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Link Prediction

topic3 events
papersTODAY 04:00 UTC

arXiv Paper Proposes Temporally Enhanced Signed Graph Neural Networks for Link Prediction

A revised arXiv preprint presents a graph neural network approach for predicting links in temporal signed networks, which capture how cooperative and adversarial relationships evolve over time. The authors motivate the work with applications including social media analysis, trust and reputation systems, and financial transaction networks. The paper's abstract excerpt focuses on the method's design for handling dynamic, sign-aware graph structure.

graph-neural-networks/link-prediction/arxiv/research
papersTODAY 04:00 UTC

Biquaternionic Space with Complex-Valued Attention for Temporal Knowledge Graph Completion

A new arXiv preprint proposes embedding temporal knowledge graphs in biquaternionic space, arguing that relying on a single geometric space limits how well models capture varied relational patterns. The method pairs that representation with complex-valued attention to score facts whose validity changes over time. The work targets the link-prediction task of inferring missing facts in evolving knowledge graphs.

knowledge-graphs/embeddings/attention/research
papersSEP 10 04:00 UTC

LiFTER: A Neuro-Symbolic Method for Interpretable Continuous-Time Graph Forecasting

Researchers introduce LiFTER, a neuro-symbolic framework aimed at making continuous-time dynamic graph forecasting more transparent. Instead of leaving link predictions hidden inside opaque neural states that compress past interactions, the method surfaces which entities are shared across events and how temporal patterns contribute to forecasts. A revised version (v2) of the preprint has been posted on arXiv.

interpretability/neuro-symbolic/graphs/forecasting