This survey paper has provided a comprehensive review of works that are a combination of graph neural networks (GNNs) and meta-learning. They have also provided a thorough review, summary of methods, and applications in these categories. The application of meta-learning to GNNs is a growing and exciting field; many graph problems will benefit immensely from the combination of the two approaches.

1. A survey on methods and
applications of meta-learning with
GNNs
Paper by Debmalya Mandal, Sourav Medya, Brian Uzzi, Charu Aggarwal
Presented by- Shreya Goyal

2. Image from Hacker Noon

3. Meta-Learning:
The subfield of Deep learning and an exciting area
of research as it deals with the problem of having
very few samples to train the model. It works on
the essence of learning to learn to contemplate the
model which can be designed with very few
samples.

4. GNNs (Graph neural networks):
● Generalization of Deep neural networks on graph data is termed as GNN.
● It has been used in various domains to solve complicated problems
having graph-structured data.
● For example, in drug discovery, the goal is to find the group of molecules
that are likely to form a drug where input molecules are represented in a
graph structure.
● In the recommender system, the motive is to find the link between users
and items where these are represented as nodes of graph data.

5. Meta-learning for GNNs:
Despite recent success, GNN has its drawbacks. One of them is to apply GNNs on the problems
having very few samples to train the model. Problems having very large graph dataset, sometimes
have limited number of samples. Moreover, like in the recommender system, it needs to handle
diverse situations in real life and adapt to them with very limited samples.
Recently, meta-learning has unfolded the problem of limited samples in deep learning fields like
natural language processing, robotics, and health care. Meta-learning with GNN can be the spin
for the GNNs. Recently in this direction, several meta-learning methods to train GNNs have been
proposed for various applications. The main challenge in applying meta-learning to graph-
structured data is to determine the type of representation that is shared across tasks and devise
an effective training strategy.

6. Node embedding
The motivation for node embeddings lies in the possibility to capture characteristics of
the nodes of the graph so that any downstream application can directly work with these
representations, without considering the original graph. This problem is often
challenging because there are many nodes with very few connections.
Liu et al. [Liu+20] address this issue by applying meta-learning to the problem of node
embedding of graphs. They set up a regression problem with a common prior to
learning the node embeddings. Here, the training set for this problem is defined by the
higher degree nodes (more no of neighbors) having better accuracy. The testing set is
defined by lower degree nodes having only a few neighbors. To learn the
representation of a testing set, this problem is formulated as a meta-testing problem
and the common prior is adapted with a small number of samples for learning the
embeddings of such nodes.

7. Node classification
The goal of node classification is to find the missing labels of nodes of a partially
labeled graph. Examples of node classification problems are document categorization
and protein classification. These problems have received significant attention in recent
years. The obstacle is many classes are novel i.e., they have very few labeled nodes.
Due to the scarcity of the lack of samples, it is suitable to apply meta-learning
techniques in this problem.
Zhou et al. [Zho+19] apply a meta-learning approach for node classification using a
transferable technique. There are some shared common data between nodes. Shared
data has been used from the classes having many labeled examples and then in meta
testing, the same data is used to classify nodes with few labeled samples.

8. Link prediction
It is the problem of the existence of a link between two nodes in a network. Meta-learning is useful
for learning new relationships via edges in multi-relational graphs. An edge is defined as a triplet of
two nodes and a relation. The goal of link prediction in multi-relational graphs is to predict new
triples given one endpoint of a relation r with observing a few triples about r. This problem is
challenging because a limited number of triplet samples are given for a particular relation r.
Multi-relational graphs are even more difficult to manage with their dynamic nature (addition of new
nodes) over time and the learning is even more difficult when these newly added nodes have only
a few links among them. Baek et al. [BLH20] introduced a link prediction technique, where they
predict the links between the seen and unseen nodes as well as between the unseen nodes. The
main intention is to randomly split the entitled in a given graph into a meta training set and meta
testing set. Training set consists of simulated unseen entities and the testing set consists of real
unseen entities.

9. Node/Edge level shared representation
Shared representations at node/edge level mean for different tasks, nodes or edges are common in a given input
graph. Huang et al. [HZ20] consider the node classification problem where the input graphs, as well as the
labels, can be different across tasks.

10. Node/Edge level shared representation
Here, d(u,v) is the distance of the shortest path between nodes u and v.
Considered the above metric to construct a subgraph because the influence of a
node v on u decreases exponentially as the shortest path distance between them
increases. Then to learn the embedding of node u, feed Su to the GCN. Once we
have embedding for nodes, we can learn any function that maps the encoding to
class labels. They have used MAML (Model agnostic meta-learning) to learn this
function with very few samples on a new task, enjoying the benefits of local
shared representations in node classification.

11. Graph level shared representation
Shared representations at graph level mean for different tasks, the whole graph is a
common/shared part among tasks. A canonical application of this representation is the graph
classification problem, where the goal is to classify a given graph as one of the classes. Graph
classification requires a large number of samples for high-quality prediction. In real-world
problems, a limited number of samples are there for a given label. This problem can be handled by
meta-learning.

12. Graph level shared representation
Chauhan et al. [CNK20] proposed a few-shot graph classification based on graph
spectral measures. In particular, they train a feature-extractor Fθ to extract features
from the graphs in meta-training. For classification, they use two units Csup to predict
the super-class probability of a graph, and CGAT, a graph attention network to predict
the graph class label. During the meta-test phase, the weights of the networks Fθ and
Csup are fixed, and the network CGAT is retrained on the new test classes. As the
feature extractor Fθ is the common shared structure and is not retrained on the test
tasks, this approach requires few samples from new classes.

13. Conclusion
This survey paper has provided a comprehensive review of
works that are a combination of graph neural networks (GNNs)
and meta-learning. They have also provided a thorough review,
summary of methods, and applications in these categories. The
application of meta-learning to GNNs is a growing and exciting
field and many graph problems will benefit immensely from the
combination of the two approaches.

14. References
● https://arxiv.org/pdf/2103.00137.pdf
● https://dl.acm.org/doi/10.1145/3340531.3411910
● https://arxiv.org/pdf/1905.09718.pdf
● https://arxiv.org/pdf/2006.06648.pdf
● https://arxiv.org/pdf/2006.07889.pdf
● https://openreview.net/attachment?id=Bkeeca4Kvr&name=original_pdf
● https://arxiv.org/pdf/2003.08246v1.pdf
● https://arxiv.org/pdf/1609.02907.pdf