Dynamic Graph Embedding on Event Streams with Apache Flink
Abstract: Graphs are often considered an excellent way of modeling complex real-world problems since they allow to capture relationships between items. Because of their ubiquity, graph embedding techniques have occupied research groups, seeking how vertices can be encoded into a low-dimensional latent space, useful to then perform machine learning. Recently Graph Neural Networks (GNN) have dominated the space of embeddings generation due to their inherent ability to encode latent node dependencies. Moreover, the newly introduced Inductive Graph Neural Networks gained much popularity for inductively learning and representing node embeddings through neighborhood aggregate measures. Even when an entirely new node, unseen during training, appears in the graph, it can still be properly represented by its neighboring nodes. Although this approach appears suitable for dynamic graphs, available systems and training methodologies are agnostic of dynamicity and solely rely on re-processing full graph snapshots in batches, an approach that has been criticized for its high computational costs. This work provides a thorough solution to this particular problem via an efficient prioritybased method for selecting rehearsed samples that guarantees low complexity and high accuracy. Finally, a data-parallel inference method has been evaluated at scale using Apache Flink, a data stream processor for real-time predictions on high volume graph data streams.
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