24,870 research outputs found
Identifying Influential Nodes in Bipartite Networks Using the Clustering Coefficient
The identification of influential nodes in complex network can be very
challenging. If the network has a community structure, centrality measures may
fail to identify the complete set of influential nodes, as the hubs and other
central nodes of the network may lie inside only one community. Here we define
a bipartite clustering coefficient that, by taking differently structured
clusters into account, can find important nodes across communities
Locating influential nodes via dynamics-sensitive centrality
With great theoretical and practical significance, locating influential nodes
of complex networks is a promising issues. In this paper, we propose a
dynamics-sensitive (DS) centrality that integrates topological features and
dynamical properties. The DS centrality can be directly applied in locating
influential spreaders. According to the empirical results on four real networks
for both susceptible-infected-recovered (SIR) and susceptible-infected (SI)
spreading models, the DS centrality is much more accurate than degree,
-shell index and eigenvector centrality.Comment: 6 pages, 1 table and 2 figure
A General Framework for Complex Network Applications
Complex network theory has been applied to solving practical problems from
different domains. In this paper, we present a general framework for complex
network applications. The keys of a successful application are a thorough
understanding of the real system and a correct mapping of complex network
theory to practical problems in the system. Despite of certain limitations
discussed in this paper, complex network theory provides a foundation on which
to develop powerful tools in analyzing and optimizing large interconnected
systems.Comment: 8 page
- …