Title: Are Graph Attention Networks Attentive Enough? Rethinking Graph Attention by Capturing Homophily and Heterophily. Abstract: Attention Mechanism has been successfully applied in Graph Neural Networks (GNNs). However, as messages propagate along the edges, the node embeddings for edge-connected nodes will become closer even though we can not ensure these nodes have similar features and labels, especially in heterophily graphs. The current attention mechanisms cannot adaptively extract information from the neighbors because they can not fully use the graph information in self-attention calculation. We introduce new a graph attention mechanism (GATv3) straightly involving the graphic information in the self-attention calculation, which can be aware of the homophily or heterophily of the graphs. We conduct an extensive evaluation in node classification tasks and show that using graphic information and features simultaneously can extract more diverse attention scores. Our code is available at https://github.com/anonymousSubscriber/G-GAT