Graph kernel decomposition method based on h-hop distance

The invention discloses a graph kernel decomposition method based on an h-hop distance, and the method comprises the steps: obtaining a to-be-decomposed big data original graph G, calculating the h-hop neighbor data of each node in the original graph G, traversing the whole original graph G to find the minimum value of the h-hop neighbors, assigning k to the minimum value, and putting all nodes with the number of the h-hop neighbors being k into a queue Q, sequentially selecting a node v from Q, deleting the node v from G and Q, when one node v is deleted, updating the number of h-hop neighbors of all nodes in the h-hop neighbors of the node v, and iteratively deleting the node with the least h-hop neighbors until all nodes are deleted. Compared with the prior art, the method does not needto repeatedly calculate the h-hop neighbors of the nodes, the calculation efficiency is higher, and the algorithm design is simple and easy to implement. 本发明公开了一种基于h-跳距离的图核分解方法，包括获取待分解的大数据原图G，计算原图G中每个节点的h-跳邻居数据；遍历整个原图G中找到h-跳