scholarly journals Scalable temporal latent space inference for link prediction in dynamic social networks (extended abstract)

2017 ◽  
Author(s):  
Linhong Zhu ◽  
Dong Guo ◽  
Junming Yin ◽  
Greg Ver Steeg ◽  
Aram Galstyan
Keyword(s):  
Social Networks ◽  
Link Prediction ◽  
Dynamic Social Networks ◽  
Latent Space

scholarly journals Scalable Temporal Latent Space Inference for Link Prediction in Dynamic Social Networks

2016 ◽  
Vol 28 (10) ◽  
pp. 2765-2777 ◽  
Author(s):  
Linhong Zhu ◽  
Dong Guo ◽  
Junming Yin ◽  
Greg Ver Steeg ◽  
Aram Galstyan
Keyword(s):  
Social Networks ◽  
Link Prediction ◽  
Dynamic Social Networks ◽  
Latent Space

scholarly journals A Feasible Temporal Links Prediction Framework Combining with Improved Gravity Model

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 100
Author(s):  
Xinyu Huang ◽  
Dongming Chen ◽  
Tao Ren
Keyword(s):  
Social Networks ◽  
Gravity Model ◽  
Link Prediction ◽  
Temporal Networks ◽  
Scientific Society ◽  
Multidisciplinary Study ◽  
Dynamic Social Networks ◽  
Traditional Research ◽  
Real World Datasets ◽  
Social Applications

Social network analysis is a multidisciplinary study covering informatics, mathematics, sociology, management, psychology, etc. Link prediction, as one of the fundamental studies with a variety of applications, has attracted increasing focus from scientific society. Traditional research based on graph theory has made numerous achievements, whereas suffering from incapability of dealing with dynamic behaviors and low predicting accuracy. Aiming at addressing the problem, this paper employs a diagonally symmetrical supra-adjacency matrix to represent the dynamic social networks, and proposes a temporal links prediction framework combining with an improved gravity model. Extensive experiments on several real-world datasets verified the superiority on competitors, which benefits recommending friends in social networks. It is of remarkable significance in revealing the evolutions in temporal networks and promoting considerable commercial interest for social applications.

scholarly journals A Generative Model for Dynamic Networks with Applications

2019 ◽  
Vol 33 ◽  
pp. 7842-7849
Author(s):  
Shubham Gupta ◽  
Gaurav Sharma ◽  
Ambedkar Dukkipati
Keyword(s):  
Neural Network ◽  
Social Networks ◽  
Real World ◽  
Link Prediction ◽  
Temporal Dynamics ◽  
Dynamic Networks ◽  
Collaboration Networks ◽  
Proposed Model ◽  
Latent Space ◽  
Over Time

Networks observed in real world like social networks, collaboration networks etc., exhibit temporal dynamics, i.e. nodes and edges appear and/or disappear over time. In this paper, we propose a generative, latent space based, statistical model for such networks (called dynamic networks). We consider the case where the number of nodes is fixed, but the presence of edges can vary over time. Our model allows the number of communities in the network to be different at different time steps. We use a neural network based methodology to perform approximate inference in the proposed model and its simplified version. Experiments done on synthetic and real world networks for the task of community detection and link prediction demonstrate the utility and effectiveness of our model as compared to other similar existing approaches.

Link prediction on signed social networks based on latent space mapping

2018 ◽  
Vol 49 (2) ◽  
pp. 703-722 ◽  
Author(s):  
Shensheng Gu ◽  
Ling Chen ◽  
Bin Li ◽  
Wei Liu ◽  
Bolun Chen
Keyword(s):  
Social Networks ◽  
Link Prediction ◽  
Space Mapping ◽  
Latent Space ◽  
Signed Social Networks

scholarly journals An evolutionary algorithm approach to link prediction in dynamic social networks

2014 ◽  
Vol 5 (5) ◽  
pp. 750-764 ◽  
Author(s):  
Catherine A. Bliss ◽  
Morgan R. Frank ◽  
Christopher M. Danforth ◽  
Peter Sheridan Dodds
Keyword(s):  
Social Networks ◽  
Evolutionary Algorithm ◽  
Link Prediction ◽  
Dynamic Social Networks
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