Community detection is a fundamental work to analyse the structural and functional properties of complex networks. The label propagation algorithm (LPA) is a near linear time algorithm to find a good community structure. Despite various subsequent advances, an important issue of this algorithm has not yet been properly addressed. Random update orders within the algorithm severely hamper the stability of the identified community structure. In this paper, we executed the basic label propagation algorithm on networks multiple times, to obtain a set of consensus partitions. Based on these consensus partitions, we created a consensus weighted graph. In this consensus weighted graph, the weight value of the edge was the proportion value that the number of node pairs allocated in the same cluster was divided by the total number of partitions. Then, we introduced consensus weight to indicate the direction of label propagation. In label update steps, by computing the mixing value of consensus weight and label frequency, a node adopted the label which has the maximum mixing value instead of the most frequent one. For extending to different networks, we introduced a proportion parameter to adjust the proportion of consensus weight and label frequency in computing mixing value. Finally, we proposed an approach named the label propagation algorithm with consensus weight (LPAcw), and the experimental results showed that the LPAcw could enhance considerably both the stability and the accuracy of community partitions.

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Identifying influential spreaders is an important and fundamental work in control information diffusion. Many methods based on centrality measures such as degree centrality, the betweenness centrality, closeness centrality and eigenvector centrality are proposed in the previous literatures, and it has proved that the shell k decomposition plays overwhelming performance to find influential spreaders in networks. However, as the performance of former three methods is not satisfying enough and shell k decomposition cannot rank nodes in the same core k how to find the influential spreaders is still an open challenge. In this paper, we concerned about the influence of hop neighborhoods on a node and propose a novel metric, shell k values of hop neighborhoods ( NKS ), to estimate the spreading influence of nodes of each shell k in networks. Our experimental resultsshow that the proposed method can quantify the node influence more accurately and provide a more monotonic ranking list than other ranking methods.

In the study of complex networks, scholars have long focused on the identification of influencing nodes. Based on topological information, several quantitative methods for determining the importance of nodes are proposed. K-shell is an efficient way to find potentially affected nodes. However, K-shell overemphasizes the influence of the location of the central node and ignores the influence of the force of the nodes located at the periphery of the network. Furthermore, the topology of real networks is complex, which makes the computation of the K-shell problem for large scale-free networks extremely difficult. In order to avoid ignoring the contribution of any node in the network to the propagation, this paper proposes an improved method based on iteration factor and information entropy to estimate the propagation ability of each layer of nodes. This method not only achieves the accuracy of node ordering, but also effectively avoids the phenomenon of rich clubs. To evaluate the performance of this method, the SIR model is used to simulate the propagation efficiency of each node, and the algorithm is compared with other algorithms. Experimental results show that this method has better performance than other methods and is suitable for large-scale networks.

With the ever greater development of technology of malicious code, malware has been becoming one of the most serious threats to information security. And since the encryption and transformation of program code lead the traditional signature scanning not so instantaneous and effective any longer, researching a new method of detection of malicious code has no time to delay. In recent years, although the technology of detection of malicious code, especially behavior oriented detection, has made a great progress rapidly, it also faces great challenges and problems to be overcome. This paper is a detailed overview of this behavior oriented detection of malicious code including architecture, variety and development tendency of this technology, furthermore it provides the next researcher key points.