Abstract
Understanding and controlling spreading processes in networks is an important topic with many diverse applications, including information dissemination, disease propagation and viral marketing. It is of crucial importance to identify which entities act as influential spreaders that can propagate information to a large portion of the network, in order to ensure efficient information diffusion, optimize available resources or even control the spreading. In this work, we capitalize on the properties of the K-truss decomposition, a triangle-based extension of the core decomposition of graphs, to locate individual influential nodes. Our analysis on real networks indicates that the nodes belonging to the maximal K-truss subgraph show better spreading behavior compared to previously used importance criteria, including node degree and k-core index, leading to faster and wider epidemic spreading. We further show that nodes belonging to such dense subgraphs, dominate the small set of nodes that achieve the optimal spreading in the network.
Highlights
Understanding and controlling spreading processes in networks is an important topic with many diverse applications, including information dissemination, disease propagation and viral marketing
Let T denotes the set of nodes belonging to the maximal K-truss subgraph of the graph
It has been shown that the maximal k-core and K-truss subgraphs overlap, with the latter being a subgraph of the former; the K-truss subgraph represents the most connected part of the corresponding k-core, leading to a significant reduction of the set of nodes with respect to their structural properties and position within the graph
Summary
Understanding and controlling spreading processes in networks is an important topic with many diverse applications, including information dissemination, disease propagation and viral marketing. It is of crucial importance to identify which entities act as influential spreaders that can propagate information to a large portion of the network, in order to ensure efficient information diffusion, optimize available resources or even control the spreading. There exist cases where a node can have arbitrarily high degree, while its neighbors are not well-connected, making degree a not very accurate predictor of the spreading properties This can occur when a high degree node is located to the periphery of the network. The spreading properties of a node are strongly related to the ones of its neighbors in the graph, and global centrality criteria seem to be more appropriate for this task Towards this direction, several approaches have been proposed in the related literature. Lu et al.[9] proposed LeaderRank, a random walk-based algorithm similar to PageRank[10] for identifying influential users in social
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