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Graph Neural Networks propagate information through local message passing, but the graph topologies themselves can silently prevent any amount of training from solving long-range tasks. When we deploy GNNs on new graphs, there is currently no inexpensive way to know, before training begins, whether the graphs' structures will allow information to travel far enough between distant nodes.
Conflicts of Interest The authors declare no conflicts of interest. References 1 and , “Phishing for Phishing Awareness,” Behaviour & Information Technology 32, no. 6 (2013): 584–593. 2, “ Chapter 1: Banking on Phishing,” in Phishing Exposed, vol. 10 (Science Direct, 2006), 1–35. 3, “Achieving a Consensual Definition of Phishing Based on a Systematic Review of the Literature,” Crime Science 3, no. 1 (2014): 1–10.
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