Optimal Substructure In Graph
%20testing%20graphs%20that%20are%20from%20different%20distributions%20unknown%20during%20training.%20A%20trustworthy%20model%20should%20be%20able%20to%20detect%20OOD%20graphs%20to%20avoid%20unreliable%20predictions%2C%20while%20producing%20accurate%20in-distribution%20(ID)%20predictions.%20To%20achieve%20this%2C%20we%20present%20SGOOD%2C%20a%20novel%20graph-level%20OOD%20detection%20framework.%20We%20find%20that%20substructure%20differences%20commonly%20exist%20between%20ID%20and%20OOD%20graphs%2C%20and%20design%20SGOOD%20with%20a%20series%20of%20techniques%20to%20encode%20task-agnostic%20substructures%20for%20effective%20OOD%20detection.%20Specifically%2C%20we%20build%20a%20super%20graph%20of%20substructures%20for%20every%20graph%2C%20and%20develop%20a%20two-level%20graph%20encoding%20pipeline%20that%20works%20on%20both%20original%20graphs%20and%20super%20graphs%20to%20obtain%20substructure-enhanced%20graph%20representations.%20We%20then%20devise%20substructure-preserving%20graph%20augmentation%20techniques%20to%20further%20capture%20more%20substructure%20semantics%20of%20ID%20graphs.%20Extensive%20experiments%20against%2011%20competitors%20on%20numerous%20graph%20datasets%20demonstrate%20the%20superiority%20of%20SGOOD%2C%20often%20surpassing%20existing%20methods%20by%20a%20significant%20margin.%20The%20code%20is%20available%20at%20https:%2F%2Fgithub.com%2FTommyDzh%2FSGOOD.)
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