Weighted k-shell decomposition for complex networks based on potential edge weights

Wei, Bo; Liu, Jie; Wei, Daijun; Gao, Cai; Deng, Yong

doi:10.1016/j.physa.2014.11.012

Cited by 82 publications

(35 citation statements)

References 53 publications

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“…It assumes that the weight of a link from node u to v equals

\frac{1}{N (v)}

, where N ( v ) is the number of neighbors of the uninfected node v . In each iteration, both IC method and WC method add one node to the list of initiators. K‐shell (KS) , weighted K‐shell (WS) , GV, gravity plus (G+) , LeaderRank , and weighted LeaderRank (WL) : These are centrality‐based methods. The influence of a node is measured by the KS value (for KS and WS), gravity value (for GV and G+), and leader scores (for LR and WL).…”

Section: Resultsmentioning

confidence: 99%

Identifying effective initiators in OSNs: from the spectral radius perspective

Chen

et al. 2016

Wireless Communications

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In this paper, we focus on maximizing the influence of online social networks (OSNs). Particularly, we try to answer how to select proper information initiators such that information can propagate as widely as possible. We stress our attention on the susceptible-infected model, a type of epidemic models, to describe the process of information diffusion. In general, OSNs can be classified into two categories, Facebook-like OSNs and Twitter-like OSNs. The former ones require bidirectional connections, while the latter do not, so we use the undirected unweighted graph and directed unweighted graph to describe them, respectively. We also pay additional attention to the nonidentity of the link probability on information transmission and build the weight graph, which can also cover both the two types of OSNs. In order to determine values of weight graph's weights, we introduce a learning method to obtain useful factors from raw data for assessing the true link probability on information transmission. Based on spectral analysis within the three graphs, our investigations on the information diffusion show that the spectral radius of the graph adjacency matrix can reflect the capability of information propagation, according to which we could determine effective initiators. We conduct our simulations on real OSNs. Experimental results show that our approach could effectively discover the initiators that spread information widely.

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“…It assumes that the weight of a link from node u to v equals

\frac{1}{N (v)}

Section: Resultsmentioning

confidence: 99%

Identifying effective initiators in OSNs: from the spectral radius perspective

Chen

et al. 2016

Wireless Communications

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“…Being able to work directly on weighted networks would allow for the integration of the number of interactions occurring between a given pair of individuals. Because biological processes involving information transmission are likely to rely heavily on repeated interactions between animals ( Wei et al, 2015 ), this improvement would certainly provide even more insight into the mechanisms regulating such processes.…”

Section: Discussionmentioning

confidence: 99%

Understanding Dynamics of Information Transmission in Drosophila melanogaster Using a Statistical Modeling Framework for Longitudinal Network Data (the RSiena Package)

et al. 2016

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Social learning – the transmission of behaviors through observation or interaction with conspecifics – can be viewed as a decision-making process driven by interactions among individuals. Animal group structures change over time and interactions among individuals occur in particular orders that may be repeated following specific patterns, change in their nature, or disappear completely. Here we used a stochastic actor-oriented model built using the RSiena package in R to estimate individual behaviors and their changes through time, by analyzing the dynamic of the interaction network of the fruit fly Drosophila melanogaster during social learning experiments. In particular, we re-analyzed an experimental dataset where uninformed flies, left free to interact with informed ones, acquired and later used information about oviposition site choice obtained by social interactions. We estimated the degree to which the uninformed flies had successfully acquired the information carried by informed individuals using the proportion of eggs laid by uninformed flies on the medium their conspecifics had been trained to favor. Regardless of the degree of information acquisition measured in uninformed individuals, they always received and started interactions more frequently than informed ones did. However, information was efficiently transmitted (i.e., uninformed flies predominantly laid eggs on the same medium informed ones had learn to prefer) only when the difference in contacts sent between the two fly types was small. Interestingly, we found that the degree of reciprocation, the tendency of individuals to form mutual connections between each other, strongly affected oviposition site choice in uninformed flies. This work highlights the great potential of RSiena and its utility in the studies of interaction networks among non-human animals.

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“…To strengthen the case for AS, we performed a number of analyses beside those found in the main text. First we compare how AS fares against a fairly recent immunization strategy called k-core (hereafter KCS) (Kitsak et al, 2010;Wei et al, 2015). A conceptual advantage of AS in comparison with KCS is that the latter strategy needs global knowledge to decompose a network and then identify the most influential nodes.…”

Section: Discussionmentioning

confidence: 99%

A biologically inspired immunization strategy for network epidemiology

Liu

Deng

Jusup

et al. 2016

Journal of Theoretical Biology

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Well-known immunization strategies, based on degree centrality, betweenness centrality, or closeness centrality, either neglect the structural significance of a node or require global information about the network. We propose a biologically inspired immunization strategy that circumvents both of these problems by considering the number of links of a focal node and the way the neighbors are connected among themselves. The strategy thus measures the dependence of the neighbors on the focal node, identifying the ability of this node to spread the disease. Nodes with the highest ability in the network are the first to be immunized. To test the performance of our method, we conduct numerical simulations on several computer-generated and empirical networks, using the susceptible-infected-recovered (SIR) model. The results show that the proposed strategy largely outperforms the existing well-known strategies.

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Weighted k-shell decomposition for complex networks based on potential edge weights

Cited by 82 publications

References 53 publications

Identifying effective initiators in OSNs: from the spectral radius perspective

Identifying effective initiators in OSNs: from the spectral radius perspective

Understanding Dynamics of Information Transmission in Drosophila melanogaster Using a Statistical Modeling Framework for Longitudinal Network Data (the RSiena Package)

A biologically inspired immunization strategy for network epidemiology

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