Most of entity relationships in the real world cannot be represented by simple binary relations， and hypergraph can represent the n-ary relations among entities well. Therefore， definitions of hypergraph clique and maximal clique were proposed， and the exact algorithm and approximation algorithm for searching hypergraph maximal clique were given. First， the reason why the existing maximal clique searching algorithms on ordinary graphs cannot be applied to hypergraphs directly was analyzed. Then， based on the characteristics of hypergraph and the definition of maximal clique， a novel data structure for preserving the adjacency relations among hyperpoints was proposed， and an accurate maximal clique searching algorithm on hypergraph was proposed. As the running of the exact algorithm is slow， the pruning idea of pivots was combined with， the number of recursive layers was reduced， and an approximation maximal clique searching algorithm on hypergraph was proposed. Experimental results on multiple real hypergraph datasets show that under the premise finding most maximal cliques， the proposed approximation algorithm improves the search speed. When the number of test hypergraph cliques on 3-uniform hypergraph is 22， the acceleration ratio reaches over 1 000.

Attributed graph embedding aims to represent the nodes in an attributed graph into low-dimensional vectors while preserving the topology information and attribute information of the nodes. There are lots of works related to attributed graph embedding. However， most of algorithms proposed in them are supervised or semi-supervised. In practical applications， the number of nodes that need to be labeled is large， which makes these algorithms difficult and consume huge manpower and material resources. Above problems were reanalyzed from an unsupervised perspective， and an unsupervised attributed graph embedding algorithm was proposed. Firstly， the topology information and attribute information of the nodes were calculated respectively by using the existing non-attributed graph embedding algorithm and attributes of the attributed graph. Then， the embedding vector of the nodes was obtained by using Graph Convolutional Network （GCN）， and the difference between the embedding vector and the topology information and the difference between the embedding vector and attribute information were minimized. Finally， similar embeddings was obtained by the paired nodes with similar topological information and attribute information. Compared with Graph Auto-Encoder （GAE） method， the proposed method has the node classification accuracy improved by 1.2 percentage points and 2.4 percentage points on Cora and Citeseer datasets respectively. Experimental results show that the proposed method can effectively improve the quality of the generated embedding.