Graph Neural Networks (GNN):

• Graph Convolutional Networks (GCNs): Using GCNs for node classification, link prediction, and other graph-based tasks. GNNs operate directly on graph structures, learning from node features and their neighbors.

pythonCopy codeimport tensorflow as tf
from tensorflow.keras import layers

class GraphConvolution(layers.Layer):
    def call(self, inputs):
        adjacency_matrix, features = inputs
        aggregated_features = tf.matmul(adjacency_matrix, features)
        return aggregated_features

inputs = layers.Input(shape=(num_nodes, feature_dim))
adj_matrix = layers.Input(shape=(num_nodes, num_nodes))
gcn_layer = GraphConvolution()([adj_matrix, inputs])
output = layers.Dense(num_classes, activation='softmax')(gcn_layer)

model = models.Model([inputs, adj_matrix], output)