Link Sign Prediction
5 papers with code • 3 benchmarks • 2 datasets
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Adversarially Robust Signed Graph Contrastive Learning from Balance Augmentation
Similar to how structure learning can restore unsigned graphs, balance learning can be applied to signed graphs by improving the balance degree of the poisoned graph.
CSG: Curriculum Representation Learning for Signed Graph
Signed graphs are valuable for modeling complex relationships with positive and negative connections, and Signed Graph Neural Networks (SGNNs) have become crucial tools for their analysis.
SGA: A Graph Augmentation Method for Signed Graph Neural Networks
Signed Graph Neural Networks (SGNNs) are vital for analyzing complex patterns in real-world signed graphs containing positive and negative links.
A Signed Subgraph Encoding Approach via Linear Optimization for Link Sign Prediction
In this paper, we propose a different link sign prediction architecture call SELO (Subgraph Encoding via Linear Optimization), which obtains overall leading prediction performances compared the state-of-the-art algorithm SDGNN.
A Graph Convolution for Signed Directed Graphs
A signed directed graph is a graph with sign and direction information on the edges.
Signed Graph Neural Networks: A Frequency Perspective
Graph convolutional networks (GCNs) and its variants are designed for unsigned graphs containing only positive links.
Representation Learning in Continuous-Time Dynamic Signed Networks
Modeling such dynamics of signed networks is crucial to understanding the evolution of polarization in the network and enabling effective prediction of the signed structure (i. e., link signs and signed weights) in the future.
wsGAT: Weighted and Signed Graph Attention Networks for Link Prediction
Graph Neural Networks (GNNs) have been widely used to learn representations on graphs and tackle many real-world problems from a wide range of domains.
Signed Graph Diffusion Network
In this paper, we propose Signed Graph Diffusion Network (SGDNet), a novel graph neural network that achieves end-to-end node representation learning for link sign prediction in signed social graphs.
