Identifying critical nodes in temporal networks by network embedding

Yu, En-Yu; Fu, Yan; Chen, Xiao; Xie, Mei; Chen, Duanbing

doi:10.1038/s41598-020-69379-z

Cited by 20 publications

(11 citation statements)

References 42 publications

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“…Note that our approach had a specific focus on identifying an agent’s spreading potential, while many other related approaches exist, each with own scope and (dis)advantages 39 . For example, Yu et al (2020) combines network embedding with machine learning to assess a node’s importance in terms of spreading as evaluated by a SIR-model, which contrasts with our approach relying only on topology 43 .…”

Section: Discussionmentioning

confidence: 99%

Quantifying agent impacts on contact sequences in social interactions

Dekker

Blanken

Dablander

et al. 2022

Sci Rep

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Human social behavior plays a crucial role in how pathogens like SARS-CoV-2 or fake news spread in a population. Social interactions determine the contact network among individuals, while spreading, requiring individual-to-individual transmission, takes place on top of the network. Studying the topological aspects of a contact network, therefore, not only has the potential of leading to valuable insights into how the behavior of individuals impacts spreading phenomena, but it may also open up possibilities for devising effective behavioral interventions. Because of the temporal nature of interactions—since the topology of the network, containing who is in contact with whom, when, for how long, and in which precise sequence, varies (rapidly) in time—analyzing them requires developing network methods and metrics that respect temporal variability, in contrast to those developed for static (i.e., time-invariant) networks. Here, by means of event mapping, we propose a method to quantify how quickly agents mingle by transforming temporal network data of agent contacts. We define a novel measure called contact sequence centrality, which quantifies the impact of an individual on the contact sequences, reflecting the individual’s behavioral potential for spreading. Comparing contact sequence centrality across agents allows for ranking the impact of agents and identifying potential ‘behavioral super-spreaders’. The method is applied to social interaction data collected at an art fair in Amsterdam. We relate the measure to the existing network metrics, both temporal and static, and find that (mostly at longer time scales) traditional metrics lose their resemblance to contact sequence centrality. Our work highlights the importance of accounting for the sequential nature of contacts when analyzing social interactions.

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Section: Discussionmentioning

confidence: 99%

Quantifying agent impacts on contact sequences in social interactions

Dekker

Blanken

Dablander

et al. 2022

Sci Rep

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“…Besides, the continued growth of the network scale and high-dimensional node attributes put forward higher requirements for the efficiency and scalability of community detection algorithms in attributed networks. Inspired by the significant progresses in graph-embedding 42 , graph-embedding based community detection came into view in recent years. AANE 43 computed the attribute similarity matrix between nodes and calculated vector representation associated with structural information and designed the joint learning process in a distributed manner.…”

Section: Related Workmentioning

confidence: 99%

Identifying vital nodes for influence maximization in attributed networks

Wang

Zheng

Liu

2022

Sci Rep

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Identifying a set of vital nodes to achieve influence maximization is a topic of general interest in network science. Many algorithms have been proposed to solve the influence maximization problem in complex networks. Most of them just use topology information of networks to measure the node influence. However, the node attribute is also an important factor for measuring node influence in attributed networks. To tackle this problem, we first propose an extension model of linear threshold (LT) propagation model to simulate the information propagation in attributed networks. Then, we propose a novel community-based method to identify a set of vital nodes for influence maximization in attributed networks. The proposed method considers both topology influence and attribute influence of nodes, which is more suitable for identifying vital nodes in attributed networks. A series of experiments are carried out on five real world networks and a large scale synthetic network. Compared with CELF, IMM, CoFIM, HGD, NCVoteRank and K-Shell methods, experimental results based on different propagation models show that the proposed method improves the influence spread by $$-2.28\% \, \textrm{to} \, 4.76\%$$ - 2.28 % to 4.76 % , $$-2.50\% \, \textrm{to} \, 16.97\%$$ - 2.50 % to 16.97 % , $$0.18\% \, \textrm{to} \, 16.07\%$$ 0.18 % to 16.07 % , $$0.22\% \, \textrm{to} \, 41.82\%$$ 0.22 % to 41.82 % , $$0.23\% \, \textrm{to} \, 11.24\%$$ 0.23 % to 11.24 % and $$10.78\% \, \textrm{to} \, 75.22\%$$ 10.78 % to 75.22 % .

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“…an edge is generated when two people are in contact. (3)DNC [51]. This is a directed temporal network of emails in the 2016 Democratic National Committee email leak.…”

Section: Datasetsmentioning

confidence: 99%