Knowledge-based graph compression using graph property on Yago

It is common to find graphs with millions of nodes and edges in social networks, computer network, and knowledge bases. A number of edges and nodes that make the computational complexity becomes large beside it the number of nodes and edges that are not important difficult for us to make the process of patterning graph. We propose a compression method to eliminate unimportant nodes and edges becomes a property of the important nodes. We developed an algorithm graph property compression (GPC) to compress the compression property on the yago knowledge-based. The results of research conducted on yago compression can reduce a 42.45 % node and 47.66% edge. It is effective to reduce the size of the graph primarily unimportant node and edge.