Identifying influential nodes in complex networks: A multiple attributes fusion method

How to identify influential nodes is still an open hot issue in complex networks. Lots of methods (e.g., degree centrality, betweenness centrality or K-shell) are based on the topology of a network. These methods work well in scale-free networks. In order to design a universal method suitable for networks with different topologies, this paper proposes a Multiple Attribute Fusion (MAF) method through combining topological attributes and diffused attributes of a node together. Two fusion strategies have been proposed in this paper. One is based on the attribute union (FU), and the other is based on the attribute ranking (FR). Simulation results in the Susceptible-Infected (SI) model show that our proposed method gains more information propagation efficiency in different types of networks. © 2014 Springer International Publishing.

Identifying influential nodes in complex networks for network immunization

Identifying influential nodes is of theoretical significance in network immunization which is one of important methods to prevent virus propagation through protecting the influential nodes in a network. Lots of methods have been proposed to find these influential nodes based on the topological characteristics of a network (e.g., degree, betweenness or K-shell). Whereas due to the diversity of network topologies, these methods are not always effective in identifying influential nodes in any benchmark networks. We combine the advantages of existing methods based on attribute ranking and propose a universal ranking method, namely MAF (Multiple Attribute Fusion), to identify influential nodes from a complex network. We compare the efficiency of our proposed method with existing immunization strategies in different types of networks. Simulation results in the interactive email model show that the immunized nodes selected by MAF can restrain virus propagation effectively.

Atom probe tomography investigation of heterogeneous short-range ordering in the 'komplex' phase state (K-state) of Fe-18Al (at.%)

We study an Fe-18Al (at.%) alloy after various thermal treatments at different times (24-336 h) and temperatures (250-1100 °C) to determine the nature of the so-called 'komplex' phase state (or "K-state"), which is common to other alloy systems having compositions at the boundaries of known order-disorder transitions and is characterised by heterogeneous short-range-ordering (SRO). This has been done by direct observation using atom probe tomography (APT), which reveals that nano-sized, ordered regions/particles do not exist. Also, by employing shell-based analysis of the three-dimensional atomic positions, we have determined chemically sensitive, generalised multicomponent short-range order (GM-SRO) parameters, which are compared with published pairwise SRO parameters derived from bulk, volume-averaged measurement techniques (e.g. X-ray and neutron scattering, Mössbauer spectroscopy) and combined ab-initio and Monte Carlo simulations. This analysis procedure has general relevance for other alloy systems where quantitative chemical-structure evaluation of local atomic environments is required to understand ordering and partial ordering phenomena that affect physical and mechanical properties.