé par
Description
The animation walks through a deterministic algorithm that selects seed nodes for influence spread in a dark‑background network. It shows degree computation, initial seed choice, assignment of influence probabilities, identification of underserved nodes, a degree‑based tiebreaker, iterative seed addition, and final performance metrics such as average influence, variance, and fairness index.
Date de création
Aug 13, 2026, 07:21 AM
Durée
0:00