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The difference between CNNs and GCNs is that neighbor size is different for different nodes and there is no order in the neighbors of a node (e.g., there is no order for the nodes a, b, c, and d) for the convolutions of GCNs. The difference shown in Figure 4 actually inspired the development of GCNs.

In graph theory, a tree is an undirected graph in which any two vertices are connected by exactly one path, or equivalently a connected acyclic undirected graph.

CNN vs GCN | Image Source. Learn more about basic CNNs by following Convolutional Neural Networks (CNN) with the TensorFlow tutorial. There are two types of GCNs: Spatial Graph Convolutional Networks use spatial features to learn from graphs that are located in spatial space.

Connectedness: A complete graph is a connected graph, which means that there exists a path between any two vertices in the graph. Count of edges: Every vertex in a complete graph has a degree (n-1), where n is the number of vertices in the graph. So total edges are n*(n-1)/2.

GraphSAGE and GAT are inductive, where the entire graph structure need not be known upfront. The aggregation of information from the neighboring node is different in GCN, GraphSAGE, and GAT. GCN runs a simple mean of nearby embeddings, while GraphSAGE uses a generalized aggregate function.

A graph (sometimes called an undirected graph to distinguish it from a directed graph, or a simple graph to distinguish it from a multigraph) is a pair G = (V, E), where V is a set whose elements are called vertices (singular: vertex), and E is a set of paired vertices, whose elements are called edges (sometimes links ...

GNN (Graph Neural Networks) This behaves similarly to an RNN as weights are shared in each recurrent step. In contrast, GCN does not share weights between their hidden layers (For example, Grec below shares the same parameters).