Results Display Panel

Relevant source files

The following files were used as context for generating this wiki page:

• Form1.Designer.cs
• Form1.cs
• Program.cs
• README.md
• Barabasi.cs
• Network.py

The Results Display Panel is the primary graphical user interface for the C# "Network Analyzer" application. Its purpose is to load a network structure from an edge list file, compute a variety of network science metrics, and present the results to the user through statistical labels and graphical charts. This tool is a core component of the project, designed to analyze network properties like clustering, degree correlation, and degree distribution, as described in the project's README.md.

The panel is implemented as a Windows Form (Form1) and serves as the main entry point and interactive surface for the C# analysis program. It provides functionality to open files, process network data, and visualize key characteristics, particularly those related to scale-free networks.

Sources: Form1.cs, Form1.Designer.cs, README.md:3-5

UI Components and Layout

The main window, titled "Network Analyzer", is composed of several key areas: a menu bar for file operations, a panel for displaying summary statistics, a status text box, and two primary charts for data visualization.

Sources: Form1.Designer.cs:290, Form1.Designer.cs:291

The following diagram shows the logical arrangement of the main UI components on the Form1 window.

Key UI Elements

The table below details the primary UI controls defined in Form1.Designer.cs and their functions.

Component ID	Type	Description
menuStrip1	MenuStrip	Contains the "File" -> "Open" menu to load an edge list file.
lblDataset	Label	Displays the name of the loaded dataset file.
lblNodes	Label	Displays the total number of nodes in the network.
lblLinks	Label	Displays the total number of links (edges) in the network.
lblMean	Label	Shows the average degree of the network.
lblSelf	Label	Shows the count of self-linking nodes.
lblDupe	Label	Shows the count of duplicate links.
lblCluster	Label	Displays the average clustering coefficient (if chkClustering is checked).
lblCorrelation	Label	Displays the calculated degree correlation exponent.
lblGamma	Label	Displays the calculated degree exponent (gamma) for the degree distribution.
lblMin / lblMax	Label	Display the minimum and maximum node degrees found in the network.
chkClustering	CheckBox	A checkbox to enable/disable the calculation of the average clustering coefficient.
txtCutoff	TextBox	An input field to set a minimum degree cutoff for the cumulative degree distribution regression.
textBox1	TextBox	A multi-line text box used to display real-time status updates during data processing.
chart1	Chart	Visualizes the Degree Correlation, plotting average neighbor degree vs. node degree.
chart2	Chart	Visualizes the Cumulative Degree Distribution.

Sources: Form1.Designer.cs:46-285

Data Processing and Display Flow

The analysis process is initiated when the user selects a file via the "Open" menu item. This triggers the openToolStripMenuItem_Click event handler, which orchestrates the entire workflow from file reading to UI updates.

The following flowchart outlines the sequence of operations:

Core Data Structures

The application's logic is built around a few key data structures defined within Form1.cs to represent the network.

Sources: Form1.cs:54-199

• Edge: A simple class to represent a link between two nodes, a and b.
  Sources: Form1.cs:56-67
• Node: Represents a single node in the network. It stores its node_ID, degree, a list of its neighbors, its clustering coefficient, and the average degree of its neighbors (neighbors_degree).
  Sources: Form1.cs:69-82
• NodeList: A container for all Node objects. It is constructed from an initial List<Edge>. During its construction, it calculates basic properties like selfies, dupes, max_degree, and min_degree, and populates the degree and neighbor information for each node. It also contains the method for calculating the global average clustering coefficient.
  Sources: Form1.cs:84-173

Chart Generation

The panel features two charts that are central to the network analysis. Both charts use logarithmic scaling on both axes to better visualize power-law relationships, which are characteristic of scale-free networks.

Sources: Form1.cs:289-290, 347-348

Degree Correlation Chart (chart1)

This chart visualizes the degree correlation of the network.

• Purpose: To determine if the network is assortative (high-degree nodes connect to other high-degree nodes) or disassortative (high-degree nodes connect to low-degree nodes).
• X-axis: Node Degree (k)
• Y-axis: Average Neighbor Degree (k_nn(k))
• Implementation:
  1. For each degree value deg present in the network, the code calculates the average of neighbors_degree for all nodes with that degree.
  2. These (deg, avg_neighbors_degree) pairs are plotted as points.
  3. A power-law regression line (y = c * x^exp) is fitted to these points using MathNet.Numerics.Fit.Power.
  4. The points and the red regression line are plotted on chart1. The calculated exponent is displayed in lblCorrelation.

Sources: Form1.cs:259-293, Form1.Designer.cs:79-82

Cumulative Degree Distribution Chart (chart2)

This chart visualizes the cumulative probability distribution of node degrees, P(k).

• Purpose: To analyze if the network follows a power-law (scale-free) degree distribution.
• X-axis: Node Degree (k)
• Y-axis: Cumulative Probability P(k) (the fraction of nodes with degree k or greater).
• Implementation:
  1. The code iterates from degree 1 to max_degree, calculating the fraction of nodes with a degree less than the current one and subtracting it from a running total prev (initialized to 1). This gives the cumulative probability.
  2. These (deg, P(k)) pairs are plotted as points.
  3. A "shadow" series is created containing only points where the degree is greater than the value in txtCutoff.
  4. A power-law regression is performed on this shadow series to find the exponent.
  5. The degree exponent gamma is calculated as 1 - exp (where exp is the exponent from the cumulative distribution fit) and displayed in lblGamma.
  6. The points and the red regression line are plotted on chart2.

Sources: Form1.cs:316-353, Form1.Designer.cs:238-241

Related Project Components

While the Results Display Panel is part of the C# application, other files in the repository serve related purposes in the overall study of network science.

• Barabasi.cs: A console application that can generate a synthetic network using the Barabasi-Albert preferential attachment model. The output is an edge list file (.txt) that can be directly loaded and analyzed by the "Network Analyzer" GUI.
  Sources: Barabasi.cs:11-15
• Network.py: A Python script that performs a similar function to the C# application. It reads an edge list, calculates the degree distribution, performs a linear regression on the log-log data to find the exponent (gamma), and plots the results using the bokeh library. This represents an alternative implementation of the core analysis.
  Sources: Network.py