Measures of node centrality in mobile social networks

2015 ◽  
Vol 26 (09) ◽  
pp. 1550107 ◽  
Author(s):  
Zhenxiang Gao ◽  
Yan Shi ◽  
Shanzhi Chen
Keyword(s):  
Social Networks ◽  
Social Relations ◽  
Human Mobility ◽  
Graph Model ◽  
Information Propagation ◽  
Mobile Social Networks ◽  
Mobility Patterns ◽  
Centrality Metrics ◽  
Influential Nodes ◽  
Over Time

Mobile social networks exploit human mobility and consequent device-to-device contact to opportunistically create data paths over time. While links in mobile social networks are time-varied and strongly impacted by human mobility, discovering influential nodes is one of the important issues for efficient information propagation in mobile social networks. Although traditional centrality definitions give metrics to identify the nodes with central positions in static binary networks, they cannot effectively identify the influential nodes for information propagation in mobile social networks. In this paper, we address the problems of discovering the influential nodes in mobile social networks. We first use the temporal evolution graph model which can more accurately capture the topology dynamics of the mobile social network over time. Based on the model, we explore human social relations and mobility patterns to redefine three common centrality metrics: degree centrality, closeness centrality and betweenness centrality. We then employ empirical traces to evaluate the benefits of the proposed centrality metrics, and discuss the predictability of nodes' global centrality ranking by nodes' local centrality ranking. Results demonstrate the efficiency of the proposed centrality metrics.

scholarly journals A Study on the Node Centrality Based Multisocial Attributes Weighted in Mobile Social Networks

2018 ◽  
Vol 17 ◽  
pp. 01001
Author(s):  
Yanhong Meng ◽  
Xianxian Liu ◽  
Peirong Zhao ◽  
Yunhui Yi
Keyword(s):  
Social Networks ◽  
Social Relations ◽  
Human Mobility ◽  
Data Communication ◽  
Graph Model ◽  
Real Data ◽  
Mobility Model ◽  
Mobile Social Networks ◽  
Data Set ◽  
Binary Network

Mobile social networks (MSNs) exploit human mobility and consequent device-to-device contact to opportunistically realize data communication. Thus links in MSNs is dynamic changing over time and strongly influenced by people activities, mining influential nodes is one of the important questions for effective information transmission in MSNs. While traditional centrality definitions are based on the static binary network model and not suitable for time-varying topology structure in mobile social network. Furthermore previous centrality metrics often referred to social attributes about neighbor nodes and contact times, and did not take the contact duration time into consideration. Therefore, this paper proposes a centrality measurement method based on multi-social attributes weighted. We first use the temporal evolution graph model which more accurately depicts the dynamic nature of topology in MSNs. Quantifying human social relations and mobility model as weights for the links, and then we redefine degree of centrality and the measurement of shortest path. Finally, the superiority of the concepts we posed are evaluated in the real data set.

scholarly journals Ranking Influential Nodes of Fake News Spreading on Mobile Social Networks

2021 ◽  
Vol 29 (4) ◽  
pp. 93-130
Author(s):  
Yunfei Xing ◽  
Xiwei Wang ◽  
Feng-Kwei Wang ◽  
Yang Shi ◽  
Wu He ◽  
...  
Keyword(s):  
Social Networks ◽  
Information Entropy ◽  
Dynamic Process ◽  
Negative Impact ◽  
Entropy Theory ◽  
Direct Influence ◽  
Mobile Social Networks ◽  
Fake News ◽  
Two Factors ◽  
Influential Nodes

Online fake news can generate a negative impact on both users and society. Due to the concerns with spread of fake news and misinformation, assessing the network influence of online users has become an important issue. This study quantifies the influence of nodes by proposing an algorithm based on information entropy theory. Dynamic process of influence of nodes is characterized on mobile social networks (MSNs). Weibo (i.e., the Chinese version of microblogging) users are chosen to build the real network and quantified influence of them is analyzed according to the model proposed in this paper. MATLAB is employed to simulate and validate the model. Results show the comprehensive influence of nodes increases with the rise of two factors: the number of nodes connected to them and the frequency of their interaction. Indirect influence of nodes becomes stronger than direct influence when the network scope rises. This study can help relevant organizations effectively oversee the spread of online fake news on MSNs.

Influential nodes selection to enhance data dissemination in mobile social networks: A survey

2020 ◽  
Vol 169 ◽  
pp. 102768
Author(s):  
Muluneh Mekonnen Tulu ◽  
Mbazingwa E. Mkiramweni ◽  
Ronghui Hou ◽  
Sultan Feisso ◽  
Talha Younas
Keyword(s):  
Social Networks ◽  
Data Dissemination ◽  
Mobile Social Networks ◽  
Influential Nodes

scholarly journals Incorporating Memory-Based Preferences and Point-of-Interest Stickiness into Recommendations in Location-Based Social Networks

2021 ◽  
Vol 10 (1) ◽  
pp. 36
Author(s):  
Hang Zhang ◽  
Mingxin Gan ◽  
Xi Sun
Keyword(s):  
Social Networks ◽  
Human Mobility ◽  
Mobility Patterns ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Repeat Visit ◽  
Preference Evolution ◽  
Attenuation Mechanism ◽  
Mobility Behavior ◽  
Location Based Social Networks

In location-based social networks (LBSNs), point-of-interest (POI) recommendations facilitate access to information for people by recommending attractive locations they have not previously visited. Check-in data and various contextual factors are widely taken into consideration to obtain people’s preferences regarding POIs in existing POI recommendation methods. In psychological effect-based POI recommendations, the memory-based attenuation of people’s preferences with respect to POIs, e.g., the fact that more attention is paid to POIs that were checked in to recently than those visited earlier, is emphasized. However, the memory effect only reflects the changes in an individual’s check-in trajectory and cannot discover the important POIs that dominate their mobility patterns, which are related to the repeat-visit frequency of an individual at a POI. To solve this problem, in this paper, we developed a novel POI recommendation framework using people’s memory-based preferences and POI stickiness, named U-CF-Memory-Stickiness. First, we used the memory-based preference-attenuation mechanism to emphasize personal psychological effects and memory-based preference evolution in human mobility patterns. Second, we took the visiting frequency of POIs into consideration and introduced the concept of POI stickiness to identify the important POIs that reflect the stable interests of an individual with respect to their mobility behavior decisions. Lastly, we incorporated the influence of both memory-based preferences and POI stickiness into a user-based collaborative filtering framework to improve the performance of POI recommendations. The results of the experiments we conducted on a real LBSN dataset demonstrated that our method outperformed other methods.

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