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Found 2 papers, 1 papers with code
Intruding with Words: Towards Understanding Graph Injection Attacks at the Text Level
In this paper, we pioneer the exploration of GIAs at the text level, presenting three novel attack designs that inject textual content into the graph.
EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks
Despite their extraordinary predictive accuracy, existing approaches, such as GCN and GPRGNN, are not robust in the face of homophily changes on test graphs, rendering these models vulnerable to graph structural attacks and with limited capacity in generalizing to graphs of varied homophily levels.
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