Improving short-term information spreading efficiency in scale-free networks by specifying top large-degree vertices as the initial spreaders

The positive function of initially influential vertices could be exploited to improve spreading efficiency for short-term spreading in scale-free networks. However, the selection of initial spreaders depends on the specific scenes. The selection of initial spreaders needs to offer low complexity and low power consumption for short-term spreading. In this paper, we propose a selection strategy for efficiently spreading information by specifying a set of top large-degree vertices as the initially informed vertices. The essential idea behind the proposed selection strategy is to exploit the significant diffusion of the top large-degree vertices at the beginning of spreading. To evaluate the positive impact of initially influential vertices, we first build an information spreading model in the Barabási–Albert (BA) scale-free network; next, we design 54 comparative Monte Carlo experiments based on a benchmark strategy and the proposed selection strategy in different BA scale-free network structures. Experimental results indicate that (i) the proposed selection strategy can significantly improve spreading efficiency in the short-term spreading and (ii) both network size and number of hubs have a strong impact on spreading efficiency, while the number of initially informed vertices has a weak impact. The proposed selection strategy can be employed in short-term spreading, such as sending warnings or crisis information spreading or information spreading in emergency training or realistic emergency scenes.

and 3 (e.g., the specification of RIIV and SIIV). In addition, the figures are not clear since you shouldn't choose colors referring to lines, but, e.g., different line styles.
The paper is a good ad a "guideline paper" in describing the topic of information spreading in networks, but it is not particularly clear to me the contribution you provided and the novelty of the paper. Despite the Information Spreading Model that you propose is quite reasonable, the experiments you design are not useful. In fact, e.g., it is obvious that a proper choice of initial Informed Vertices performs better than a random one, and this is proved by the fact that, after a transient, both strategies reach the same performance. Moreover, it is not clear how you choose the "specific" IIV.

22-May-2018
Dear Dr Wang: Manuscript ID RSOS-180404 entitled "An Efficient Method for Short-term Information Spreading in Scale-free Networks by Specifying the Initially Informed Vertices" which you submitted to Royal Society Open Science, has been reviewed. The comments from reviewers are included at the bottom of this letter.
In view of the criticisms of the reviewers, the manuscript has been rejected in its current form. However, a new manuscript may be submitted which takes into consideration these comments.
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We look forward to receiving your resubmission. Comments to the Author(s) The paper describes a selection strategy for spreading information by specifying a set of top large-degree vertices.The main idea is to exploit the significant diffusion of the top large-degree vertices at the beginning of spreading. The approach seems to be novel and very interesting. I suggest to accept this paper after minor revisios.
i) Please discuss this approach more in general in the introduction section ii) Please explain better fig. 2 iii) Discuss better the Monte Carlo Method. iv) detalis better the experimental section Reviewer: 2 Comments to the Author(s) The authors describe the a selection strategy for spreading information called SIIV, which consists in properly detecting a set of top-degree vertices as the Initial Informed Vertices. The paper is well written and the overall organization is good. However, is not completely proof-checked (you need to correct several typos in it) and there are some problems in tables 2 and 3 (e.g., the specification of RIIV and SIIV). In addition, the figures are not clear since you shouldn't choose colors referring to lines, but, e.g., different line styles.
The paper is a good ad a "guideline paper" in describing the topic of information spreading in networks, but it is not particularly clear to me the contribution you provided and the novelty of the paper. Despite the Information Spreading Model that you propose is quite reasonable, the experiments you design are not useful. In fact, e.g., it is obvious that a proper choice of initial Informed Vertices performs better than a random one, and this is proved by the fact that, after a transient, both strategies reach the same performance. Moreover, it is not clear how you choose the "specific" IIV.

RSOS-181137.R0
Review form: Reviewer 2 Is the manuscript scientifically sound in its present form? Yes

Are the interpretations and conclusions justified by the results? Yes
Is the language acceptable? Yes

Recommendation?
Accept as is Dear Dr Wang, I am pleased to inform you that your manuscript entitled "Improving Short-Term Information Spreading Efficiency in Scale-Free Networks by Specifying Top Large-Degree Vertices as the Initial Spreaders" is now accepted for publication in Royal Society Open Science.
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List of Modifications in the Revised Manuscript
Shuangyan Wang 1 , Yunfeng Deng 2* , Ying Li 1 1: School of Engineering and Technology, China University of Geosciences Beijing 2: Chinese Academy of Governance sy.wang@cugb.edu.cn; 13910185162@139.com; liying@cugb.edu.cn Acknowledgement The authors are grateful to the editor and the anonymous reviewers for a careful checking of the details and for helpful comments that improved this paper.

Modification of Main Text
1. There is a minor revision in the Section of Abstract. 5. The Section of Results is revised.

The third and fourth paragraphs in
6. The Section of Discussion is revised. Table 1~3 are revised. Table 2~3. Figure 2 are revised. Figure 3~8 are revised. The authors are grateful to the editor and anonymous reviewers for helpful comments! anonymous reviewers for a careful checking of the details and for helpful comments that improved this paper.

Modification of References
Please note that the modifications made in the revised manuscript are listed on a document named as "List of Modifications. pdf".

Comment # 1:
The paper is well written and the overall organization is good.
However, is not completely proof-checked (you need to correct several typos in it)

Response:
First, thank you so much for giving this very critical and valuable comment!
We have checked our manuscript carefully.

Comment # 2:
There are some problems in tables 2 and 3 (e.g., the specification of RIIV and SIIV).

Response:
We have added the descriptions of the RIIV and SIIV in Table 2 and 3, and we have checked the two tables carefully.

Comment # 3:
In addition, the figures are not clear since you shouldn't choose colors referring to lines, but, e.g., different line styles.

Response:
We have revised all figures with different line styles in the manuscript.

Comment # 4:
The paper is a good ad a "guideline paper" in describing the topic of information spreading in networks, but it is not particularly clear to me the contribution you provided and the novelty of the paper. Despite the Information Spreading Model that you propose is quite reasonable, the experiments you design are not useful. In fact, e.g., it is obvious that a proper choice of initial Informed Vertices performs better than a random one, and this is proved by the fact that, after a transient, both strategies reach the same performance.

Response:
We agree that a proper choice of initial informed vertices performs better than a random one, and our experimental results have proved this point. However, it is also a good question that how to select the initial spreaders. Actually, many metrics and methods are proposed for identifying the initial spreaders, but for the short-term spreading with limited time, the identified method must be of low complexity and low power consumption. In this paper, we select the degree centrality as the metric to measure the influence of initial spreaders, because of the low complexity for identifying the large-degree vertices and the strong diffusion of large-degree vertices. We propose a selection strategy for improving spreading efficiency which is suitable to be applied to the short-term spreading.
Moreover, if there no limitation of the spreading time, it is obviously that all spreading can reach the same performance. However, 3 in the short-term spreading, the spreading time is limited, the essential question is that how many people can receive the information during a limited time. And in this paper, we terminate experiments until the spreading is convergent because that we want to record the completely experimental process. In real situations, the spreading cannot be convergent because of the limited spreading time. For the short-term spreading, it is important to improve the spreading efficiency at the beginning of the spreading or during a short limited time.

Comment # 5:
Moreover, it is not clear how you choose the "specific" IIV.

Response:
We select the initial informed spreader according to the degree centrality of vertices. The specific procedure is that: we first sort all vertices according to the degree centrality on descending order. Then we select a set of top large-degree vertices as the initial informed vertices. This method is of low computational complexity, and the degree centrality can measure the strong diffusion of vertices. The degree centrality is suitable to be employed in the short-term spreading.
In the real emergency training, it is easy to find the large-degree persons in a community, they are always the leaders, managers, or organizers.