To remove edges from the graph

To find shortest paths

To reduce graph size

To detect groups of densely connected nodes

Learns node embeddings using random walks

Computes shortest paths

Removes noisy nodes

Detects communities

Partitioning graphs

Sorting nodes by degree

Moving randomly from a node to one of its neighbors

Randomly deleting nodes

To detect communities

To remove edges

To cluster nodes

To rank nodes based on their importance in a network

Size of the largest community

The longest shortest path between any two nodes

Number of edges

Average node degree

Graph density

Number of clusters

Number of edges in the graph

How often a node lies on shortest paths between other nodes

To discover useful patterns and knowledge from data

To compress data for storage

To visualize data using charts

To store large volumes of data