## Paper总结 - Relational Representation Learning for Dynamic (Knowledge) Graph: A Survey

This paper did a detailed survey on Representation Learning on (Knowledge) Graph. It gives a valid devolopment progress on static KGRL and makes a summary on approaches of Dynamic Graph Representation Learning. They describes existing models from an encode-decode perspective and categorize these encoders and decoders based on the techniques they employ.

And in the end they highlighr directions for future research.

Firtly I want present the devolope line of KGRL. (Understanding big picture of KGRL is very necessary!!)

In this section I present several definations:

Dynamic Graph (Not KG):

1. Continuous-time dynamic graph ($$CTDG$$): $$CTDG = (G,O)$$, $$G$$ representes graph at time $$t_1$$, $$O$$ represents several observations occur after $$t_1$$. This defination of dynamic graph can capture all the changes happened over time.
2. Discrete-time dynamic graph ($$DTDG$$): $$DTDG = (G_1, G_2, ..., G_n)$$. Each $$G_i$$ is a snapshot and representes graph at time $$i$$. $$DTDG$$ may not capture all the changes on a graph because there may be a time interval between two adjacent snapshots.

We call both $$CTDG$$ and $$DTDG$$ dynamic graph. Most existing models for dynamic learning is on $$DTDG$$ because it is easier.

KGs: I think I am a bit of familiar with.

1. Node classification
3. Graph classification

Streaming scenario: A model may not have enough time to retrain compeletly or in part when new observations arrive. Streaming scernarios are often best handled by $$CTDGs$$.

### Encoder-decoder framework for static graph and KGs:

Decoders for static graph: very simple

1. average of two vectors
2. element-wise multiplication of two vectors
3. element-wise absolute value of the difference of the two vectors
4. elemet-wise squared value of the difference of the two vectors

Decoders for static KGs: much more complex. Examples of each catelogue is shown in previous picture.

1. Translation-based models
2. Bilinearity-based models
3. Neural network-based models

Encoders for static graph:

1. High-Order Proximity Matrices: an extension of adjacency matrices
2. Shallow Encoders
3. Decomposition Approaches: its idea is that connected nodes are close to each other in the embedded space.
4. Random Walk Approaches
6. GCN Approaches

Encoders for static KGs: very few compared to static graph

1. shallow encoders
2. GCNs: such as R-GCN

### Encoder-decoder framework for dynamic graph: (Note that no Encoders and Decoders of dynamic KGs are introduced in this survey paper).

1. Time -predicting decoders
2. Time-conditioned decoders
3. Staleness

Encoders for dynamic graph:

1. Aggregating Temporal Observations
2. Aggregating Static Features
3. Time as a Regularizer
4. Decomposition-based Encoders
5. Random Walk Encoders
6. Sequence-Model Encoders
7. Autoencoder-based Encoders

### Applications, Datasets and Codes

Applications:

2. Entity/relation prediction
3. Recommender Systems
4. Time Prediction
5. Node classfication
6. Graph classification
7. Network clustering
2. Currently representation learning algorithms have been mostly designed for $$DTDG$$, with only few works on $$CTDG$$