Core-Periphery Fit Index Calculator
Actual unweighted pair agreement with caller binary ideal:edge iff at least one endpoint core. Not optimize core assignment, universal core-periphery fit statistic or social status inference.
Description
An ideal core-periphery comparison tests a declared core partition against observed ties.
An ideal core-periphery comparison tests a declared core partition against observed ties.1
Pair agreement under that binary ideal differs from fitting a probabilistic or weighted model.
When to use Core-Periphery Fit Index Calculator
Use this tool to obtain observedIdealCorePeripheryAgreementFraction from the supplied records using the stated calculation. Establish the population, period, variable definitions, sample coverage and independently applicable instrument or calibration.
How to use Core-Periphery Fit Index Calculator and understand its results
- Establish the population, period, variable definitions, sample coverage and independently applicable instrument or calibration.
- Enter observedAdjacency, callerCoreFlags, using the field meanings and rules below, then run the tool.
- Read result.observedIdealCorePeripheryAgreementFraction. Use the units and sign conventions below, and review any unknown records separately.
- Correct any reported input issue and run again. Before using a successful result, check the stated units, signs, reference scope and observation coverage.
| Input field | Meaning | Value type | Requirement |
|---|---|---|---|
| observedAdjacency | Observed adjacency | numeric matrix | Required |
| callerCoreFlags | Caller core flags | list of number | Required |
Require a symmetric binary zero-diagonal graph for two through 32 nodes and aligned binary caller-declared core flags. For every unordered pair, the ideal expects a tie when either endpoint is core and no tie when both are peripheral. Return the fraction of observed pairs agreeing with that ideal, including absent ties. No partition optimization or chance correction is applied.
agreementFraction=count(a_ij=(core_i or core_j), i<j)/[n×(n−1)/2] Use the matching field units and the exact boundary, ordering and reference rules above.
| Output field | Meaning | Value type |
|---|---|---|
| result | Calculated observation or qualified reference | object |
| result.observedIdealCorePeripheryAgreementFraction | Fraction of unordered node pairs agreeing with the caller-partition binary ideal. | number |
{
"input": {
"observedAdjacency": [
[
0,
1,
0
],
[
1,
0,
1
],
[
0,
1,
0
]
],
"callerCoreFlags": [
0,
1,
0
]
},
"output": {
"result": {
"observedIdealCorePeripheryAgreementFraction": 1
}
}
}Limitations and assumptions
Population definitions, survey design, calibration and observation coverage are independently established. A result describes the supplied record or conditional model without demonstrating causation, validating an instrument or supplying representative population evidence.
Missing required fields, unknown fields, wrong types and violated rules return input issues. Numeric values and outputs must be finite. Lists and matrices must be dense, without missing cells. Numeric lists allow at most 10000 entries, subject to the narrower lengths and code rules above. Numeric matrices allow at most 128 rows and 128 columns, subject to the narrower shape requirements above. Extremely large composite outputs are rejected.
Alternative or Complementary methods
For a related step, use Regular Equivalence Role Assigner Tool: Network role refinement groups nodes by the role types they reach and receive ties from. For a complementary comparison, use Blockmodel Image Matrix Density Calculator: A blockmodel image can summarize directed edge density between declared node groups.
References
-
Core-Periphery Fit Index Calculator — tool.io
Similar or alternative tools
- Regular Equivalence Role Assigner Tool
Network role refinement groups nodes by the role types they reach and receive ties from.
- Blockmodel Image Matrix Density Calculator
A blockmodel image can summarize directed edge density between declared node groups.