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Kim J, Jeong HJ, Lim S, Kim J. Effective and efficient core computation in signed networks. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.03.097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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2
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Dai C, Chen L, Hu K, Ding Y. Minimizing the Spread of Negative Influence in SNIR Model by Contact Blocking. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1623. [PMID: 36359713 PMCID: PMC9689805 DOI: 10.3390/e24111623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 10/29/2022] [Accepted: 11/07/2022] [Indexed: 06/16/2023]
Abstract
This paper presents a method to minimize the spread of negative influence on social networks by contact blocking. First, based on the infection-spreading process of COVID-19, the traditional susceptible, infectious, and recovered (SIR) propagation model is extended to the susceptible, non-symptomatic, infectious, and recovered (SNIR) model. Based on this model, we present a method to estimate the number of individuals infected by a virus at any given time. By calculating the reduction in the number of infected individuals after blocking contacts, the method selects the set of contacts to be blocked that can maximally reduce the affected range. The selection of contacts to be blocked is repeated until the number of isolated contacts that need to be blocked is reached or all infection sources are blocked. The experimental results on three real datasets and three synthetic datasets show that the algorithm obtains contact blockings that can achieve a larger reduction in the range of infection than other similar algorithms. This shows that the presented SNIR propagation model can more precisely reflect the diffusion and infection process of viruses in social networks, and can efficiently block virus infections.
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Affiliation(s)
- Caiyan Dai
- College of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Ling Chen
- College of Information Engineering, Yangzhou University, Yangzhou 225012, China
| | - Kongfa Hu
- College of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, China
- Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine in Prevention and Treatment of Tumor, Nanjing 210023, China
| | - Youwei Ding
- College of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, China
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3
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Opinion influence maximization problem in online social networks based on group polarization effect. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.07.086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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4
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Li L, Zeng A, Fan Y, Di Z. Modeling multi-opinion propagation in complex systems with heterogeneous relationships via Potts model on signed networks. CHAOS (WOODBURY, N.Y.) 2022; 32:083101. [PMID: 36049951 DOI: 10.1063/5.0084525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 07/05/2022] [Indexed: 06/15/2023]
Abstract
This paper investigates how the heterogenous relationships around us affect the spread of diverse opinions in the population. We apply the Potts model, derived from condensed matter physics on signed networks, to multi-opinion propagation in complex systems with logically contradictory interactions. Signed networks have received increasing attention due to their ability to portray both positive and negative associations simultaneously, while the Potts model depicts the coevolution of multiple states affected by interactions. Analyses and experiments on both synthetic and real signed networks reveal the impact of the topology structure on the emergence of consensus and the evolution of balance in a system. We find that, regardless of the initial opinion distribution, the proportion and location of negative edges in the signed network determine whether a consensus can be formed. The effect of topology on the critical ratio of negative edges reflects two distinct phenomena: consensus and the multiparty situation. Surprisingly, adding a small number of negative edges leads to a sharp breakdown in consensus under certain circumstances. The community structure contributes to the common view within camps and the confrontation (or alliance) between camps. The importance of inter- or intra-community negative relationships varies depending on the diversity of opinions. The results also show that the dynamic process causes an increase in network structural balance and the emergence of dominant high-order structures. Our findings demonstrate the strong effects of logically contradictory interactions on collective behaviors, and could help control multi-opinion propagation and enhance the system balance.
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Affiliation(s)
- Lingbo Li
- School of Systems Science, Beijing Normal University, Beijing 100875, People's Republic of China
| | - An Zeng
- School of Systems Science, Beijing Normal University, Beijing 100875, People's Republic of China
| | - Ying Fan
- School of Systems Science, Beijing Normal University, Beijing 100875, People's Republic of China
| | - Zengru Di
- School of Systems Science, Beijing Normal University, Beijing 100875, People's Republic of China
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Shi B, Xu K, Zhao J. Behavior Variations and Their Implications for Popularity Promotions: From Elites to Mass on Weibo. ENTROPY 2022; 24:e24050664. [PMID: 35626549 PMCID: PMC9141265 DOI: 10.3390/e24050664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/03/2022] [Accepted: 05/05/2022] [Indexed: 11/21/2022]
Abstract
The boom in social media with regard to producing and consuming information simultaneously implies the crucial role of online user influence in determining content popularity. In particular, understanding behavior variations between the influential elites and the mass grassroots is an important issue in communication. However, how their behavior varies across user categories and content domains and how these differences influence content popularity are rarely addressed. From a novel view of seven content domains, a detailed picture of the behavior variations among five user groups, from the views of both the elites and mass, is drawn on Weibo, one of the most popular Twitter-like services in China. Interestingly, elites post more diverse content with video links, while the mass possess retweeters of higher loyalty. According to these variations, user-oriented actions for enhancing content popularity are discussed and testified. The most surprising finding is that the diverse content does not always bring more retweets, and the mass and elites should promote content popularity by increasing their retweeter counts and loyalty, respectively. For the first time, our results demonstrate the possibility of highly individualized strategies of popularity promotions in social media, instead of a universal principle.
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Affiliation(s)
- Bowen Shi
- State Key Lab of Software Development Environment, Beihang University, Beijing 100191, China; (B.S.); (K.X.)
| | - Ke Xu
- State Key Lab of Software Development Environment, Beihang University, Beijing 100191, China; (B.S.); (K.X.)
| | - Jichang Zhao
- School of Economics and Management, Beihang University, Beijing 100191, China
- Correspondence:
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Samanta S, Kumar Dubey V, Das K. Coopetition bunch graphs: Competition and cooperation on COVID19 research. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2021.12.025] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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7
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Distance-aware optimization model for influential nodes identification in social networks with independent cascade diffusion. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.09.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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8
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Role-Aware Information Spread in Online Social Networks. ENTROPY 2021; 23:e23111542. [PMID: 34828240 PMCID: PMC8618065 DOI: 10.3390/e23111542] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 11/10/2021] [Accepted: 11/15/2021] [Indexed: 12/29/2022]
Abstract
Understanding the complex process of information spread in online social networks (OSNs) enables the efficient maximization/minimization of the spread of useful/harmful information. Users assume various roles based on their behaviors while engaging with information in these OSNs. Recent reviews on information spread in OSNs have focused on algorithms and challenges for modeling the local node-to-node cascading paths of viral information. However, they neglected to analyze non-viral information with low reach size that can also spread globally beyond OSN edges (links) via non-neighbors through, for example, pushed information via content recommendation algorithms. Previous reviews have also not fully considered user roles in the spread of information. To address these gaps, we: (i) provide a comprehensive survey of the latest studies on role-aware information spread in OSNs, also addressing the different temporal spreading patterns of viral and non-viral information; (ii) survey modeling approaches that consider structural, non-structural, and hybrid features, and provide a taxonomy of these approaches; (iii) review software platforms for the analysis and visualization of role-aware information spread in OSNs; and (iv) describe how information spread models enable useful applications in OSNs such as detecting influential users. We conclude by highlighting future research directions for studying information spread in OSNs, accounting for dynamic user roles.
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Node deletion-based algorithm for blocking maximizing on negative influence from uncertain sources. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.107451] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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10
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Qiu L, Yang Z, Zhu S, Gu C, Tian X. LTHS: A heuristic algorithm based on local two-hop search strategy for influence maximization in social networks. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-210379] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Influence maximization is a classic network optimization problem, which has been widely used in the field of viral marketing. The influence maximization problem aims to find a fixed number of active nodes. After a specific propagation model, the number of active nodes reaches the maximum. However, the existing influence maximization algorithms are overly pursuing certain indicators of efficiency or accuracy, which cannot be well accepted by some researchers. This paper proposes an effective algorithm to balance the accuracy and efficiency of the influence maximization problem called local two-hop search algorithm (LTHS). The core of the proposed algorithm is a node not only be affected by one-hop neighbor nodes, but also by two-hop neighbor nodes. Firstly, this paper selects initial seed nodes according to the characteristics of the node degree. Generally, the high degree of nodes regards as influential nodes. Secondly, this paper proposes a node two-hop influence evaluate function called two-hop diffusion value (THDV), which can evaluate node influence more accurately. Furthermore, in order to seek higher efficiency, this paper proposes a method to reduce the network scale. This paper conducted full experiments on five real-world social network datasets, and compared with other four well-known algorithms. The experimental results show that the LTHS algorithm is better than the comparison algorithms in terms of efficiency and accuracy.
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Affiliation(s)
- Liqing Qiu
- Shandong Province Key Laboratory of Wisdom Mine Information Technology, College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Zhongqi Yang
- Shandong Province Key Laboratory of Wisdom Mine Information Technology, College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Shiwei Zhu
- Qilu University of Technology/Shandong Academy of Sciences, Jinan, China
- Information Research Institute of Shandong Academy of Sciences, Jinan, China
- National Technical University of Ukraine, Igor Sikorsky Kyiv Polytechnic Institute
| | - Chunmei Gu
- Shandong Province Key Laboratory of Wisdom Mine Information Technology, College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Xiangbo Tian
- Shandong Province Key Laboratory of Wisdom Mine Information Technology, College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
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11
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Zhu J, Ni P, Wang G, Li Y. Misinformation influence minimization problem based on group disbanded in social networks. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.04.086] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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12
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Agha Mohammad Ali Kermani M, Ghesmati R, Pishvaee MS. A robust optimization model for influence maximization in social networks with heterogeneous nodes. COMPUTATIONAL SOCIAL NETWORKS 2021. [DOI: 10.1186/s40649-021-00096-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
AbstractInfluence maximization is the problem of trying to maximize the number of influenced nodes by selecting optimal seed nodes, given that influencing these nodes is costly. Due to the probabilistic nature of the problem, existing approaches deal with the concept of the expected number of nodes. In the current research, a scenario-based robust optimization approach is taken to finding the most influential nodes. The proposed robust optimization model maximizes the number of infected nodes in the last step of diffusion while minimizing the number of seed nodes. Nodes, however, are treated as heterogeneous with regard to their propensity to pass messages along; or as having varying activation thresholds. Experiments are performed on a real text-messaging social network. The model developed here significantly outperforms some of the well-known existing heuristic approaches which are proposed in previous works.
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Influence maximization frameworks, performance, challenges and directions on social network: A theoretical study. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2021. [DOI: 10.1016/j.jksuci.2021.08.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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14
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Chen L, Zhang Y, Chen Y, Li B, Liu W. Negative influence blocking maximization with uncertain sources under the independent cascade model. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.02.063] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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15
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Ahmadi Beni H, Bouyer A. Identifying Influential Nodes Using a Shell-Based Ranking and Filtering Method in Social Networks. BIG DATA 2021; 9:219-232. [PMID: 34029125 DOI: 10.1089/big.2020.0259] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The main goal in the influence maximization problem (IMP) is to find k minimum nodes with the highest influence spread on the social networks. Since IMP is NP-hard and is not possible to obtain the optimum results, it is considered by heuristic algorithms. Many strategies focus on the power of the influence spread of core nodes to find k influential nodes. Most of the core detection-based methods concentrate on nodes in the highest core and often give the same power for all nodes in the best core. However, some other nodes fairly have the potential to select as seed nodes in other less important cores, because these nodes can play an important role in the diffusion of information among the core nodes with other nodes. Given this fact, this article proposes a new shell-based ranking and filtering method, called shell-based ranking and filtering method (SRFM), for selecting influential seeds with the aim to maximize influence in the network. The proposed algorithm initially selects a set of nodes in different shells. Moreover, a set of the candidate nodes are created, and most of the periphery nodes are removed during a pruning approach to reduce the computational overhead. Therefore, the seed nodes are selected from the candidate nodes set using the role of the bridge nodes. Experimental results in both synthetic and real data sets showed that the proposed SRFM algorithm has more acceptable efficiency in the influence spread and runtime than other algorithms.
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Affiliation(s)
- Hamid Ahmadi Beni
- Department of Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran
| | - Asgarali Bouyer
- Department of Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran
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16
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Gong Y, Liu S, Bai Y. Efficient parallel computing on the game theory-aware robust influence maximization problem. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.106942] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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17
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Multi-topical authority sensitive influence maximization with authority based graph pruning and three-stage heuristic optimization. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02213-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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18
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Identification of top-k influential nodes based on discrete crow search algorithm optimization for influence maximization. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02283-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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19
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Wang F, She J, Ohyama Y, Jiang W, Min G, Wang G, Wu M. Maximizing positive influence in competitive social networks: A trust-based solution. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2020.09.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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20
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Olivares R, Muñoz F, Riquelme F. A multi-objective linear threshold influence spread model solved by swarm intelligence-based methods. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106623] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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21
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Aghaee Z, Kianian S. Efficient influence spread estimation for influence maximization. SOCIAL NETWORK ANALYSIS AND MINING 2020. [DOI: 10.1007/s13278-020-00694-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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