Results 11 to 20 of about 146,402 (261)
Balance and frustration in signed networks [PDF]
Journal of Complex Networks, 2018 The frustration index is a key measure for analysing signed networks, which has been underused due to its computational complexity. We use an exact optimisation-based method to analyse frustration as a global structural property of signed networks coming from diverse application areas.
Samin Aref, Mark C. Wilson
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Signed networks in social media [PDF]
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2010 Relations between users on social media sites often reflect a mixture of positive (friendly) and negative (antagonistic) interactions. In contrast to the bulk of research on social networks that has focused almost exclusively on positive interpretations of links between people, we study how the interplay between positive and negative relationships ...
Jure Leskovec +2 more
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Signed random walk diffusion for effective representation learning in signed graphs.
PLoS ONE, 2022 How can we model node representations to accurately infer the signs of missing edges in a signed social graph? Signed social graphs have attracted considerable attention to model trust relationships between people. Various representation learning methods
Jinhong Jung, Jaemin Yoo, U Kang
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Dynamics over Signed Networks [PDF]
SIAM Review, 2019 In press, SIAM ...
Shi, Guodong +2 more
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Enhanced Signed Graph Neural Network with Node Polarity
Entropy, 2022 Signed graph neural networks learn low-dimensional representations for nodes in signed networks with positive and negative links, which helps with many downstream tasks like link prediction.
Jiawang Chen +3 more
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Signed Network Node Embedding via Dual Attention Mechanism
IEEE Access, 2022 In signed networks, GNNs are used to get node embedding by aggregating node neighbor information. Most of the existing methods aggregate neighbor information from the node level, and the different paths between nodes and neighbors will also affect node ...
Zekun Lu +4 more
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Learning Embedding for Signed Network in Social Media with Hierarchical Graph Pooling
Applied Sciences, 2022 Signed network embedding concentrates on learning fixed-length representations for nodes in signed networks with positive and negative links, which contributes to many downstream tasks in social media, such as link prediction.
Jiawang Chen, Zhenqiang Wu
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Characterization of 2-Path Signed Network
Complexity, 2020 A signed network is a network where each edge receives a sign: positive or negative. In this paper, we report our investigation on 2-path signed network of a given signed network Σ, which is defined as the signed network whose vertex set is that of Σ and
Deepa Sinha, Deepakshi Sharma
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Learning Weight Signed Network Embedding with Graph Neural Networks
Data Science and Engineering, 2023 Network embedding aims to map nodes in a network to low-dimensional vector representations. Graph neural networks (GNNs) have received much attention and have achieved state-of-the-art performance in learning node representation.
Zekun Lu +4 more
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A negative link prediction method for signed social networks
Xi'an Gongcheng Daxue xuebao, 2021 Aiming at the disadvantages of negative link feature fusion and effective information mining in signed social networks, resulting in low prediction performance, a new feature fusion negative link prediction method is proposed.
Wei WANG, Miaomiao XUE, Momeng LIU
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