scholarly journals Exploring Trusted Relations among Virtual Interactions in Social Networks for Detecting Influence Diffusion

2019 ◽  
Vol 8 (9) ◽  
pp. 415
Author(s):  
Heba M. Wagih ◽  
Hoda M. O. Mokhtar ◽  
Samy S. Ghoniemy
Keyword(s):  
Social Networks ◽  
Peer Influence ◽  
Online Social Networks ◽  
Large Scale ◽  
Social Ties ◽  
Mutual Trust ◽  
Friend Recommendation ◽  
Influential Nodes ◽  
Location Based Social Networks ◽  
Virtual Interactions

Recently, social networks have shown huge potential in terms of collaborative web services and the study of peer influence as a result of the massive amount of data, datasets, and interrelations generated. These interrelations cannot guarantee the success of online social networks without ensuring the existence of trust between nodes. Detecting influential nodes improves collaborative filtering (CF) recommendations in which nodes with the highest influential capability are most likely to be the source of recommendations. Although CF-based recommendation systems are the most widely used approach for implementing recommender systems, this approach ignores the mutual trust between users. In this paper, a trust-based algorithm (TBA) is introduced to detect influential spreaders in social networks efficiently. In particular, the proposed TBA estimates the influence that each node has on the other connected nodes as well as on the whole network. Next, a Friend-of-Friend recommendation (FoF-SocialI) algorithm is addressed to detect the influence of social ties in the recommendation process. Finally, experimental results, performed on three large scale location-based social networks, namely, Brightkite, Gowalla, and Weeplaces, to test the efficiency of the proposed algorithm, are presented. The conducted experiments show a remarkable enhancement in predicting and recommending locations in various social networks.

Exploiting User Check-In Data for Geo-Friend Recommendations in Location-Based Social Networks

2020 ◽  
Vol 11 (2) ◽  
pp. 1-17
Author(s):  
Shudong Liu ◽  
Ke Zhang
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Time Slot ◽  
Empirical Studies ◽  
Real Life ◽  
Periodic Trajectories ◽  
The Public ◽  
Friend Recommendation ◽  
Location Based Social Networks ◽  
Friend Recommendations

The development of Web 2.0 technologies has meant that online social networks can both help the public facilitate sharing and communication and help them make new friends through their cyberspace social circles. Generating more accurate and geographically related results to help users find more friends in real life is gradually becoming a research hotspot. Recommending geographically related friends and alleviating check-in data sparsity problems in location-based social networks allows those to divide a day into different time slots and automatically collect user check-in data at each time slot over a certain period. Second, some important location points or regions are extracted from raw check-in trajectories, temporal periodic trajectories are constructed, and a geo-friend recommendation framework is proposed that can help users find geographically related friends. Finally, empirical studies from a real-world dataset demonstrate that this paper's method outperforms other existing methods for geo-friend recommendations in location-based social networks.

Community Detection and Profiling in Location-Based Social Networks

2014 ◽  
pp. 158-175
Author(s):  
Zhu Wang ◽  
Xingshe Zhou ◽  
Daqing Zhang ◽  
Bin Guo ◽  
Zhiwen Yu
Keyword(s):  
Social Networks ◽  
Social Networking ◽  
Community Detection ◽  
Online Social Networks ◽  
Large Scale ◽  
Recent Progress ◽  
Significant Feature ◽  
Data Set ◽  
Community Profiling ◽  
Location Based Social Networks

Due to the proliferation of GPS-enabled smartphones, Location-Based Social Networking (LBSNs) services have been experiencing a remarkable growth over the last few years. Compared with traditional online social networks, a significant feature of LBSNs is the coexistence of both online and offline social interactions, providing a large-scale heterogeneous social network that is able to facilitate lots of academic studies. One possible study is to leverage both online and offline social ties for the recognition and profiling of community structures. In this chapter, the authors attempt to summarize some recent progress in the community detection problem based on LBSNs. In particular, starting with an empirical analysis on the characters of the LBSN data set, the authors present three different community detection approaches, namely, link-based community detection, content-based community detection, and hybrid community detection based on both links and contents. Meanwhile, they also address the community profiling problem, which is very useful in real-world applications.

A topic community-based method for friend recommendation in large-scale online social networks

2016 ◽  
Vol 29 (6) ◽  
pp. e3924 ◽  
Author(s):  
Chaobo He ◽  
Hanchao Li ◽  
Xiang Fei ◽  
Atiao Yang ◽  
Yong Tang ◽  
...  
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Large Scale ◽  
Community Based ◽  
Friend Recommendation

scholarly journals Hashkat: large-scale simulations of online social networks

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Kevin Ryczko ◽  
Adam Domurad ◽  
Nicholas Buhagiar ◽  
Isaac Tamblyn
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Large Scale ◽  
Large Scale Simulations

Scalable Influence Maximization Meets Efficiency and Effectiveness in Large-Scale Social Networks

2020 ◽  
Vol 30 (08) ◽  
pp. 1079-1096
Author(s):  
Liqing Qiu ◽  
Shuang Zhang ◽  
Chunmei Gu ◽  
Xiangbo Tian
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Influence Maximization ◽  
Scalable Algorithm ◽  
Influence Spread ◽  
Efficiency And Effectiveness ◽  
Spread Of Influence ◽  
Influential Nodes ◽  
Comparison Algorithms

Influence maximization is a problem that aims to select top [Formula: see text] influential nodes to maximize the spread of influence in social networks. The classical greedy-based algorithms and their improvements are relatively slow or not scalable. The efficiency of heuristic algorithms is fast but their accuracy is unacceptable. Some algorithms improve the accuracy and efficiency by consuming a large amount of memory usage. To overcome the above shortcoming, this paper proposes a fast and scalable algorithm for influence maximization, called K-paths, which utilizes the influence tree to estimate the influence spread. Additionally, extensive experiments demonstrate that the K-paths algorithm outperforms the comparison algorithms in terms of efficiency while keeping competitive accuracy.

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