Link Sign Prediction
5 papers with code • 3 benchmarks • 2 datasets
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Most implemented papers
Deep Network Embedding for Graph Representation Learning in Signed Networks
As an effective approach to solve graph mining problems, network embedding aims to learn a low-dimensional feature vector representation for each node of a given network.
Learning Opinion Dynamics From Social Traces
In this work we propose an inference mechanism for fitting a generative, agent-like model of opinion dynamics to real-world social traces.
Decoupled Variational Embedding for Signed Directed Networks
Comprehensive results on both link sign prediction and node recommendation task demonstrate the effectiveness of DVE.
Signed Bipartite Graph Neural Networks
Signed bipartite networks are different from classical signed networks, which contain two different node sets and signed links between two node sets.
SSSNET: Semi-Supervised Signed Network Clustering
sherylhyx/sssnet_signed_clustering
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Node embeddings are a powerful tool in the analysis of networks; yet, their full potential for the important task of node clustering has not been fully exploited.
