With recent advancements in graph neural networks (GNNs), spectral GNNs have received increasing popularity by virtue of their ability to retrieve graph signals in the spectral domain. These models feature uniqueness in efficient computation as well as rich expressiveness, which stems from advanced management and profound understanding of graph data. However, few systematic studies have been conducted to assess spectral GNNs, particularly in benchmarking their efficiency, memory consumption, and effectiveness in a unified and fair manner. There is also a pressing need to select spectral models suitable for learning specific graph data and deploying them to massive web-scale graphs, which is currently constrained by the varied model designs and training settings. In this work, we extensively benchmark spectral GNNs with a focus on the spectral perspective, demystifying them as spectral graph filters. We analyze and categorize 35 GNNs with 27 corresponding filters, spanning diverse formulations and utilizations of the graph data. Then, we implement the filters within a unified spectral-oriented framework with dedicated graph computations and efficient training schemes. In particular, our implementation enables the deployment of spectral GNNs over million-scale graphs and various tasks with comparable performance and less overhead. Thorough experiments are conducted on the graph filters with comprehensive metrics on effectiveness and efficiency, offering novel observations and practical guidelines that are only available from our evaluations across graph scales. Different from the prevailing belief, our benchmark reveals an intricate landscape regarding the effectiveness and efficiency of spectral graph filters, demonstrating the potential to achieve desirable performance through tailored spectral manipulation of graph data.

We study the problem of Quality-of-Service (QoS)-Aware Personalized PageRank (PPR) computation. Existing studies mostly focus on improving the PPR query processing time. However, the query processing time alone may not reflect the service quality in real-world PPR-based systems. The query response time can be a more service-relevant measure in many applications such as the online game service of Tencent and the related-pin recommendation module of Pinterest. We make the first attempt at studying QoS-Aware PPR computation and present Quota, a system that adapts the state-of-the-art PPR algorithms to a given environment for minimizing query response time. Equipped with mathematical tools including queuing theory, algorithmic complexity analysis, and constrained optimization, Quota is designed to adapt itself to a wide spectrum of workloads. We conduct extensive experiments on real datasets and show that Quota can reduce the query response time compared with state-of-the-art PPR algorithms, often by a significant margin.

While contrastive self-supervised learning has become the de-facto learning paradigm for graph neural networks, the pursuit of higher task accuracy requires a larger hidden dimensionality to learn informative and discriminative full-precision representations, raising concerns about computation, memory footprint, and energy consumption burden (largely overlooked) for real-world applications. This work explores a promising direction for graph contrastive learning (GCL) with spiking neural networks (SNNs), which leverage sparse and binary characteristics to learn more biologically plausible and compact representations. We propose SpikeGCL, a novel GCL framework to learn binarized 1-bit representations for graphs, making balanced trade-offs between efficiency and performance. We provide theoretical guarantees to demonstrate that SpikeGCL has comparable expressiveness with its full-precision counterparts. Experimental results demonstrate that, with nearly 32x representation storage compression, SpikeGCL is either comparable to or outperforms many fancy state-of-the-art supervised and self-supervised methods across several graph benchmarks.

Graph contrastive learning is a representative self-supervised graph learning that has demonstrated excellent performance in learning node representations. Despite the extensive studies on graph con- trastive learning models, most existing models are tailored to static graphs, hindering their application to real-world graphs which are often dynamically evolving. Directly applying these models to dynamic graphs brings in severe efficiency issues in repetitively updating the learned embeddings. To address this challenge, we propose IDOL, a novel contrastive learning framework for dynamic graph representation learning. IDOL conducts the graph propagation process based on a specially designed Personalized PageRank algorithm which can capture the topological changes incrementally. This effectively eliminates heavy recomputation while maintain- ing high learning quality. Our another main design is a topology-monitorable sampling strategy which lays the foundation of graph contrastive learning. We further show that the design in IDOL achieves a desired performance guarantee. Our experimental results on multiple dynamic graphs show that IDOL outperforms the strongest baselines on node classification tasks in various performance metrics.

Recent years have seen a surge in research on dynamic graph representation learning, which aims to model temporal graphs that are dynamic and evolving constantly over time. However, current work typically models graph dynamics with recurrent neural networks (RNNs), making them suffer seriously from computation and memory overheads on large temporal graphs. So far, scalability of dynamic graph representation learning on large temporal graphs remains one of the major challenges. In this paper, we present a scalable framework, namely SpikeNet, to efficiently capture the temporal and structural patterns of temporal graphs. We explore a new direction in that we can capture the evolving dynamics of temporal graphs with spiking neural networks (SNNs) instead of RNNs. As a low-power alternative to RNNs, SNNs explicitly model graph dynamics as spike trains of neuron populations and enable spike-based propagation in an efficient way. Experiments on three large real-world temporal graph datasets demonstrate that SpikeNet outperforms strong baselines on the temporal node classification task with lower computational costs. Particularly, SpikeNet generalizes to a large temporal graph (2.7M nodes and 13.9M edges) with significantly fewer parameters and computation overheads.

Graph Convolutional Networks (GCNs) achieve an impressive performance due to the remarkable representation ability in learning the graph information. However, GCNs, when implemented on a deep network, require expensive computation power, making them difficult to be deployed on battery-powered devices. In contrast, Spiking Neural Networks (SNNs), which perform a bio-fidelity inference process, offer an energy-efficient neural architecture. In this work, we propose SpikingGCN, an end-to-end framework that aims to integrate the embedding of GCNs with the biofidelity characteristics of SNNs. The original graph data are encoded into spike trains based on the incorporation of graph convolution. We further model biological information processing by utilizing a fully connected layer combined with neuron nodes. In a wide range of scenarios (e.g., citation networks, image graph classification, and recommender systems), our experimental results show that the proposed method could gain competitive performance against state-of-the-art approaches. Furthermore, we show that SpikingGCN on a neuromorphic chip can bring a clear advantage of energy efficiency into graph data analysis, which demonstrates its great potential to construct environment-friendly machine learning models.

Three-dimensional (3D) laser-based simultaneous localization and mapping (SLAM) can provide real-time pose information and construct accurate 3D map. However, detecting loop closures is a challenging task in the 3D laser-based SLAM because of the heavy computational overheads. In this paper, we propose a visual method to simultaneously detect and correct loop closures in the 3D laser-based SLAM based on prior work. In particular, we improve the experiments and evaluate our method by analyzing computational errors. The experimental results on the KITTI dataset prove that our method can efficiently reduce motion accumulation errors and successfully ensure the consistency performance of loop closure correction.

Three-dimensional (3D) laser-based simultaneous localization and mapping (SLAM) can provide real-time pose information and construct accurate 3D map. However, detecting loop closures is a challenge in the 3D laser-based SLAM for expensive computation of algorithms. In this paper, we propose a visual method to detect and correct loop closures. We introduce visual bags-of-words techniques for loop closure detection in the 3D laser-based SLAM. Time of computing similarities between points clouds can be saved. Our method maintains visual keyframes, each of which associates with its pose and segmentation of laser point clouds. Our experiments on KITTI dataset prove that our method can efficiently reduce motion accumulation errors and successfully ensure the real-time performance of loop closure correction.