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+## Graph
+
+Graph is a useful data structure for representing most of the real world problems involving a set of users/candidates/nodes and their relations. A Graph consists of two parameters :
+
+```
+V = a set of vertices
+E = a set of edges
+```
+
+Each edge in `E` connects any two vertices from `V`. Based on the type of edge, graphs can be of two types:
+
+1. **Directed**: The edges are directed in nature which means that when there is an edge from node `A` to `B`, it does not imply that there is an edge from `B` to `A`.
+An example of directed edge graph the **follow** feature of social media. If you follow a celebrity, it doesn't imply that s/he follows you.
+
+2. **Undirected**: The edges don't have any direction. So if `A` and `B` are connected, we can assume that there is edge from both `A` to `B` and `B` to `A`.
+Example: Social media graph, where if two persons are friend, it implies that both are friend with each other.
+
+
+### Representation
+
+1.  **Adjacency Lists**: Each node is represented as an entry and all the edges are represented as a list emerging from the corresponding node. So if vertex `1` has eadges to 2,3, and 6, the list corresponding to 1 will have 2,3 and 6 as entries. Consider the following graph.
+
+```
+0: 1-->2-->3
+1: 0-->2
+2: 0-->1
+3: 0-->4
+4: 3
+```
+It means there are edges from 0 to 1, 2 and 3; from 1 to 0 and 2 and so on.
+2. **Adjacency Matrix**: The graph is represented as a matrix of size `|V| x |V|` and an entry 1 in cell `(i,j)` implies that there is an edge from i to j. 0 represents no edge.
+The mtrix for the above graph:
+
+```
+   0 1 2 3 4
+
+0  0 1 1 1 0
+1  1 0 1 0 0
+2  1 1 0 0 0
+3  1 0 0 0 1
+4  0 0 0 1 0
+```
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