Improving global awareness of linkset predictions using Cross-Attentive Modulation tokens

Most of multiple link prediction or graph generation techniques rely on the attention mechanism or on Graph Neural Networks (GNNs), which consist in leveraging node-level information exchanges in order to form proper link predictions. Such node-level interactions do not process nodes as an ordered sequence, which would imply some kind of natural ordering of the nodes: they are said to be permutation invariant mechanisms. They are well suited for graph problems, but struggle at providing a global orchestration of the predicted links, which can result in a loss of performance. Some typical issues can be the difficulty to ensure high-level properties such as global connectedness, fixed diameter or to avoid information bottleneck effects such as oversmoothing and oversquashing, which respectively consist in abundant smoothing in dense areas leading to a loss of information and a tendency to exclude isolated nodes from the message passing scheme, and often result in irrelevant, unbalanced link predictions. To tackle this problem, we hereby present Cross-Attentive Modulation (CAM) tokens, which introduce cross-attentive units used to condition node and edge-level modulations in order to enable context-aware computations that improve the global consistency of the prediction links. We will implement it on a few permutation invariant architectures, and showcase benchmarks that prove the merits of our work.