114 research outputs found
Identifying effective multiple spreaders by coloring complex networks
How to identify influential nodes in social networks is of theoretical
significance, which relates to how to prevent epidemic spreading or cascading
failure, how to accelerate information diffusion, and so on. In this Letter, we
make an attempt to find \emph{effective multiple spreaders} in complex networks
by generalizing the idea of the coloring problem in graph theory to complex
networks. In our method, each node in a network is colored by one kind of color
and nodes with the same color are sorted into an independent set. Then, for a
given centrality index, the nodes with the highest centrality in an independent
set are chosen as multiple spreaders. Comparing this approach with the
traditional method, in which nodes with the highest centrality from the
\emph{entire} network perspective are chosen, we find that our method is more
effective in accelerating the spreading process and maximizing the spreading
coverage than the traditional method, no matter in network models or in real
social networks. Meanwhile, the low computational complexity of the coloring
algorithm guarantees the potential applications of our method.Comment: 6 pages, 6 figure
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Identifying Multiple Influential Users Based on the Overlapping Influence in Multiplex Networks
Online social networks (OSNs) are interaction platforms that can promote knowledge spreading, rumor propagation, and virus diffusion. Identifying influential users in OSNs is of great significance for accelerating the information propagation especially when information is able to travel across multiple channels. However, most previous studies are limited to a single network or select multiple influential users based on the centrality ranking result of each user, not addressing the overlapping influence (OI) among users. In practice, the collective influence of multiple users is not equal to the total sum of these users' influences. In this paper, we propose a novel OI-based method for identifying multiple influential users in multiplex social networks. We first define the effective spreading shortest path (ESSP) by utilizing the concept of spreading rate in order to denote the relative location of users. Then, the collective influence is quantified by taking the topological factor and the location distribution of users into account. The identified users based on our proposed method are central and relatively scattered with a low overlapping influence. With the Susceptible-Infected-Recovered (SIR) model, we estimate our proposed method with other benchmark algorithms. Experimental results in both synthetic and real-world networks verify that our proposed method has a better performance in terms of the spreading efficiency. © 2013 IEEE
Influencer Identification on Link Predicted Graphs
How would admissions look like in a it university program for influencers? In
the realm of social network analysis, influence maximization and link
prediction stand out as pivotal challenges. Influence maximization focuses on
identifying a set of key nodes to maximize information dissemination, while
link prediction aims to foresee potential connections within the network. These
strategies, primarily deep learning link prediction methods and greedy
algorithms, have been previously used in tandem to identify future influencers.
However, given the complexity of these tasks, especially in large-scale
networks, we propose an algorithm, The Social Sphere Model, which uniquely
utilizes expected value in its future graph prediction and combines
specifically path-based link prediction metrics and heuristic influence
maximization strategies to effectively identify future vital nodes in weighted
networks. Our approach is tested on two distinct contagion models, offering a
promising solution with lower computational demands. This advancement not only
enhances our understanding of network dynamics but also opens new avenues for
efficient network management and influence strategy development.Comment: 19 pages + appendix. V2 has additional information on how our model
differs from existing algorithm
A voting approach to uncover multiple influential spreaders on weighted networks
The identifcation of multiple spreaders on weighted complex networks is a crucial step towards effcient information diffusion, preventing epidemics spreading and etc. In this paper, we propose a novel approach WVoteRank to find multiple spreaders by extending VoteRank. VoteRank has limitations to select multiple spreaders on unweighted networks while various real networks are weighted networks such as trade networks, traffic flow networks and etc. Thus our approach WVoteRank is generalized to deal with both unweighted and weighted networks by considering both degree and weight in voting process. Experimental studies on LFR synthetic networks and real networks show that in the context of Susceptible-Infected-Recovered (SIR) propagation, WVoteRank outperforms existing states of arts methods such as weighted H-index, weighted K-shell, weighted degree centrality and weighted betweeness centrality on final affected scale. It obtains an improvement of final affected scale as much as 8:96%. Linear time complexity enables it to be applied on large networks effectively
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