GLISP: A Scalable GNN Learning System by Exploiting Inherent Structural Properties of Graphs

As a powerful tool for modeling graph data, Graph Neural Networks (GNNs) have received increasing attention in both academia and industry. Nevertheless, it is notoriously difficult to deploy GNNs on industrial scale graphs, due to their huge data size and complex topological structures. In this paper, we propose GLISP, a sampling based GNN learning system for industrial scale graphs. By exploiting the inherent structural properties of graphs, such as power law distribution and data locality, GLISP addresses the scalability and performance issues that arise at different stages of the graph learning process. GLISP consists of three core components: graph partitioner, graph sampling service and graph inference engine. The graph partitioner adopts the proposed vertex-cut graph partitioning algorithm AdaDNE to produce balanced partitioning for power law graphs, which is essential for sampling based GNN systems. The graph sampling service employs a load balancing design that allows the one hop sampling request of high degree vertices to be handled by multiple servers. In conjunction with the memory efficient data structure, the efficiency and scalability are effectively improved. The graph inference engine splits the $K$-layer GNN into $K$ slices and caches the vertex embeddings produced by each slice in the data locality aware hybrid caching system for reuse, thus completely eliminating redundant computation caused by the data dependency of graph. Extensive experiments show that GLISP achieves up to $6.53\times$ and $70.77\times$ speedups over existing GNN systems for training and inference tasks, respectively, and can scale to the graph with over 10 billion vertices and 40 billion edges with limited resources.

LogicMP: A Neuro-symbolic Approach for Encoding First-order Logic Constraints

Integrating first-order logic constraints (FOLCs) with neural networks is a crucial but challenging problem since it involves modeling intricate correlations to satisfy the constraints. This paper proposes a novel neural layer, LogicMP, whose layers perform mean-field variational inference over an MLN. It can be plugged into any off-the-shelf neural network to encode FOLCs while retaining modularity and efficiency. By exploiting the structure and symmetries in MLNs, we theoretically demonstrate that our well-designed, efficient mean-field iterations effectively mitigate the difficulty of MLN inference, reducing the inference from sequential calculation to a series of parallel tensor operations. Empirical results in three kinds of tasks over graphs, images, and text show that LogicMP outperforms advanced competitors in both performance and efficiency.

Augmenting Anchors by the Detector Itself

It is difficult to determine the scale and aspect ratio of anchors for anchor-based object detection methods. Current state-of-the-art object detectors either determine anchor parameters according to objects' shape and scale in a dataset, or avoid this problem by utilizing anchor-free method. In this paper, we propose a gradient-free anchor augmentation method named AADI, which means Augmenting Anchors by the Detector Itself. AADI is not an anchor-free method, but it converts the scale and aspect ratio of anchors from a continuous space to a discrete space, which greatly alleviates the problem of anchors' designation. Furthermore, AADI does not add any parameters or hyper-parameters, which is beneficial for future research and downstream tasks. Extensive experiments on COCO dataset show that AADI has obvious advantages for both two-stage and single-stage methods, specifically, AADI achieves at least 2.1 AP improvements on Faster R-CNN and 1.6 AP improvements on RetinaNet, using ResNet-50 model. We hope that this simple and cost-efficient method can be widely used in object detection.

Augmenting Proposals by the Detector Itself

Lacking enough high quality proposals for RoI box head has impeded two-stage and multi-stage object detectors for a long time, and many previous works try to solve it via improving RPN's performance or manually generating proposals from ground truth. However, these methods either need huge training and inference costs or bring little improvements. In this paper, we design a novel training method named APDI, which means augmenting proposals by the detector itself and can generate proposals with higher quality. Furthermore, APDI makes it possible to integrate IoU head into RoI box head. And it does not add any hyperparameter, which is beneficial for future research and downstream tasks. Extensive experiments on COCO dataset show that our method brings at least 2.7 AP improvements on Faster R-CNN with various backbones, and APDI can cooperate with advanced RPNs, such as GA-RPN and Cascade RPN, to obtain extra gains. Furthermore, it brings significant improvements on Cascade R-CNN.