Daijun Wei

Assessment of E-Commerce security using AHP and evidential reasoning

In the development of E-Commerce, security has always been the core and key issue. In this paper, a new model is proposed to assist E-Commerce practitioners in the assessment of E-Commerce security. The proposed model is based on Analytical Hierarchy Process (AHP) and Dempster-Shafer (DS) theory of evidence. First, according to the characteristics of E-Commerce, a hierarchical structure of E-Commerce security is established to calculate the weights of relevant issues using AHP. Then Dempster-Shafer theory of evidence is applied to combine all the issues, regarded as evidences, in order to derive a consensus decision for the degree of E-Commerce security. An illustrative example is given to show the efficiency of our model.

Box-covering algorithm for fractal dimension of weighted networks

Box-covering algorithm is a widely used method to measure the fractal dimension of complex networks. Existing researches mainly deal with the fractal dimension of unweighted networks. Here, the classical box covering algorithm is modified to deal with the fractal dimension of weighted networks. Box size length is obtained by accumulating the distance between two nodes connected directly and graph-coloring algorithm is based on the node strength. The proposed method is applied to calculate the fractal dimensions of the “Sierpinski” weighted fractal networks, the E.coli network, the Scientific collaboration network, the C.elegans network and the USAir97 network. Our results show that the proposed method is efficient when dealing with the fractal dimension problem of complex networks. We find that the fractal property is influenced by the edge-weight in weighted networks. The possible variation of fractal dimension due to changes in edge-weights of weighted networks is also discussed.

A new information dimension of complex networks

Abstract The fractal and self-similarity properties are revealed in many complex networks. The classical information dimension is an important method to study fractal and self-similarity properties of planar networks. However, it is not practical for real complex networks. In this Letter, a new information dimension of complex networks is proposed. The nodes number in each box is considered by using the box-covering algorithm of complex networks. The proposed method is applied to calculate the fractal dimensions of some real networks. Our results show that the proposed method is efficient when dealing with the fractal dimension problem of complex networks.

SELF-SIMILARITY IN COMPLEX NETWORKS: FROM THE VIEW OF THE HUB REPULSION

Complex networks are widely used to model the structure of many complex systems in nature and society. Recently, fractal and self-similarity of complex networks have attracted much attention. It is observed that hub repulsion is the key principle that leads to the fractal structure of networks. Based on the principle of hub repulsion, the metric in complex networks is redefined and a new method to calculate the fractal dimension of complex networks is proposed in this paper. Some real complex networks are investigated and the results are illustrated to show the self-similarity of complex networks.

Identifying influential nodes based on local dimension

How to identify influential nodes in complex networks is still an open issue. In this paper, we propose a novel method to identify influential nodes based on the local dimension (LD) of each node, where low LD values are suggestive of high influence. Applied to four real networks, our method has been demonstrated to have a comparable ability of identifying influential nodes with other commonly used methods. Furthermore, our method performs much better than the k-shell decomposition method, especially in the network with community structure. It can not only identify the influential nodes but also subdivide the nodes in the innermost layers.

Degree centrality based on the weighted network

Node centrality has been widely studied in the complex networks. In 2010, the model of node centrality under the weighted network was obtained by Tore Opashl et al. Tie weights and the number of ties were connected with certain proportion by tuning parameter in the model. However, the proportion is random measure. In this paper, the selection standard of the optimal turning parameters is proposed. In the proposed method, the maximum degree centrality of node can be emphasized. The numerical example of weighted network on optimal value selection is used to show the efficiency of the method.

Multi-attribute decision-making method based on interval-valued intuitionistic fuzzy sets and D-S theory of evidence

The theory of interval-valued intuitionistic fussy sets is now widely studied to deal with vagueness and D-S theory of evidence has a widespread use in multi-attribute decision-making (MADM) problems under uncertain situation. In this paper, A new method based on interval-valued intuitionistic fuzzy sets and D-S theory of evidence is proposed to handle MADM problems. In our method, the interval-valued intuitionistic fuzzy numbers are represented by the interval average numbers. These average numbers are assigned to corresponding basic probability assignment (bpa) based on discounting method. Then the D-S combination rule is used to fuse information in order to obtain final mass functions for each alternative, thus the order of each alternative is obtained. A numerical example is used to illustrate the efficiency of the proposed method.

An amoeboid algorithm for shortest path in fuzzy weighted networks

Taking the uncertainty existing in edge weights of networks into consideration, finding shortest path in such fuzzy weighted networks has been widely studied in various practical applications. In this paper, an amoeboid algorithm is proposed, combing fuzzy sets theory with a path finding model inspired by an amoeboid organism, Physarum polycephalum. With the help of fuzzy numbers, uncertainty is well represented and handled in our algorithm. Whats more, biological intelligence of Physarum polycephalum has been incorporate into the algorithm. A numerical example on a transportation network is demonstrated to show the efficiency and flexibility of our proposed amoeboid algorithm.

Uncertain information clustering based on distance between BPAs

It is necessary to cluster the information according to their sources when analyzing multi-source information. In this paper, a new evidential clustering method is proposed. In the proposed method, pairwise distance between BPAs have been introduced to form a matrix for clustering. The clustering method is based on vector which is transformed from distance matrix. Illustrative example with several sets demonstrate the validity of the proposed method as compared to other methods.

Measuring the vulnerability of community structure in complex networks

Abstract This paper develops a quantitative method to measure the vulnerability of community structure with emphasis on both internal and external connectivity characteristics of the community. In particular, the number of links between communities and the strength of links connecting two communities are considered as external factors, while the connection density, the degree of gateway nodes, as well as the strength of links within each community are treated as internal factors. A non-linear weighted function is used to combine the internal factors with external factors. Then the developed method is used to illustrate the vulnerability analysis of community structure of a power transmission grid, a karate club network, and an air transportation network. The results reveal that the proposed measure is effective in differentiating the vulnerability level of community structure in a variety of networks.