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Found 4 papers, 3 papers with code
PORCA: Root Cause Analysis with Partially Observed Data
In this paper, we unveil the issues of unobserved confounders and heterogeneity in partial observation and come up with a new problem of root cause analysis with partially observed data.
SIG: Efficient Self-Interpretable Graph Neural Network for Continuous-time Dynamic Graphs
We aim to predict future links within the dynamic graph while simultaneously providing causal explanations for these predictions.
AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language Models
With the evolving of time, the types of anomaly edges are emerging and the labeled anomaly samples are few for each type.
Learning the Relation between Similarity Loss and Clustering Loss in Self-Supervised Learning
However, the relation between these two losses is not clear.
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