Author: William L. Hamilton, Rex Ying, and Jure Leskovec
Published: 2017
Why and What
Key word
Inductive learning is learning from the specific data and reasoning to general rules.
Transductive learning is learning from the specific data and reasoning to specific rules.
The embedding method introduced so far is intended for learning the embedding matrix, which is easy to learn.
However, the embedding can be difficult to be used in the evolving graph which is increasing the nodes and edges such as youtube and facebook as time goes by. This method, GraphSAGE, learns the function which governs the mapping of nodes into latent embedding spaces. The function learned in certain graphs can be used across graphs. Therefore, it is useful to capture the features of graphs and generalize the embedding.
This method utilizes the node features such as labels, profile information, and degrees in a supervised manner.
The intuition is below.
Related Work
This algorithm proposed by the author is related to previous nodes embedding approaches, supervised learning over graphs, and graph convolution. Take a look at in a simple way.
Factorization-based embedding approach
There are various kinds of methods making use of the random walk such as DeepWalk and node2vec. The problem existing in these methods is the lack of generalization. These methods are intended for transductive learning, which needs additional optimization when new nodes appear.
Supervised learning over graphs
This include graph kernel approaches which capture the features of the whole graphs and is used for the graph classification. The difference between GraphSAGE and graph kernels is that GraphSage is intended for the node embeddings.
General Information
Author: William L. Hamilton, Rex Ying, and Jure Leskovec Published: 2017
Why and What
Key word
Inductive learning is learning from the specific data and reasoning to general rules. Transductive learning is learning from the specific data and reasoning to specific rules.
The embedding method introduced so far is intended for learning the embedding matrix, which is easy to learn. However, the embedding can be difficult to be used in the evolving graph which is increasing the nodes and edges such as youtube and facebook as time goes by. This method, GraphSAGE, learns the function which governs the mapping of nodes into latent embedding spaces. The function learned in certain graphs can be used across graphs. Therefore, it is useful to capture the features of graphs and generalize the embedding.
This method utilizes the node features such as labels, profile information, and degrees in a supervised manner. The intuition is below.![2018-10-29 14 38 28](https://user-images.githubusercontent.com/38586033/47653252-63ba7780-db88-11e8-8572-f3dafda490c3.png)
Related Work
This algorithm proposed by the author is related to previous nodes embedding approaches, supervised learning over graphs, and graph convolution. Take a look at in a simple way.
Factorization-based embedding approach
There are various kinds of methods making use of the random walk such as DeepWalk and node2vec. The problem existing in these methods is the lack of generalization. These methods are intended for transductive learning, which needs additional optimization when new nodes appear.
Supervised learning over graphs
This include graph kernel approaches which capture the features of the whole graphs and is used for the graph classification. The difference between GraphSAGE and graph kernels is that GraphSage is intended for the node embeddings.
Proposed method
Forward propagation
すでに最適化されたパラメータを持っていると仮定する. アルゴリズムは以下の通り.
推論のステップは以下の二つに分けられる.
Back propagation
コスト関数は以下のように与えられる.
uは目的ノード,vは目的ノードからfixed length random walkをした際に共起するノード.考え方はskipgramのネガティブサンプリングと同じ.
Aggregator Architectures
Aggregation 操作として,mean, LSTM, Max-poolingがある.