Muso

Directory About
Continue with Google
Continue with with Facebook

Top Related Posts

Parks and Recreation
Mary FaberKiddingThe Brink

Recent Logins

View Members Directory

Tadhg Kelleher
Last login: 10 hours ago
Comments: 0

jacierlogyn
Last login: 2 days ago
Comments: 0

Eileen Lally
Last login: 4 weeks ago
Comments: 0

Özkan Konu
Last login: 1 month ago
Comments: 0

Anne Marie Bevan
Last login: 1 month ago
Comments: 0

Kevin Greene
Last login: 2 months ago
Comments: 0

Big City Greens

Loading Graph...
Press Spacebar to toggle layout
Join the conversation

💬 Know something?

Sign in to leave a note, add a photo, or make a connection.

Continue with Google Continue with Facebook

Keep Muso free

Muso is built by one person, for the love of it. no investors — just your support.

€10
Covers an hour of research
Most popular
€25
Keeps the archive running
€50
Funds a full week of work
✎ Enter my own amount
€5
per month · cancel any time

You'll confirm the amount on the next screen

Donate €25 →
Secure checkout via Stripe  ·  No account needed

Analyzing Network Connections...

Network Profile

Overall Strength
i
0.12% of network
(9.78M)
Strength Breakdown
  • This Post (0.12%)
  • Parks and Recreation (63.05%)
  • Kidding (34.45%)
  • Mary Faber (1.91%)
  • The Brink (0.48%)
Influence Score
i
20.00% of network
(1)
Influence Breakdown
  • This Post (20.00%)
  • Parks and Recreation (20.00%)
  • Kidding (20.00%)
  • Mary Faber (20.00%)
  • The Brink (20.00%)
Direct Connections 2

Node & Network Details

How is this calculated?

The math continuously tracks how strongly this post is connected to the rest of the network. Every tag forms a 2-way link. The base stats determine personal node strength, and the pie charts below show this node's share against its direct neighbours.

// 1. Base variables (floored at 1 to prevent zero-multiplication math errors)
$inbound = max(1, 1) = 1
$outbound = max(1, 1) = 1

// 2. Node Base Values (Local connection strength)
Base_Strength (PV) = $inbound * $outbound = 1 * 1 = 1
Base_Influence (IV) = $inbound / $outbound = 1 / 1 = 1

// 3. Exponential Network Values (accumulating 4 direct neighbours)
Network_Strength (CV) = Base_PV * (Neighbour_1_PV * Neighbour_2_PV * ...)
                         = 1 *
                           ( 529 [Parks and Recreation] *
                            289 [Kidding] *
                            16 [Mary Faber] *
                            4 [The Brink]
                           )

                         = 9.78M

Network_Influence (TV) = Base_IV * (Neighbour_1_IV * Neighbour_2_IV * ...)
                         = 1 *
                           ( 1 [Parks and Recreation] *
                            1 [Kidding] *
                            1 [Mary Faber] *
                            1 [The Brink]
                           )

                         = 1
Outbound 1 Tags on post
Inbound 1 Posts tagging this
Connections 4 Total nodes
Base Node Strength 1
Base Node Influence 1
Strength Share (vs Direct Neighbours)
0.12% (9.78M overall)
  • This Post (0.12%)
  • Parks and Recreation (63.05%)
  • Kidding (34.45%)
  • Mary Faber (1.91%)
  • The Brink (0.48%)
Influence Share (vs Direct Neighbours)
20.00% (1 overall)
  • This Post (20.00%)
  • Parks and Recreation (20.00%)
  • Kidding (20.00%)
  • Mary Faber (20.00%)
  • The Brink (20.00%)

Connected Network Hierarchy

Sort list by:
Top Network Boosters (Highest Multipliers)
Parks and Recreation ↗
Str: 529Inf: 1
Kidding ↗
Str: 289Inf: 1
Mary Faber ↗
Str: 16Inf: 1
The Brink ↗
Str: 4Inf: 1
Weakest Connections (Lowest Multipliers)
The Brink ↗
Str: 4Inf: 1
Mary Faber ↗
Str: 16Inf: 1
Kidding ↗
Str: 289Inf: 1
Parks and Recreation ↗
Str: 529Inf: 1

Connection Health Audit (Red = broken 1-way link)

Outbound Tags (1)
Mary Faber
Inbound Posts (1)
Mary Faber
Last calculated: Jul 1, 4:42 AM
14

Related Content

Actors

  • Mary Faber

Films

  • The Brink
  • Parks and Recreation
  • Kidding