Graph-structured data exhibits universality and widespread applicability across diverse domains, such as social network analysis, biochemistry, financial fraud detection, and network security. Significant strides have been made in leveraging Graph Neural Networks (GNNs) to achieve remarkable success in these areas. However, in real-world scenarios, the training environment for models is often far from ideal, leading to substantial performance degradation of GNN models due to various unfavorable factors, including imbalance in data distribution, the presence of noise in erroneous data, privacy protection of sensitive information, and generalization capability for out-of-distribution (OOD) scenarios. To tackle these issues, substantial efforts have been devoted to improving the performance of GNN models in practical real-world scenarios, as well as enhancing their reliability and robustness. In this paper, we present a comprehensive survey that systematically reviews existing GNN models, focusing on solutions to the four mentioned real-world challenges including imbalance, noise, privacy, and OOD in practical scenarios that many existing reviews have not considered. Specifically, we first highlight the four key challenges faced by existing GNNs, paving the way for our exploration of real-world GNN models. Subsequently, we provide detailed discussions on these four aspects, dissecting how these solutions contribute to enhancing the reliability and robustness of GNN models. Last but not least, we outline promising directions and offer future perspectives in the field.

Recommender systems are widely used in various real-world applications, but they often encounter the persistent challenge of the user cold-start problem. Cross-domain recommendation (CDR), which leverages user interactions from one domain to improve prediction performance in another, has emerged as a promising solution. However, users with similar preferences in the source domain may exhibit different interests in the target domain. Therefore, directly transferring embeddings may introduce irrelevant source-domain collaborative information. In this paper, we propose a novel graph-based disentangled contrastive learning framework to capture fine-grained user intent and filter out irrelevant collaborative information, thereby avoiding negative transfer. Specifically, for each domain, we use a multi-channel graph encoder to capture diverse user intents. We then construct the affinity graph in the embedding space and perform multi-step random walks to capture high-order user similarity relationships. Treating one domain as the target, we propose a disentangled intent-wise contrastive learning approach, guided by user similarity, to refine the bridging of user intents across domains.Extensive experiments on four benchmark CDR datasets demonstrate that DisCo consistently outperforms existing state-of-the-art baselines, thereby validating the effectiveness of both DisCo and its components.

Graph neural networks (GNNs) have shown strong performance in graph fairness learning, which aims to ensure that predictions are unbiased with respect to sensitive attributes. However, existing approaches usually assume that training and test data share the same distribution, which rarely holds in the real world. To tackle this challenge, we propose a novel approach named Dual Unbiased Expansion with Group-acquired Alignment (DANCE) for graph fairness learning under distribution shifts. The core idea of our DANCE is to synthesize challenging yet unbiased virtual graph data in both graph and hidden spaces, simulating distribution shifts from a data-centric view. Specifically, we introduce the unbiased Mixup in the hidden space, prioritizing minor groups to address the potential imbalance of sensitive attributes. Simultaneously, we conduct fairness-aware adversarial learning in the graph space to focus on challenging samples and improve model robustness. To further bridge the domain gap, we propose a group-acquired alignment objective that prioritizes negative pair groups with identical sensitive labels. Additionally, a representation disentanglement objective is adopted to decorrelate sensitive attributes and target representations for enhanced fairness. Extensive experiments demonstrate the superior effectiveness of the proposed DANCE.

Stream processing has been in widespread use, and one of the most common application scenarios is SQL query on streams. By 2021, the global deployment of IoT endpoints reached 12.3 billion, indicating a surge in data generation. However, the escalating demands for high throughput and low latency in stream processing systems have posed significant challenges due to the increasing data volume and evolving user requirements. We present a compression-based stream processing engine, called CompressStreamDB, which enables adaptive fine-grained stream processing directly on compressed streams, to significantly enhance the performance of existing stream processing solutions. CompressStreamDB utilizes nine diverse compression methods tailored for different stream data types and integrates a cost model to automatically select the most efficient compression schemes. CompressStreamDB provides high throughput with low latency in stream SQL processing by identifying and eliminating redundant data among streams. Our evaluation demonstrates that CompressStreamDB improves average performance by 3.84× and reduces average delay by 68.0% compared to the state-of-the-art stream processing solution for uncompressed streams, along with 68.7% space savings. Besides, our edge trials show an average throughput/price ratio of 9.95× and a throughput/power ratio of 7.32× compared to the cloud design.

Stream processing prevails and SQL query on streams has become one of the most popular application scenarios. For example, in 2021, the global number of active IoT endpoints reaches 12.3 billion. Unfortunately, the increasing scale of data and strict user requests place much pressure on existing stream processing systems, requiring high processing throughput with low latency. To further improve the performance of current stream processing systems, we propose a compression-based stream processing engine, called CompressStreamDB, which enables adaptive fine-grained stream processing directly on compressed streams, without decompression. Particularly, CompressStreamDB involves eight compression methods targeting various data types in streams, and it also provides a cost model for dynamically selecting the appropriate compression methods. By exploring data redundancy among streams, CompressStreamDB not only saves space in data transmission between client and server, but also achieves high throughput with low latency in SQL query on stream processing. Our experimental results show that compared to the state-of-the-art stream processing system on uncompressed streams, CompressStreamDB achieves 3.24× throughput improvement and 66.0% lower latency on average. Besides, CompressStreamDB saves 66.8% space.

Huge amounts of data have been generated on edge devices every day, which requires efficient data analytics and management. However, due to the limited computing capacity of these edge devices, query processing at the edge faces tremendous pressure. Fortunately, in recent years, hardware vendors have integrated heterogeneous coprocessors, such as GPUs, into the edge device, which can provide much more computing power. Furthermore, the CPU-GPU integrated edge device has shown significant benefits in a variety of situations. Therefore, the exploration of query processing on such CPU-GPU integrated edge devices becomes an urgent need. In this article, we develop a fine-grained query processing engine, called FineQuery, which can perform efficient query processing on CPU-GPU integrated edge devices. Particularly, FineQuery can take advantage of both architectural features of edge devices and query characteristics by performing fine-grained workload scheduling between the CPU and the GPU. Experiments show that on TPC-H workloads, FineQuery reduces 42.81% latency and improves 2.39× bandwidth utilization on average compared to the implementation of using only GPU or CPU. Furthermore, query processing at the edge can bring significant performance-per-cost benefits and energy efficiency. On average, FineQuery at the edge brings 21× performance-per-cost ratio and 4× energy efficiency compared with processing the data on a discrete GPU platform.

Deep learning has been one of the trendiest research topics. However, as data quantities rise exponentially, training large neural networks can become prohibitively expensive with billions of parameters. Fortunately, recent research has discovered that not all of the computations in traditional network training are necessary. By selectively sparsifying the majority of the neurons during training, we can still obtain acceptable accuracy. SLIDE, a C++ OpenMP-based sub-linear deep learning engine, has been developed in this situation. SLIDE uses the algorithm of locality-sensitive hashing (LSH) to query neurons with high activation in sub-linear time. It achieves a remarkable speedup in training large fully-connected networks by making use of the network sparsity as well as multi-core parallelism. However, SLIDE is limited to CPUs, ignoring the popular GPU devices with greater parallel potential and computational capability. In this paper, we propose G-SLIDE, a GPU-based sub-linear deep learning engine, which combines the benefits of SLIDEs adaptive sparsification algorithms with GPUs high performance. The main challenges in developing G-SLIDE are efficiently using LSH to sparsify networks and training the special sparse neural networks on the GPU. To address these challenges, we propose several novel solutions, such as specific data formats and appropriate workload partitioning for threads to fully utilize the GPU resources. We evaluate G-SLIDE on two extremely sparse datasets with a 2080 Ti GPU, and the results demonstrate that for the time of one training epoch, G-SLIDE can achieve more than 16.4 speedup over SLIDE on a 32-core/64-thread CPU. Furthermore, on the same platform, G-SLIDE can earn an average of 16.2 speedup over TensorFlow-GPU and 30.8 speedup over TensorFlow-CPU.

Using heterogeneous coprocessors, such as GPUs, to accelerate complicated SQL queries has been proved to be effective in the database domain. Previous works show that taking advantage of the high parallelism and computing capacity of heterogeneous coprocessors can bring significant performance improvements. However, in the discrete memory architecture, the advantages of heterogeneous coprocessors will be weakened due to the low PCI-e bandwidth and high latency. Fortunately, hardware vendors have proposed a novel integration architecture design, which integrates CPU and GPU on the same chip. This integrated architecture allows the GPU and CPU to share the same unified memory, taking new opportunities for fine-grained collaboration between the GPU and CPU to optimize SQL queries. In this paper, we propose a query processing engine, called FineQuery, to optimize the execution of SQL queries on the integrated architecture. FineQuery can take advantage of both architectural features and query characteristics by performing fine-grained workload scheduling between the CPU and the GPU. Experimental results show that 1) on the integration architecture, FineQuery can reduce the latency by 25.30% and increase the bandwidth utilization by 39.46% on average. 2) FineQuery on the integrated architecture achieves $13.74\times$ the performance-per-cost ratio and $6.87\times$ energy efficiency over query processing on the discrete GPU platform.