Introduction to Graph Theory - Networks, Flows and Cut Edges

Introduction to Graph Theory – Networks, Flows and Cut Edges

Basic Concepts and Definitions

– Graph: mathematical structure consisting of vertices and edges

– Vertex: fundamental unit representing entity, also called node

– Edge: connection between two vertices 

– Path: sequence of vertices connected by edges 

– Cycle: path that starts and ends at same vertex

– Connected graph: path exists between all vertex pairs  

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Networks and Graphs 

– Networks as graphs: systems mapped to vertices and interactions as edges

– Social networks, information networks, infrastructure networks

– Weighted graphs model interaction strengths  

– Directed graphs have asymmetric relationships

– Adjacency matrix compactly represents network structure

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Paths, Walks, and Flows

– Finding shortest paths essential for transport and routing 

– Flows model transmission through vertex sequences 

– Max flow algorithms optimize transport capacity  

– Random walk analysis reveals network accessibility

Cut Edges and Connectivity

– Cut edges divide graphs by removing key connections

– Minimum cut set identifies vulnerability points  

– Connectivity metrics quantify redundancy  

– Improving connectivity increases robustness

Advanced Topics 

– Spectral graph theory studies eigenvectors and eigenvalues

– Graph neural networks apply deep learning techniques 

– Community detection finds densely connected subgroups

What is the definition of a graph in graph theory?

A graph G consists of a set V of vertices (also called nodes or points) and a set E of edges, which are connections between pairs of vertices. Mathematically, this can be written as:

G = (V, E)


– V is a finite, non-empty set of vertices

– E is a set of edges, defined as pairs of vertices {u,v} where u and v are members of V. 

Some key notes on graphs:

– Edges may be directed or undirected. Undirected edges link vertices symmetrically while directed edges link them asymmetrically.

– Graphs with no loops or parallel edges are called simple graphs. Multigraphs allow for multiple edges between vertices.

– Connected graphs have paths linking each vertex pair. Disconnected graphs do not.

– Additional data or labels can be associated with vertices and edges to represent attributes of real-world systems as networks.

So in summary, a graph models relationships by connecting objects represented as an abstract set of vertices via edges in a defined mathematical structure. This allows the application of theoretical analysis techniques.

What is the difference between a graph and a tree in graph theory?

– A tree is a specialized, restricted form of a graph. All trees are graphs but not all graphs are trees.

– A tree contains no cycles or loops, so there is exactly one path between any two vertices. A graph can have cycles and multiple paths between vertices.

– A tree has a hierarchical structure with a root node, parent-child relationships, and defined depths. A graph has no fixed hierarchy and is a more general network structure.

– Trees can be traversed recursively using depth-first search and breadth-first search. Graphs require iterative traversal methods like Dijkstra’s algorithm.

– Trees are often used to implement abstract data types and hierarchical data. Graphs model networks and relationships in complex real-world systems.

In summary, a tree imposes additional constraints on the connections between elements compared to a general graph structure. This enables certain efficient algorithms but reduces flexibility in modeling arbitrary networks.

How are trees and graphs used in network analysis

– Represent hierarchical data like organization structures, file systems, etc. Their parent-child structure models relationships.

– Enable efficient searching and sorting algorithms to quickly find data.

– Implement optimized network routing schemes and communication protocols.


– Model complex real-world systems and relationships as networks, like social networks, infrastructure networks, etc.

– Analyze network structure and properties using graph theory metrics like connectivity, centrality, community detection.

– Find optimal paths and flows for transport, routing, scheduling problems on networks using algorithms like shortest path, maximum flow. 

– Assess robustness and vulnerability of networks by analyzing cut edges/vertices and connectivity. [4][9]

– Extract spanning trees as simplified models preserving connectivity in complex networks.

In summary, trees impose hierarchy while graphs provide flexibility in analyzing diverse networks and relationships between elements. Together they provide complementary techniques for modeling and optimizing networked systems.


Graph theory abstracts complex systems into intuitive visual models amenable to mathematical analysis. Both theoretical and data-driven techniques reveal key structural and dynamical properties. Diverse real-world networks share fundamental organizing principles independent of their specific domains.

Let me know if you would like me to expand or provide more details on any part of this outline. I aimed to cover the requested main topics at a high level while showcasing some representative applications and analysis techniques.

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