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%20have%20emerged%20as%20powerful%20tools%20for%20learning%20representations%20of%20graph-structured%20data%2C%20demonstrating%20remarkable%20performance%20across%20various%20tasks.%20Recognising%20their%20importance%2C%20there%20has%20been%20extensive%20research%20focused%20on%20explaining%20GNN%20predictions%2C%20aiming%20to%20enhance%20their%20interpretability%20and%20trustworthiness.%20However%2C%20GNNs%20and%20their%20explainers%20face%20a%20notable%20challenge:%20graphs%20are%20primarily%20designed%20to%20model%20pair-wise%20relationships%20between%20nodes%2C%20which%20can%20make%20it%20tough%20to%20capture%20higher-order%2C%20multi-node%20interactions.%20This%20characteristic%20can%20pose%20difficulties%20for%20existing%20explainers%20in%20fully%20representing%20multi-node%20relationships.%20To%20address%20this%20gap%2C%20we%20present%20Framework%20For%20Higher-Order%20Representations%20In%20Graph%20Explanations%20(FORGE)%2C%20a%20framework%20that%20enables%20graph%20explainers%20to%20capture%20such%20interactions%20by%20incorporating%20higher-order%20structures%2C%20resulting%20in%20more%20accurate%20and%20faithful%20explanations.%20Extensive%20evaluation%20shows%20that%20on%20average%20real-world%20datasets%20from%20the%20GraphXAI%20benchmark%20and%20synthetic%20datasets%20across%20various%20graph%20explainers%2C%20FORGE%20improves%20average%20explanation%20accuracy%20by%201.9x%20and%202.25x%2C%20respectively.%20We%20perform%20ablation%20studies%20to%20confirm%20the%20importance%20of%20higher-order%20relations%20in%20improving%20explanations%2C%20while%20our%20scalability%20analysis%20demonstrates%20FORGE's%20efficacy%20on%20large%20graphs.)
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