Graph neural networks (GNN) have shown great success in learning from
graph-structured data. They are widely used in various applications, such as
recommendation, fraud detection, and search. In these domains, the graphs are
typically large, containing hundreds of millions of nodes and several billions
of edges. To tackle this challenge, we develop DistDGL, a system for training
GNNs in a mini-batch fashion on a cluster of machines. DistDGL is based on the
Deep Graph Library (DGL), a popular GNN development framework. DistDGL
distributes the graph and its associated data (initial features and embeddings)
across the machines and uses this distribution to derive a computational
decomposition by following an owner-compute rule. DistDGL follows a synchronous
training approach and allows ego-networks forming the mini-batches to include
non-local nodes. To minimize the overheads associated with distributed
computations, DistDGL uses a high-quality and light-weight min-cut graph
partitioning algorithm along with multiple balancing constraints. This allows
it to reduce communication overheads and statically balance the computations.
It further reduces the communication by replicating halo nodes and by using
sparse embedding updates. The combination of these design choices allows
DistDGL to train high-quality models while achieving high parallel efficiency
and memory scalability. We demonstrate our optimizations on both inductive and
transductive GNN models. Our results show that DistDGL achieves linear speedup
without compromising model accuracy and requires only 13 seconds to complete a
training epoch for a graph with 100 million nodes and 3 billion edges on a
cluster with 16 machines. DistDGL is now publicly available as part of
DGL:this https URL

本文介绍了 DistDGL 系统，作为基于 Deep Graph Library（DGL）进行开发的图神经网络（GNN）的分布式训练系统，具有高效、可扩展性等优点。该系统采用了高质量且轻量级的最小割图划分算法，并支持非本地节点组成的自我网络进行小批量训练。经过优化后，该系统可以在线性速度下训练高质量的模型，并且在 16 台机器上只需 13 秒完成 100 万节点和 30 亿边数的图的训练。

亿级图的分布式图神经网络训练

DistDGL: Distributed Graph Neural Network Training for Billion-Scale  Graphs

Most of the modern instance segmentation approaches fall into two categories:
region-based approaches in which object bounding boxes are detected first and
later used in cropping and segmenting instances; and keypoint-based approaches
in which individual instances are represented by a set of keypoints followed by
a dense pixel clustering around those keypoints. Despite the maturity of these
two paradigms, we would like to report an alternative affinity-based paradigm
where instances are segmented based on densely predicted affinities and graph
partitioning algorithms. Such affinity-based approaches indicate that
high-level graph features other than regions or keypoints can be directly
applied in the instance segmentation task. In this work, we propose Deep
Affinity Net, an effective affinity-based approach accompanied with a new graph
partitioning algorithm Cascade-GAEC. Without bells and whistles, our end-to-end
model results in 32.4% AP on Cityscapes val and 27.5% AP on test. It achieves
the best single-shot result as well as the fastest running time among all
affinity-based models. It also outperforms the region-based method Mask R-CNN.

本研究介绍了一种新的基于亲和力及图分割算法的实例分割方法，提出了 Deep Affinity Net 模型和 Cascade-GAEC 算法。实验结果表明，该方法在 Cityscapes val 和 test 数据集上分别取得了 32.4% 和 27.5% 的平均精度，且运行速度为同类亲和力方法中最快的，优于基于区域的方法 Mask R-CNN。