Research on the Link Prediction Model of Dynamic Multiplex Social Network Based on Improved Graph Representation Learning
Tianyu Xia,Yijun Gu,Dechun Yin +2 more
TLDR
Wang et al. as discussed by the authors proposed an improved link prediction model in dynamic social networks, where the whole embedding of each node is separated into two parts, basic embedding and edge embedding, and selected time slices for dynamic social network to get the graph embeddings in different snapshots.Abstract:
In the natural and social systems of the real world, various network can be seen everywhere. The world where people live can be seen as a combination of network with different dimensions. Link prediction formalizes the interaction behavior between people. Traditional link prediction methods mainly study the user behavior of static social network. This article studied the dynamic graph representation learning so as to put forward an improved link prediction model in dynamic social network. Besides, the interactions in the real world can be multiple, links at different moments may have different meanings. The proposed model firstly solved the problem of link prediction on multiple kinds of edges. The whole embedding of each node is separated into two parts, basic embedding and edge embedding. Then the proposed model selected time slices for dynamic social network to get the graph embeddings in different snapshots. What’s more, the $t+1$ time step embedding vector was used to validate $t$ time step prediction effect and the proposed model performed better than traditional graph representation learning methods.read more
Citations
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Journal ArticleDOI
A link prediction method for MANETs based on fast spatio-temporal feature extraction and LSGANs
TL;DR: Wang et al. as discussed by the authors proposed a link prediction model named FastSTLSG, which can automatically analyze the features of the topology in a unified framework to effectively capture the spatio-temporal correlation of Mobile Ad Hoc Networks.
Journal ArticleDOI
A Deep-Reinforcement-Learning-Based Social-Aware Cooperative Caching Scheme in D2D Communication Networks
TL;DR: Zhang et al. as mentioned in this paper proposed a social-aware D2D caching scheme that integrates the concept of social incentive and recommendation with D2DM caching decision making, which can be formulated as a Markov decision process.
Journal ArticleDOI
A Deep-Reinforcement-Learning-Based Social-Aware Cooperative Caching Scheme in D2D Communication Networks
TL;DR: Zhang et al. as discussed by the authors proposed a social-aware D2D caching scheme that integrates the concept of social incentive and recommendation with D2DM caching decision-making to maximize the data offloading probability, which can be formulated as a Markov decision process.
Journal ArticleDOI
Link prediction in multiplex networks: An evidence theory method
TL;DR: Wang et al. as mentioned in this paper proposed a new multiplex link prediction method that measured the connection likelihood of a node pair by integrating its similarity scores from all layers using evidence theory, where each layer is regarded as a source of evidence, and the similarity of node pair in one layer is represented by a mass function.
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