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Juha Kiminki
Sarcofagus
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Network Profile
Node & Network Strength Details
How is this calculated? The math continuously tracks how strongly this post is connected to the rest of the website.
Every tag forms a network link. The pie charts below show each connected post's base strength (PV) and influence (IV) as a share of this post's direct neighbourhood.
// 1. Base variables (minimum value of 1 to prevent zero-multiplication issues)
Outbound ($in) = max(1, 2) =
2
Inbound ($out) = max(1, 2) =
2
// 2. Node Base Values
Base Strength (PV) = $in × $out = 2 × 2 =
4
Base Influence (IV) = $in ÷ $out = 2 ÷ 2 =
1
// 3. Network Exponential Values (accumulating 10 direct neighbours)
Network Strength (CV) = Node PV × (Π Neighbour PVs) =
64.23B
Network Influence (TV) = Node IV × (Π Neighbour IVs) =
1
Outbound
2
Tags on post
Inbound
2
Posts tagging this
Base Node Strength
4
Base Node Influence
1
Connected Nodes
10
Strength Share (vs Direct Neighbours)
- This Post (1.73%)
- Pete Ahonen (15.58%)
- Kimmo Kuusniemi (10.82%)
- Tanja Katinka Karttunen (6.93%)
- Jukka Jokikokko (3.90%)
- Ari Pekka Roitto (1.73%)
- Esa Kotilainen (1.73%)
- Jukka Homi (1.73%)
- Jukka Ritari (1.73%)
- Ville Hanhisuanto (1.73%)
Dominant nodes (excluded from chart)Sarcofagus 52.38%
Influence Share (vs Direct Neighbours)
- This Post (9.09%)
- Sarcofagus (9.09%)
- Pete Ahonen (9.09%)
- Jukka Jokikokko (9.09%)
- Ari Pekka Roitto (9.09%)
- Esa Kotilainen (9.09%)
- Jukka Homi (9.09%)
- Jukka Ritari (9.09%)
- Kimmo Kuusniemi (9.09%)
- Tanja Katinka Karttunen (9.09%)
- Ville Hanhisuanto (9.09%)
Connected Network (How neighbours affect this node)
Top Network Boosters (Highest Multipliers)
Weakest Connections (Lowest Multipliers)
Connection Health Audit (Red = broken 1-way link)
Last calculated math cycle: May 5, 5:08 AM
Analyzing Network Connections...
👁️ 20 Views
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