As a type of data structure representing entities, graphs are widely used in fields that have high requirements on data relevance, such as community data discovery, biochemical analysis and social security analysis. Focusing on the issue of real-time graph query operation under large-scale data, building a suitable index can effectively reduce query response time and improve query accuracy. The basic structure of index-based subgraph query algorithm was firstly introduced and then state-of-the-art algorithms were divided into two categories by construction method of index:enumeration construction and frequent pattern mining construction. Then these algorithms were introduced and analyzed from three aspects:index features, index structures and application datasets. Finally, main problems toward index-based subgraph query algorithm were summarized and analyzed, the latest query technology based on the distributed system was briefly described, and the future trend was forecasted.

Concerning the low efficiency of identity-based undeniable signature schemes, a new identity-based undeniable signature scheme was proposed. Under the assumption that it is hard to solve the Computational Bilinear Diffie-Hellman (CBDH) problem and the Decisional Bilinear Diffie-Hellman (DBDH) problem, the proposed scheme was proven to be unforgeable and invisible in the random oracle model, and it reduced the number of bilinear pairing operations. Analysis shows that the proposed scheme is more efficient than undeniable signature schemes proposed by Libert, Duan and Behnia, and it is more suitable for the computation-constrained environment.

With the rapid development of IP networks, network traffic becomes increasingly uncertain and unpredictable. In order to resolve this problem, this paper presented a Mixed Integer Programming (MIP) model for IGP/MPLS hybrid IP traffic planning problem under uncertain traffic matrices based on Hose model. Then, the MIP model was decomposed into two sub-problems of weight design and traffic distribution, so that it could be solved effectively. The experimental results demonstrate that the proposed method can obtain a better optimization performance with only a few established Label Switching Paths (LSPs).

asis Pursuit is a novel method of signal sparse representation.Based on the core idea of Basis Pursuit and the modification of its algorithm,we extended the application of the Basis Pursuit method to image de-noising.Aiming at the hardship to solve one kind large-scale linear programming problem of Basis Pursuit,certain kind form of separable signal representation was considered.We construct dictionaries in column-wise and row-wise of the image,the dimension of the dictionary was greatly reduced.Then two-dimension problem was turning into two one-dimension problem.Finally,one new iterative method was presented to solve this problem.Thus the limitation of computation burden iswas well solved.Experiment results demonstrate that Basis Pursuit can be implemented speedy and stably,it can provide well de-noising results at the same time.