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Greencastle
DePauw UniversityJoseph W. BarrMatthew SimpsonRobert Richford RobertsFaculty of DePauw UniversityAlumni of DePauw UniversityDePauw University athleticsDePauw University Carnegie LibraryWilliam Cumback

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McKim Observatory

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Analyzing Network Connections...

Loading Map...

Nearest Locations

  • Thumbnail for Greencastle
    Greencastle0.5 km away
  • 📍
    DePauw University1.0 km away
  • 📍
    Eastern Enlargement Historic District1.1 km away
  • Thumbnail for Plainfield Village Gamelan
    Plainfield Village Gamelan39.3 km away
  • 📍
    Foyt Wine Vault54.5 km away

Network Profile

Overall Strength
i
0.85% of network
(900)
Strength Breakdown
  • This Post (0.85%)
  • Greencastle (7.63%)
  • Alumni of DePauw University (0.85%)
  • DePauw University athletics (0.85%)
  • DePauw University Carnegie Library (0.85%)
  • Faculty of DePauw University (0.85%)
  • Joseph W. Barr (0.85%)
  • Matthew Simpson (0.85%)
  • Robert Richford Roberts (0.85%)
  • William Cumback (0.85%)
Dominant nodes (excluded from chart)
DePauw University 84.75%
Influence Score
i
9.09% of network
(1)
Influence Breakdown
  • This Post (9.09%)
  • Greencastle (9.09%)
  • DePauw University (9.09%)
  • Alumni of DePauw University (9.09%)
  • DePauw University athletics (9.09%)
  • DePauw University Carnegie Library (9.09%)
  • Faculty of DePauw University (9.09%)
  • Joseph W. Barr (9.09%)
  • Matthew Simpson (9.09%)
  • Robert Richford Roberts (9.09%)
  • William Cumback (9.09%)
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 10 direct neighbours)
Network_Strength (CV) = Base_PV * (Neighbour_1_PV * Neighbour_2_PV * ...)
                         = 1 *
                           ( 9 [Greencastle] *
                            100 [DePauw University] *
                            1 [Alumni of DePauw University] *
                            1 [DePauw University athletics] *
                            1 [DePauw University Carnegie Library] *
                            1 [Faculty of DePauw University] *
                            1 [Joseph W. Barr] *
                            1 [Matthew Simpson] *
                            1 [Robert Richford Roberts] *
                            1 [William Cumback]
                           )

                         = 900

Network_Influence (TV) = Base_IV * (Neighbour_1_IV * Neighbour_2_IV * ...)
                         = 1 *
                           ( 1 [Greencastle] *
                            1 [DePauw University] *
                            1 [Alumni of DePauw University] *
                            1 [DePauw University athletics] *
                            1 [DePauw University Carnegie Library] *
                            1 [Faculty of DePauw University] *
                            1 [Joseph W. Barr] *
                            1 [Matthew Simpson] *
                            1 [Robert Richford Roberts] *
                            1 [William Cumback]
                           )

                         = 1
Outbound 1 Tags on post
Inbound 1 Posts tagging this
Connections 10 Total nodes
Base Node Strength 1
Base Node Influence 1
Strength Share (vs Direct Neighbours)
0.85% (900 overall)
  • This Post (0.85%)
  • Greencastle (7.63%)
  • Alumni of DePauw University (0.85%)
  • DePauw University athletics (0.85%)
  • DePauw University Carnegie Library (0.85%)
  • Faculty of DePauw University (0.85%)
  • Joseph W. Barr (0.85%)
  • Matthew Simpson (0.85%)
  • Robert Richford Roberts (0.85%)
  • William Cumback (0.85%)
Dominant nodes (excluded from chart)
DePauw University 84.75%
Influence Share (vs Direct Neighbours)
9.09% (1 overall)
  • This Post (9.09%)
  • Greencastle (9.09%)
  • DePauw University (9.09%)
  • Alumni of DePauw University (9.09%)
  • DePauw University athletics (9.09%)
  • DePauw University Carnegie Library (9.09%)
  • Faculty of DePauw University (9.09%)
  • Joseph W. Barr (9.09%)
  • Matthew Simpson (9.09%)
  • Robert Richford Roberts (9.09%)
  • William Cumback (9.09%)

Connected Network Hierarchy

Sort list by:
Top Network Boosters (Highest Multipliers)
Greencastle ↗
Str: 9Inf: 1
DePauw University ↗
Str: 100Inf: 1
Alumni of DePauw University ↗
Str: 1Inf: 1
DePauw University athletics ↗
Str: 1Inf: 1
DePauw University Carnegie Library ↗
Str: 1Inf: 1
Faculty of DePauw University ↗
Str: 1Inf: 1
Joseph W. Barr ↗
Str: 1Inf: 1
Matthew Simpson ↗
Str: 1Inf: 1
Robert Richford Roberts ↗
Str: 1Inf: 1
William Cumback ↗
Str: 1Inf: 1
Weakest Connections (Lowest Multipliers)
William Cumback ↗
Str: 1Inf: 1
Robert Richford Roberts ↗
Str: 1Inf: 1
Matthew Simpson ↗
Str: 1Inf: 1
Joseph W. Barr ↗
Str: 1Inf: 1
Faculty of DePauw University ↗
Str: 1Inf: 1
DePauw University Carnegie Library ↗
Str: 1Inf: 1
DePauw University athletics ↗
Str: 1Inf: 1
Alumni of DePauw University ↗
Str: 1Inf: 1
DePauw University ↗
Str: 100Inf: 1
Greencastle ↗
Str: 9Inf: 1

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

Outbound Tags (1)
DePauw University
Inbound Posts (1)
DePauw University
Last calculated: Sep 10, 9:22 AM
Greencastle, Putnam County, United States32

Related Content

Figures

  • Joseph W. Barr
  • Matthew Simpson

Locations

  • Greencastle
  • DePauw University

Topics

  • Robert Richford Roberts
  • William Cumback
  • Alumni of DePauw University
  • DePauw University athletics
  • DePauw University Carnegie Library
  • Faculty of DePauw University