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GraphSage vs Pinsage #InsideArangoDB

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ArangoDB Database
ArangoDB Database

The ArangoML Group had a detailed discussion on the topic "GraphSage Vs PinSage" where they shared their thoughts on the difference between the working principles of two popular Graph ML algorithms. The following slidedeck is an accumulation of their thoughts about the comparison between the two algorithms.

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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)