NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams
(2312.02473)Abstract
Existing Graph Neural Network (GNN) training frameworks have been designed to help developers easily create performant GNN implementations. However, most existing GNN frameworks assume that the input graphs are static, but ignore that most real-world graphs are constantly evolving. Though many dynamic GNN models have emerged to learn from evolving graphs, the training process of these dynamic GNNs is dramatically different from traditional GNNs in that it captures both the spatial and temporal dependencies of graph updates. This poses new challenges for designing dynamic GNN training frameworks. First, the traditional batched training method fails to capture real-time structural evolution information. Second, the time-dependent nature makes parallel training hard to design. Third, it lacks system supports for users to efficiently implement dynamic GNNs. In this paper, we present NeutronStream, a framework for training dynamic GNN models. NeutronStream abstracts the input dynamic graph into a chronologically updated stream of events and processes the stream with an optimized sliding window to incrementally capture the spatial-temporal dependencies of events. Furthermore, NeutronStream provides a parallel execution engine to tackle the sequential event processing challenge to achieve high performance. NeutronStream also integrates a built-in graph storage structure that supports dynamic updates and provides a set of easy-to-use APIs that allow users to express their dynamic GNNs. Our experimental results demonstrate that, compared to state-of-the-art dynamic GNN implementations, NeutronStream achieves speedups ranging from 1.48X to 5.87X and an average accuracy improvement of 3.97%.
Overview
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NeutronStream is a new framework for training GNN models on dynamic graphs.
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It processes graph changes as a stream using a sliding window to capture dependencies.
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The framework operates in parallel and manages sequential time-dependent events efficiently.
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NeutronStream includes a dynamic graph storage structure for easy implementation of dynamic GNNs.
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It outperforms existing GNN models in speed and accuracy with no loss in accuracy.
Graph neural networks (GNNs), which excel in learning from graph data, are incredibly powerful for analyzing numerous types of data represented in graph form, such as social networks, biological networks, and recommendation systems. Traditional GNN training frameworks assume static graphs, which don't change over time. However, many real-world graphs are dynamic, constantly evolving with the addition of new connections, removal of old ones, or updates to relationships. This poses a challenge for existing GNN frameworks, as they are not designed to handle graph data that changes over time.
To tackle this problem, researchers have developed a new framework called NeutronStream, which specializes in training GNN models on dynamic graph datasets. NeutronStream introduces a novel approach that processes the dynamic graph as a stream of events and utilizes an optimized sliding window technique to incrementally capture the spatial-temporal dependencies of these events. This means that the framework doesn't process the entire graph at once but instead focuses on the most recent changes, adjusting its window of focus as new data comes in.
Moreover, the researchers have designed NeutronStream to perform in parallel, handling the challenge of sequentially processing time-dependent events. Through careful dependency analysis, NeutronStream can identify and process independent events simultaneously, improving performance without sacrificing the model's ability to understand the temporal order of events.
The integration of a built-in graph storage structure also plays a significant role in NeutronStream's effectiveness. It provides efficient and dynamic storage for the evolving graph data, as well as an easy-to-use API that allows users to implement dynamic GNNs with minimal engineering effort.
In comparative experiments, NeutronStream has shown impressive results. Against state-of-the-art implementations on GNN models such as DyRep, LDG, and DGNN, NeutronStream could significantly boost performance, with speedups ranging from 1.48X to 5.87X, and also improve average accuracy by 3.97%. These enhancements are particularly noteworthy because they're achieved without loss of accuracy—a critical factor when dealing with dynamic data.
A key takeaway from this work is the demonstrated need for systems that can efficiently learn from and adapt to continuously changing data. The development and success of NeutronStream highlight the growing importance of dynamic data processing capabilities in the field of graph neural networks. As such, NeutronStream represents a significant step forward in the development of frameworks capable of exploiting the rich, time-sensitive information present in dynamic graphs, opening the door for more sophisticated real-time analysis applications in various domains.
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