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New method generates counterfactual explanations for temporal graphs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for generating counterfactual explanations for temporal graphs, focusing on specified alternative outcomes rather than just invalidating original predictions. This approach, called Specified-Foil Counterfactual, identifies past conditions that would lead to a desired alternative prediction. The method has been demonstrated with LiFTER on dynamic graphs and TLogic on temporal knowledge graphs, showing significant reductions in predictor evaluations while maintaining success rates. AI

IMPACT Enhances explainability in temporal graph models by enabling users to explore conditions for alternative outcomes.

RANK_REASON The cluster contains a research paper detailing a new method for counterfactual explanations in temporal graphs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method generates counterfactual explanations for temporal graphs

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The cluster contains a research paper detailing a new method for counterfactual explanations in temporal graphs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Minwoo Yu, Young-guk Ha ·

    Breaking Predictions Is Not Enough: Specified-Foil Counterfactuals for Temporal Graphs

    arXiv:2609.11170v1 Announce Type: new Abstract: Temporal graph counterfactual explanations typically change past events to change or invalidate an original prediction, while leaving its replacement unspecified. Yet a user facing a predicted outcome often asks which past condition…

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