Steering Top-k Influencers in Dynamic Graphs via Local Updates

Yalavarthi, Vijaya Krishna; Khan, Arijit

doi:10.1109/bigdata.2018.8621873

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…The various studies show that information propagation to maximize the diffusion of influence in dynamic social networks. An algorithm and methods were developed to minimize the number of users or seeds with maximizing the influence and information diffusion in the social network within the given time bound [35][36][37][38][39][40]. Literature have also witnessed that researchers are looking towards the process of optimizing information propagation for various analyses.…”

Section: Related Workmentioning

confidence: 99%

Novel Approach for Optimizing Information Propagation in Dynamic Social Network

Selva

Kayavizhy

2020

Wseas Transactions on Computers

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Identification of potential node is one of the essential operations to be carried out in social network analysis as it is necessary to undertake various important decisions associated with the information propagation. Review of existing literature towards social network highlights that there is very less work carried out towards emphasizing potential node. Therefore, the proposed study offers a novel and unique solution that is capable of optimizing the level of information propagation when it is exposed to dynamic networks. The proposed study has been modeled using graph theory and it uses degree centrality distribution in order to offer more insight towards analyzing the selection of potential nodes in a social network. The study significantly contributes towards precise information propagation and its sustainability in the presence of dynamic social network in every aspect.

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Section: Related Workmentioning

confidence: 99%

Novel Approach for Optimizing Information Propagation in Dynamic Social Network

Selva

Kayavizhy

2020

Wseas Transactions on Computers

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“…For the metrics-based algorithms, updates can be applied to previous calculated metrics according to the dynamic changes in the network graph [16,23]. To reduce the update costs, local update strategies aim to heuristically limit the impact of network changes, thus preventing the need to update the influence of all nodes in social network [23,24]. For the sketch-based algorithms, especially the RR set-based algorithms, they tried to update the previously simulated RR sets, avoiding the computational cost associated with resampling [15,17,25].…”

Section: Related Workmentioning

confidence: 99%

“…One approach directly updates the existing seed set. Specifically, it attempts to select new nodes from the social network to replace nodes in the existing seed set, aiming to maximize the influence gain [20,24]. This method can avoid the time cost of selecting seed sets from scratch.…”

Section: Related Workmentioning

confidence: 99%

Influence of spacing on twin fibers pull-out test of concrete matrix under dynamic load

Zhang¹,

Luo²,

Liu³

et al. 2018

Journal of Shenzhen University Science and Engineering

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Dynamic influence maximization problem (DIMP) aims to maintain a group of influential users within an evolving social network, so that the influence scope can be maximized at any given moment. A primary category of DIMP algorithms focuses on the renewal of reverse reachable (RR) sets, which is designed for static social network scenarios, to accelerate the estimation of influence spread. And the generation time of RR sets plays a crucial role in algorithm efficiency. However, their update approaches require sequential updates for each edge change, leading to considerable computational cost. In this paper, we propose a strategy for batch updating the changes in network edge weights to efficiently maintain RR sets. By calculating the probability that previous RR sets can be regenerated at the current moment, we retain those with a high probability. This method can effectively avoid the computational cost associated with updating and sampling these RR sets. Besides, we propose an resampling strategy that generates high-probability RR sets to make the final distribution of RR sets approximate to the sampling probability distribution under the current social network. The experimental results indicate that our strategy is both scalable and efficient. On the one hand, compared to the previous update strategies, the running time of our strategy is insensitive to the number of changes in network weight; on the other hand, for various RR set-based algorithms, our strategy can reduce the running time while maintaining the solution quality that is essentially consistent with the static algorithms.

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“…The correlation between nodal centrality and influence spread is investigated in many works [90,14,132,165]. Identifying influential nodes can be useful for planning and structuring techniques that accelerate the information propagation in marketing applications [121,171,163] or hinder the propagation of unwanted information [64,21,146,135]. Targeting the influential individuals of a social community can help companies in viral marketing to expand their business potential and to promote their products by triggering a cascade of influence in the community [133].…”

Section: Introductionmentioning

confidence: 99%

Identifying the Top-k Influential Spreaders in Social Networks: a Survey and Experimental Evaluation

Taha

2022

IEEE Access

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Identifying the influential and spreader nodes in complex networks solves many types of complex scientific problems. In social networks, identifying the influential individuals can be useful for structuring techniques that accelerate or hinder information propagation. Each node in the network has unique characteristics that reflect its importance. These characteristics are used by researchers to design many different centrality algorithms. Unfortunately, current survey papers categorize these algorithms into broad classes and do not draw distinguishable boundaries among the specific techniques adopted by them. This can result in misclassifying unrelated algorithms into the same analysis category. To overcome this, we introduce a methodology-based taxonomy for classifying the algorithms that identify top-k influential spreaders into hierarchically nested, specific, and fine-grained categories. We survey 184 papers and discuss their algorithms, which fall under 26 specific techniques. Our methodological taxonomy classifies the algorithms hierarchically into the following manner: Analysis type analysis scope analysis approach analysis category analysis sub-category analysis specific technique. We introduce in this paper a comprehensive survey, review, and experimental evaluation of the recent and state-of-the-art algorithms that identify the top-k and influential spreader nodes in social networks.

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Steering Top-k Influencers in Dynamic Graphs via Local Updates

Cited by 8 publications

References 14 publications

Novel Approach for Optimizing Information Propagation in Dynamic Social Network

Novel Approach for Optimizing Information Propagation in Dynamic Social Network

Influence of spacing on twin fibers pull-out test of concrete matrix under dynamic load

Identifying the Top-k Influential Spreaders in Social Networks: a Survey and Experimental Evaluation

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