560 research outputs found
Path diversity improves the identification of influential spreaders
Identifying influential spreaders in complex networks is a crucial problem
which relates to wide applications. Many methods based on the global
information such as -shell and PageRank have been applied to rank spreaders.
However, most of related previous works overwhelmingly focus on the number of
paths for propagation, while whether the paths are diverse enough is usually
overlooked. Generally, the spreading ability of a node might not be strong if
its propagation depends on one or two paths while the other paths are dead
ends. In this Letter, we introduced the concept of path diversity and find that
it can largely improve the ranking accuracy. We further propose a local method
combining the information of path number and path diversity to identify
influential nodes in complex networks. This method is shown to outperform many
well-known methods in both undirected and directed networks. Moreover, the
efficiency of our method makes it possible to be applied to very large systems.Comment: 6 pages, 6 figure
Dirac Fermion in Strongly-Bound Graphene Systems
It is highly desirable to integrate graphene into existing semiconductor
technology, where the combined system is thermodynamically stable yet maintain
a Dirac cone at the Fermi level. Firstprinciples calculations reveal that a
certain transition metal (TM) intercalated graphene/SiC(0001), such as the
strongly-bound graphene/intercalated-Mn/SiC, could be such a system. Different
from free-standing graphene, the hybridization between graphene and Mn/SiC
leads to the formation of a dispersive Dirac cone of primarily TM d characters.
The corresponding Dirac spectrum is still isotropic, and the transport behavior
is nearly identical to that of free-standing graphene for a bias as large as
0.6 V, except that the Fermi velocity is half that of graphene. A simple model
Hamiltonian is developed to qualitatively account for the physics of the
transfer of the Dirac cone from a dispersive system (e.g., graphene) to an
originally non-dispersive system (e.g., TM).Comment: Apr 25th, 2012 submitte
Evolution characteristics of the network core in the facebook
Statistical properties of the static networks have been extensively studied. However, online social networks are evolving dynamically, understanding the evolving characteristics of the core is one of major concerns in online social networks. In this paper, we empirically investigate the evolving characteristics of the Facebook core. Firstly, we separate the Facebook-link(FL) and Facebook-wall(FW) datasets into 28 snapshots in terms of timestamps. By employing the k-core decomposition method to identify the core of each snapshot, we find that the core sizes of the FL and FW networks approximately contain about 672 and 373 nodes regardless of the exponential growth of the network sizes. Secondly, we analyze evolving topological properties of the core, including the k-core value, assortative coefficient, clustering coefficient and the average shortest path length. Empirical results show that nodes in the core are getting more interconnected in the evolving process. Thirdly, we investigate the life span of nodes belonging to the core. More than 50% nodes stay in the core for more than one year, and 19% nodes always stay in the core from the first snapshot. Finally, we analyze the connections between the core and the whole network, and find that nodes belonging to the core prefer to connect nodes with high k-core values, rather than the high degrees ones. This work could provide new insights into the online social network analysis
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