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Understanding Relationships Through Networks

Social network analysis explores how individuals are connected and influence each other. Our simulator allows you to visualize and manipulate these relationships, revealing patterns of interaction that would otherwise be hidden.

mysimulator teamUpdated June 2026≈ 5 min read▶ Open the simulation

Nodes and Edges

At its core, a social network is defined by nodes (representing individuals) and edges (representing relationships between them). In our simulator, these are represented as points connected by lines. The thickness of the line can represent the strength of the connection – a thicker line might signify a closer relationship.

Each node has attributes like age, location, and interests, which can be used to further analyze network behavior. The simulation allows you to define the initial connections between nodes, creating a starting social graph.

Network Metrics

Several key metrics quantify aspects of a social network. Degree centrality measures how many connections each node has – a high degree indicates influence within the network.

Betweenness centrality identifies nodes that act as bridges, connecting otherwise isolated groups. Clustering coefficient reflects how tightly-knit a group of nodes are connected to each other.

Degree Centrality = 2 * Σ(kᵢ) / N, where kᵢ is the degree of node i and N is the total number of nodes.
live demo · related simulation● LIVE

Simulation Dynamics

Our simulator allows you to introduce dynamic changes to the network. You can add new connections (edges), remove existing ones, or even simulate events like a person joining a group or changing their interests.

These changes propagate through the network based on defined rules – for example, a node’s influence might spread outward from its central position, or new relationships might form based on shared characteristics.

Propagation Rate = f(Node Influence, Edge Weight, Network Density)

Applications & Extensions

Social network analysis has applications in diverse fields, including marketing (identifying influencers), epidemiology (tracking disease spread), and political science (analyzing voting patterns).

Future expansions of the simulator could incorporate concepts like rumor propagation, opinion dynamics, and complex adaptive systems behavior. Experiment with different network topologies to observe emergent properties.

Frequently asked questions

What is a ‘graph’ in this context?

A graph, or network, is a mathematical structure used to represent relationships between objects. It's the fundamental data structure for social network analysis.

Can I create my own social networks?

Absolutely! The simulator provides tools to define nodes and edges, allowing you to build any type of social network you can imagine.

How does the simulation handle large networks?

The simulator utilizes efficient algorithms for graph traversal and analysis, designed to manage relatively large networks effectively.

Try it live

Everything above runs in your browser — open SPH Fluid and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open SPH Fluid simulation

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