The basic idea in the GraphRNN approach is to use a hierarchical RNN to model the edge dependencies in the equation shown below. The first RNN in the hierarchical model is used to model the state of the graph that has been generated so far. The second RNN generates the entries of L[vi, :] in an autoregressive manner. The overall GraphRNN approach is called hierarchical because the node-level RNN is initialized at each time-step with the current hidden state of the graph-level RNN.

GraphRNN

To address some of the limitations of the GraphRNN approach, GRAN model was proposed. GRAN maintains the autoregressive decomposition of the generation process, and instead of using RNNs to model the autoregressive generation process, GRAN uses GNNs. The key idea in GRAN is that we can model the conditional distribution of each row of the adjacency matrix by running a GNN on the graph that has been generated so far.

Graph Recurrent Attention Networks (GRAN)

There are two concrete instantiations of the autoregressive generation
idea: in the first model called GraphRNN, we model autoregressive dependencies using a recurrent neural network (RNN); In the second approach called graph recurrent attention network (GRAN), we generate graphs by using a GNN to condition on the adjacency matrix that has been generated so far.

Hamilton College

Graph Representation Learning is the application of machine learning techniques on graph-structured data. A few examples of growing topics in Graph Representation Learning include techniques for deep graph embeddings, Graph Neural Networks (GNNs), and applications to knowledge graphs.

Graph Representation Learning

Hamilton, W.L. Graph Representation Learning. *Synthesis Lectures on Artificial Intelligence and Machine Learning*. 2020, 14(3), 1–159. https://doi.org/10.2200/S01045ED1V01Y202009AIM046

Graph Representation Learning by William Hamilton

Node embeddings is a way of graph representation. The goal of node embeddings is to map the nodes in the graph into a low-dimensional vector space, where the relations of the nodes in graph can be reflected by the geometric relations in the embedding space. Compared with hand-engineered features, node embeddings are more flexible and they are learned through learning processes.

Node Embeddings

Neighborhood overlap metrics between pairs of nodes are useful in helping us quantify the extent to which a pair of nodes are related, which is import in tasks such as predicting the existence of an edge between two nodes.

Neighborhood Overlap Detection

By examining the eigenvectors of graph Laplacian, the nodes can be clustered into K groups. The steps are as follows:
1. Find the smallest K eigenvectors of the graph Laplacian (excluding the smallest one).
2. Let U be the matrix whose columns are the K eigenvectors, then represent each node $v$ as its corresponding row $z_v$ in U.
3. Run K-means clustering on the embeddings $z_v, \forall v\in V$

K-Clustering of Graph Nodes

Graph is a general data structure  to represent complex data in complex systems. 
It is used to describe and analyze objects/entities with relations/interactions.
A graph is a collection of nodes( objects ) and edges (interactions)


Graph Data structure

Machine learning is inherently a problem-driven discipline, where we seek to build models that can learn from data in order to solve particular tasks. Machine learning on graphs is no different, except the usual categories of supervised and unsupervised are not necessarily the most informative or useful when it comes to graphs.

Machine Learning on Graphs

Traditional approaches to classification using graph data follow the standard machine learning paradigm, where we begin by extracting some statistics or features — based on heuristic functions or domain knowledge — and then use these features as input to a standard machine learning classifier.

Graph Statistics and Kernel Methods

GNNs are a general framework for defining deep neural networks on graph data. The goal of GNNs is to generate representations of nodes that actually depend on the structure of the graph, as well as any feature information available.

Graph Neural Networks (GNNs)

One prominent example of alternative theoretical motivations for the GNN framework is the motivation of GNNs based on connections to variational inference in probabilistic graphical models (PGMs).

In this probabilistic perspective, we view the embeddings $z_{u}, \forall u \in V$ for
each node as latent variables that we attempt to infer. Assume that we observe the graph structure and the input node features X, and the goal is to infer the underlying latent variables hat can explain this observed data. Then the message passing operation that underlies GNNs can be viewed as a neural network analogue of certain message passing algorithms that are commonly used for variational inference to infer distributions over latent variables.

Probabilistic Graphical Models (PGM)

Deep generative models aim to design models that can observe a set of graphs {G1, ..., Gn} and learn to generate graphs with similar characteristics as this training set. These approaches avoid hand-coding particular properties—such as community structure or degree distributions.

Deep Generative Models

Recurrent Models for Graph Generation

The goal of graph generation is to build models that can generate realistic graph structures. In some ways, we can view this graph generation problem as the mirror image of the graph embedding problem. There are three common traditional graph generation approaches:
- Erdös-Rényi (ER) model
- Stochastic Block Model (SBM)
- Preferential Attachment (PA) model

Traditional Graph Generation

There are many important sub-area in graph representation learning. However, the author believe that there’re two key areas that have the potential of pushing fundamentals of graph representation learning forward:

1)	Latent graph inference

2)	Breaking the bottleneck of message passing


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