What Is Multiple Graph in Data Structure?


Heather Bennett

A multiple graph, also known as a multigraph, is a type of data structure used in computer science and mathematics to represent relationships between entities. In a multiple graph, each entity is represented by a vertex or node, and the relationships between entities are represented by edges or arcs.

Understanding Multiple Graphs

Multiple graphs are different from simple graphs in that they allow multiple edges between the same pair of vertices. This means that two vertices can be connected by more than one edge, and each edge can have its own unique properties or attributes. These attributes could represent different types of relationships, such as different types of connections or weights assigned to the edges.

Key Features:

  • Vertices: The entities being represented in the graph are known as vertices or nodes. Each vertex can have its own unique identifier or label.
  • Edges: The relationships between vertices are represented by edges or arcs.

    In a multiple graph, there can be multiple edges between the same pair of vertices.

  • Attributes: Each edge in a multiple graph can have its own set of attributes that describe the characteristics of the relationship it represents. These attributes can be used to store additional information such as weights, costs, or labels associated with each edge.

Use Cases for Multiple Graphs

Multiple graphs find applications in various fields including network analysis, social network analysis, transportation planning, and computer algorithms. Some common use cases include:

  • Social Networks: Multiple graphs can represent complex social relationships where individuals are connected through various types of interactions such as friendships, collaborations, or family relations.
  • Transportation Networks: In transportation planning, multiple graphs can represent different types of transportation modes (e.g., roads, railways, airways) and their connections.
  • Network Analysis: Multiple graphs are frequently used in network analysis to study the relationships between entities and analyze properties such as connectivity, centrality, or clustering.

Advantages and Disadvantages of Multiple Graphs

Multiple graphs offer several advantages over simple graphs:

  • Increased Expressiveness: Multiple graphs allow for the representation of more complex relationships between entities compared to simple graphs. This increased expressiveness enables a more accurate modeling of real-world scenarios.
  • Flexibility: The ability to attach attributes to each edge provides flexibility in representing additional information associated with the relationships.

However, there are also some disadvantages:

  • Increased Complexity: The presence of multiple edges between vertices can increase the complexity of algorithms that operate on the graph. It requires additional consideration to handle multiple edges during graph traversal or other operations.
  • Potential Redundancy: In some cases, having multiple edges between vertices may lead to redundant information. Care must be taken to ensure that the redundancy does not affect the correctness or efficiency of algorithms.


In summary, a multiple graph is a data structure that allows for multiple edges between vertices. It provides increased expressiveness and flexibility in representing complex relationships and associated attributes. While it introduces some additional complexity and potential redundancy, multiple graphs are widely used in various domains for modeling and analyzing real-world scenarios.

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