The traditional algorithms are difficult to implement the Top- K subgraph query on large-scale dynamic labeled graph due to high time or space complexity. For this reason, a dynamic Top- K interesting subgraph query method named DISQtop- K was proposed. In this algorithm, a Graph Topology Structure Feature (GTSF) index that include Node Topology Feature (NTF) index and Edge Feature (EF) index was established, which can effectively prune and filter the invalid nodes and edges. Then a multi-factor candidate set filtering strategy was put forward based on GTSF index, which can be used to further prune the query graph candidate sets. Considering that the dynamic changes in the graph may have an impact on the matching results, to ensure the real-time and accuracy of the query results, a new matching-verification method for Top- K interesting subgraph was also given, which has two stages of initial matching and dynamic correction. Experimental results show that compared with RAM and RWM, DISQtop- K method costs shorter time for index creation and occupies less space, which can effectively deal with dynamic Top- K interesting subgraph query on large-scale labeled graph.

In the evolution of dynamic graph topology, in order to quantify the change of the relation between the nodes within a certain time, a concept, namely ubiquitous similarity node, was defined, and the level of ubiquitous similarity with the current node was measured by the frequent degree of interaction with the current node and the uniformity of distribution, and a similarity node query processing algorithm for large dynamic graph based on the snapshots was proposed. The concrete content includes: the snapshot expression of the dynamic evolution of graph, namely evolution dynamic graph; the semantic representation and its formal representation of the nodes' ubiquitous similarity in the dynamic evolution of graph, which was characterized by the frequent degree of interaction and uniformity coefficient of distribution; the matrix representation and processing method of the semantic of the nodes' ubiquitous similarity; the query algorithm for ubiquitous similarity nodes. The experimental results on the synthetic dataset and the real dataset show that the proposed algorithm can deal with the nodes' ubiquitous similarity query on the large dynamic graph, and be implemented in the practical applications.