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GraphSage Vs PinSage
Discussion between the two popular Graph ML
algorithms
ArangoDB ML Reading Group
PinSage
● It is an Inductive based Graph Convolutional Neural Networks (GCNs) for Web-Scale
Recommender Systems
● PinSage is a random-walk based GCNs algorithm which learns embeddings for nodes (in
billions) in web scale graphs
● Due to its inductive nature it is highly-scalable and generic model.
● Altogether, the Pinterest graph (Dataset) contains 2 billion pins, 1 billion boards, and over 18
billion edges (i.e. memberships of pins to their corresponding boards)
● Once embeddings are learned (aka pin embeddings) it can be used for classification,
clustering or reranking.
● It is mainly used by Pinterest for visual recommendations (pins are visual bookmarks e.g. for
buying clothes or other products)
● It solves the problem of operating on entire graph laplacian during training.
PinSage
● Pinterest is a platform in which they
share and organise images.
● Images are referred to as Pins
● Users stack similar images in albums
(boards)
● PinSage simplify the graph, by
forming a Bipartite Graph of
pins — boards
Reference: PinSage
Fig1: PinSage
GraphSage
● An inductive variant of GCNs
● Could be Supervised or Unsupervised or
Semi-Supervised
● Aggregator gathers all of the sampled
neighbourhood information into 1-D
vector representations
● Does not perform on-the-fly
convolutions
● The whole graph needs to be stored in
GPU memory
● Does not support MapReduce Inference
● Perform random sampling to get the
neighbourhood of a node u
PinSage
● Built on top of GraphSage
● Supervised
● It has Convolutions which are same as
aggregators in GraphSage
● It performs on-the-fly convolutions
(sampling the neighbourhood of nodes on
demand)
● The whole graph does not need to be in
memory (using producer consumer
architecture)
● Supports Efficient MapReduce Inference
(while performing aggregation we might
compute convolutions repeatedly which can
be minimized by mapreduce)
● Use Random Walks for neighborhood
sampling (where the neighborhood of a
node u is defined as the T nodes that exert
the most influence on node u)
GraphSage
● Does not support importance pooling
● Does not support Curriculum training
PinSage
● Supports importance pooling where we
compute scores of each and every
neighbour using random walk
● Supports Curriculum training where
algorithm is fed harder-and-harder examples
during training (12% performance gain)
GraphSage vs Pinsage #InsideArangoDB
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GraphSage vs Pinsage #InsideArangoDB

  • 1. GraphSage Vs PinSage Discussion between the two popular Graph ML algorithms ArangoDB ML Reading Group
  • 2. PinSage ● It is an Inductive based Graph Convolutional Neural Networks (GCNs) for Web-Scale Recommender Systems ● PinSage is a random-walk based GCNs algorithm which learns embeddings for nodes (in billions) in web scale graphs ● Due to its inductive nature it is highly-scalable and generic model. ● Altogether, the Pinterest graph (Dataset) contains 2 billion pins, 1 billion boards, and over 18 billion edges (i.e. memberships of pins to their corresponding boards) ● Once embeddings are learned (aka pin embeddings) it can be used for classification, clustering or reranking. ● It is mainly used by Pinterest for visual recommendations (pins are visual bookmarks e.g. for buying clothes or other products) ● It solves the problem of operating on entire graph laplacian during training.
  • 3. PinSage ● Pinterest is a platform in which they share and organise images. ● Images are referred to as Pins ● Users stack similar images in albums (boards) ● PinSage simplify the graph, by forming a Bipartite Graph of pins — boards Reference: PinSage Fig1: PinSage
  • 4. GraphSage ● An inductive variant of GCNs ● Could be Supervised or Unsupervised or Semi-Supervised ● Aggregator gathers all of the sampled neighbourhood information into 1-D vector representations ● Does not perform on-the-fly convolutions ● The whole graph needs to be stored in GPU memory ● Does not support MapReduce Inference ● Perform random sampling to get the neighbourhood of a node u PinSage ● Built on top of GraphSage ● Supervised ● It has Convolutions which are same as aggregators in GraphSage ● It performs on-the-fly convolutions (sampling the neighbourhood of nodes on demand) ● The whole graph does not need to be in memory (using producer consumer architecture) ● Supports Efficient MapReduce Inference (while performing aggregation we might compute convolutions repeatedly which can be minimized by mapreduce) ● Use Random Walks for neighborhood sampling (where the neighborhood of a node u is defined as the T nodes that exert the most influence on node u)
  • 5. GraphSage ● Does not support importance pooling ● Does not support Curriculum training PinSage ● Supports importance pooling where we compute scores of each and every neighbour using random walk ● Supports Curriculum training where algorithm is fed harder-and-harder examples during training (12% performance gain)
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