Building and Running the C# Application

Relevant source files

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

• Program.cs
• Form1.cs
• Form1.Designer.cs
• Barabasi.cs
• Properties/AssemblyInfo.cs
• README.md
• Properties/Settings.Designer.cs

The C# code within this repository provides two primary functionalities related to network science. The first is a console application for generating synthetic networks using the Barabasi-Albert preferential attachment model. The second is a Windows Forms GUI application named "Network Analyzer" designed to load network data from a file, compute a variety of statistical metrics, and visualize the results. These tools are intended to support the study of network properties as described in the Barabasi Network Science textbook.

This document outlines the architecture, components, and execution flow of both C# applications, based on an analysis of the source code. It covers the GUI application's entry point, its UI structure, the data processing pipeline for network analysis, and the logic of the separate network generation console application.

Sources: README.md:2-5, Form1.Designer.cs:296, Barabasi.cs:5

Application Entry Point

The primary user-facing application is the "Network Analyzer" GUI. The entry point for this application is the Main method in Program.cs. This method is responsible for initializing the application environment and launching the main form.

// File: Program.cs:16-20
static void Main()
{
    Application.EnableVisualStyles();
    Application.SetCompatibleTextRenderingDefault(false);
    Application.Run(new Form1());
}

This standard Windows Forms startup sequence instantiates and displays Form1, which contains all the UI elements and logic for the network analysis tool.

Sources: Program.cs:15-20

Network Analyzer GUI (Form1)

The Form1 class is the core of the analysis tool, providing both the user interface and the backend logic for processing and visualizing network data.

Sources: Form1.cs, Form1.Designer.cs

UI Layout and Components

The user interface is defined in Form1.Designer.cs and provides controls for data loading, configuration, and visualization.

The following diagram shows the main components of the Form1 user interface.

The key UI components and their functions are summarized below:

Component ID	Type	Purpose	Source
menuStrip1	MenuStrip	Contains the "File" menu for opening datasets.	Form1.Designer.cs:141
openToolStripMenuItem	ToolStripMenuItem	Menu item that triggers the file open dialog.	Form1.Designer.cs:158
lblNodes, lblLinks, etc.	Label	Display various calculated network statistics.	Form1.Designer.cs:95-135, Form1.Designer.cs:254, Form1.Designer.cs:260
chart1	Chart	Visualizes the "Degree Correlation" with a power-law regression line.	Form1.Designer.cs:69
chart2	Chart	Visualizes the "Cumulative Degree Distribution" with a power-law regression line.	Form1.Designer.cs:222
textBox1	TextBox	Shows real-time status messages during long calculations.	Form1.Designer.cs:86
chkClustering	CheckBox	Toggles the calculation of the average clustering coefficient.	Form1.Designer.cs:214
txtCutoff	TextBox	Sets the minimum degree for the power-law fit on the degree distribution.	Form1.Designer.cs:266

Core Data Structures

The application uses a set of nested classes defined within Form1.cs to represent the network.

The relationship between these classes is shown below.

• Edge: A simple class representing a connection between two nodes, a and b.
  Sources: Form1.cs:46-57
• Node: Represents a single node in the network. Its key properties are detailed in the table below.
  Sources: Form1.cs:58-73
• NodeList: A container for all Node objects. It is initialized from a list of edges and is responsible for orchestrating most of the network metric calculations, including degrees, neighbor lists, and clustering coefficients.
  Sources: Form1.cs:74-179

Node Class Properties

Property	Type	Description	Source
node_ID	int	Unique identifier for the node.	Form1.cs:64
degree	int	The number of connections (edges) the node has.	Form1.cs:65
neighbors	List<int>	A list of the node_IDs of connected nodes.	Form1.cs:66
clustering	double	The node's local clustering coefficient.	Form1.cs:67
neighbors_degree	double	The average degree of the node's neighbors.	Form1.cs:68

Data Processing and Analysis Flow

The entire analysis process is initiated when the user selects a file via the "File" -> "Open" menu item, which triggers the openToolStripMenuItem_Click event handler.

The following sequence diagram illustrates the flow of operations from user interaction to final visualization.

The key steps are:

1. File Loading: An edgelist file (tab-separated node pairs) is read into a List<Edge>.
2. Network Construction: A NodeList object is created from the edgelist. Its constructor iterates through the edges to:
   • Build a unique list of Node objects.
   • Populate the neighbors list and calculate the degree for each node.
   • Calculate the average degree of neighbors (neighbors_degree) for each node.
3. Statistics Display: Basic network properties like node count, link count, and min/max degree are calculated and displayed in the UI labels.
4. Degree Correlation: The application calculates the average neighbor degree for each degree k. These points are plotted on chart1 (log-log scale), and a power-law regression is fitted using MathNet.Numerics.Fit.Power to find the degree correlation exponent.
5. Degree Distribution: The cumulative degree distribution P(k) is calculated. A power-law fit is applied to the tail of this distribution (for degrees greater than txtCutoff.Text) to estimate the degree exponent gamma. The distribution and the regression line are plotted on chart2 (log-log scale).

Sources: Form1.cs:189-346

Network Generation (Barabasi.cs)

Separate from the GUI analyzer, the project includes a console application for generating synthetic networks. This is contained entirely within Barabasi.cs.

Sources: Barabasi.cs

Purpose and Execution Flow

This application generates a network using the Barabasi-Albert model and writes the resulting edgelist to a file. It can also perform edge rewiring to control the network's degree correlation.

The generation process follows these steps:

The parameters in the Main method are configurable:

Parameter	Type	Default Value	Description	Source
nodes	int	10000	Total number of nodes to generate in the network.	Barabasi.cs:9
degree	int	3	Number of edges to add for each new node.	Barabasi.cs:10
prob	int	50	Percentage of links to rewire in the Associate method.	Barabasi.cs:11
sociable	bool	false	Determines rewiring type: true for assortative (positive), false for disassortative.	Barabasi.cs:12

The Associate method implements edge rewiring. It randomly selects two edges and, depending on the positive flag, either swaps endpoints to connect nodes of similar degree (assortative mixing) or dissimilar degree (disassortative mixing).

Sources: Barabasi.cs:65-90

Project and Assembly Information

The C# project is configured as an assembly titled "Charts". This information is defined in Properties/AssemblyInfo.cs.

// File: Properties/AssemblyInfo.cs:9
[assembly: AssemblyTitle("Charts")]
[assembly: AssemblyProduct("Charts")]
[assembly: AssemblyCopyright("Copyright ©  2020")]
[assembly: AssemblyVersion("1.0.0.0")]

Sources: Properties/AssemblyInfo.cs:9-12, Properties/AssemblyInfo.cs:28-29