The interaction between entities in complex systems is vividly described by multiplex networks， and motifs frequently appear in networks as a higher-order structure. Compared with single-layer motifs， multiplex motifs have the characteristics of large quantity， diverse types， and complicated structure. Given the current lack of complete detection algorithm for multiplex motifs， a Fast Algorithm for Multiplex Motif Detection （FAMMD） suitable for multiplex networks was proposed. Firstly， an improved ESU （Enumerate SUbgraphs） algorithm was used to enumerate multiplex subgraphs. Then a method combining layer markers and binary strings was used for accelerating the process of isomorphism detection， and a null model that preserved degree sequences and inter-layer dependencies was constructed for multiplex subgraph testing. Finally， motif detection was performed on two-layer real networks. Multiplex motifs exhibited a closely connected triple mode， and they were more homogeneous in social networks while more complementary in transportation networks. Experimental results show that the proposed method can accurately and quickly detect multiplex motifs that reflect the structure characteristics of the network and conform the actual situation.

The k-step reachability query is used to answer whether there exists a path between two nodes with length no longer than k in a Directed Acyclic Graph （DAG）. Concerning the problems of large index size and low query processing efficiency of existing approaches， a bi-directional shortest path index based on partial nodes was proposed to improve the coverage of reachable queries， and a set of optimization rules was proposed to reduce the index size. Then， a bi-directional reversed topological index was proposed to accelerate the unreachable queries answering based on the simplified graph. Finally， the farthest-node-first-visiting bi-traversal strategy was proposed to improve the efficiency of query processing. Experimental results on 21 real datasets， such as citation networks and social networks， show that compared with existing efficient approaches including PLL （Pruned Landmark Labeling） and BFSI-B （Breadth First Search Index-Bilateral）， the proposed algorithm has smaller index size and higher query response speed.

In view of the low delivery ratio of conventional probabilistic routing in opportunistic networks, an improved routing algorithm based on History Meeting Predictability Routing (HMPR) was put forward. The algorithm was primarily based on the contact duration and the meeting frequency of history information of nodes, and predicted the utility of packets successfully delivered to the destination. Through comparing the utility value, nodes could determine packets whether to be forwarded from them to next hop nodes. The simulation results show that, compared with traditional epidemic routing and probabilistic routing, the proposed routing scheme has better performance in the delivery ratio of packets, the average delay time and the average buffer time.

The existing antivirus methods take too much system overhead, consume a lot of network bandwidth and can not detect the unknown programs in time. Therefore, this paper improved the previous work and presented a new virtual desktop antivirus model regarding virtual desktop infrastructure. It supported active antivirus and passive antivirus moves. Privileged virtual machines were used to scan viruses, manage the trust-list and transmit signatures of every virtual machine to others. Agents were used to analyze the signatures and characteristics of files, optimize the bytes to be uploaded and scanned, and scan the programs timely when being loaded. The experimental results show that model can detect viruses in real-time, in the meantime reduce system overhead and network bandwidth usage.