Loading contrib/hagcn/hagcn_layers.py +36 −0 Original line number Diff line number Diff line Loading @@ -27,6 +27,24 @@ class AdaptiveFilter(Layer): init='glorot_uniform', combine_method='linear', **kwargs): """ Parameters ---------- num_nodes: int Number of nodes in the graph num_node_features: int Number of features per node in the graph batch_size: int, optional Batch size used for training k: int, optional k-hop neighborhood to look at init: str, optional Initialization method for the weights combine_method: str, optional How to combine adjacency matrix and node features """ if combine_method not in ['linear', 'prod']: raise ValueError('Combine method needs to be one of linear or product') self.k = k Loading Loading @@ -111,6 +129,24 @@ class KOrderGraphConv(Layer): k=1, init='glorot_uniform', **kwargs): """ Parameters ---------- num_nodes: int Number of nodes in the graph num_node_features: int Number of features per node in the graph batch_size: int, optional Batch size used for training k: int, optional k-hop neighborhood to look at init: str, optional Initialization method for the weights combine_method: str, optional How to combine adjacency matrix and node features """ self.num_nodes = num_nodes self.num_node_features = num_node_features self.k = k Loading Loading
contrib/hagcn/hagcn_layers.py +36 −0 Original line number Diff line number Diff line Loading @@ -27,6 +27,24 @@ class AdaptiveFilter(Layer): init='glorot_uniform', combine_method='linear', **kwargs): """ Parameters ---------- num_nodes: int Number of nodes in the graph num_node_features: int Number of features per node in the graph batch_size: int, optional Batch size used for training k: int, optional k-hop neighborhood to look at init: str, optional Initialization method for the weights combine_method: str, optional How to combine adjacency matrix and node features """ if combine_method not in ['linear', 'prod']: raise ValueError('Combine method needs to be one of linear or product') self.k = k Loading Loading @@ -111,6 +129,24 @@ class KOrderGraphConv(Layer): k=1, init='glorot_uniform', **kwargs): """ Parameters ---------- num_nodes: int Number of nodes in the graph num_node_features: int Number of features per node in the graph batch_size: int, optional Batch size used for training k: int, optional k-hop neighborhood to look at init: str, optional Initialization method for the weights combine_method: str, optional How to combine adjacency matrix and node features """ self.num_nodes = num_nodes self.num_node_features = num_node_features self.k = k Loading
