Tensors represent multidimensional data and cover various areas of scientific computing. The Canonical Polyadic Decomposition (CPD) emerges to extract latent patterns from large but highly sparse tensors. In real-world scenarios, tensor slices often arrive dynamically over time in streaming form, making traditional CPD algorithms inefficient in processing the entire tensor at each time step. Streaming CPD processes tensor slices incrementally, exploiting a forgetting factor to adjust the weight of historical information to capture dynamics. Current optimizations mainly focus on CPU platforms, failing to meet the real-time processing requirements of modern applications. Efficiently deploying streaming CPD on GPU remains challenging due to frequent data transfers and memory operations throughout the complex workflow, as well as the intricate computational patterns of bottleneck operators.

Price inconsistency between the flight listing page and subsequent booking stages is a critical yet underexplored challenge in large-scale Online Travel Platforms (OTPs). Due to caching latency and real-time inventory dynamics, particularly cabin-class exhaustion, users frequently encounter unexpected fare increases, leading to degraded trust and reduced conversion. While existing ranking and CTCVR models excel at relevance and conversion prediction, they largely ignore the impact of price volatility on user experience. In this work, we formally define the price change aware ranking problem and propose PCANet, Price Change Aware Framework for Mitigating Inconsistencies in Large-Scale Ranking Systems. PCANet integrates three key components: (1) Price Consistency Aligning (PCA), a pre-ranking module that calibrates cached prices using real-time inventory signals; And (2) Price-Sensitive Matching (PSM), a personalized attention mechanism that adapts ranking based on individual user sensitivity to price jumps; Extensive offline experiments on production data and large-scale online A/B tests on Fliggy demonstrate that PCANet significantly improves both ranking accuracy and price consistency, yielding substantial gains in user engagement and booking conversion. To the best of our knowledge, this is the first industrial-scale solution to address price inconsistency in flight ranking systems.

Fuzzy flight search is a vital traffic entry for large-scale platforms like Fliggy, serving over 200k daily active users. Unlike traditional fixed-itinerary searches, fuzzy search involves highly ambiguous intentions and flexible constraints, leading to low conversion rates (1.6% UV-CVR) due to sparse intent signals and complex user trade-offs. Standard ranking models struggle with the domain discrepancy between precise and fuzzy queries, often resulting in a misalignment between offline metrics and online performance. To address these challenges, we propose the Multi-level Cross-scenario Knowledge Distillation (MCKD) framework. MCKD transfers ''dark knowledge'' from a high-capacity Teacher model (trained on precise search data) to a lightweight Student (fuzzy search) model. Our framework introduces three core innovations: 1. Feature-level Hint Learning to align latent semantic representations across heterogeneous feature spaces; 2. Uncertainty-aware Distillation to adaptively weight knowledge transfer based on teacher confidence, mitigating noise propagation; 3. Pairwise Ranking Distillation to explicitly preserve the ranking manifold and relative preference orders. Extensive industrial evaluations and online A/B tests demonstrate that MCKD significantly outperforms state-of-the-art baselines and successfully translates offline gains into substantial online conversion improvements, offering a scalable solution for intent-vague retrieval tasks.

Dynamic graph processing is becoming increasingly critical across a wide range of domains, including social networks, financial transactions, and business intelligence. Its effectiveness relies heavily on optimizations in both storage and analytics, which are essential for improving system performance, throughput, and scalability. While dynamic graph processing has attracted significant research attention and yielded notable progress, a comprehensive analysis that integrates advancements in both dynamic graph storage and analytics remains lacking. To address this gap, this paper presents a thorough review of stateof-the-art techniques that support dynamic graph processing, with a particular focus on storage and analytical methods. Specifically, we first outline the fundamental challenges and core design principles in the field. Then, we systematically classify and summarize existing approaches, encompassing dynamic graph storage and analytics optimizations across both CPU and GPU platforms. Finally, we identify key research gaps and suggest promising directions for future work. This survey presents a comprehensive and up-to-date review of the literature on dynamic graph processing, offering valuable insights for both new and established researchers and contributing to the advancement of the field. The related materials for this paper are available at:https://github.com/yzhang610/DynGraphSurvey.

Dynamic graph storage and analytics have garnered increasing attention and found widespread application across various domains. However, real-world graphs own the characteristics of an inherently sparse, dynamic, and irregular nature, presenting significant challenges for efficient processing. These challenges encompass limited update throughput, suboptimal analytics performance, and difficulty in effectively supporting diverse and evolving workloads. To address these challenges, we design and implement TripleGraph, a high-throughput data structure for dynamic graphs that supports diverse workloads. Specifically, a hash-based bucket array is proposed to store vertices, enabling efficient vertex indexing. Then, we propose a diverse-workload-friendly two-layer edge storage strategy (dynamically mutable short and sorted arrays) that supports highthroughput graph updates while facilitating efficient graph analytics and pattern-matching. Besides, in terms of concurrency control, we introduce a fine-grained optimistic locking coupling scheme and an adaptive optimistic locking conversion mechanism to ensure data consistency and enhance system throughput. Extensive experimental results demonstrate that TripleGraph achieves average speedups of$23.97 \times 1.93 \times, 3.52 \times$, and 3.07 × over STINGER, GraphOne, Teseo, and Sortledton, respectively, on graph update workloads. Moreover, TripleGraph consistently outperforms STINGER, GraphOne, and Teseo in graph analytics and pattern-matching workloads.

Recently, large-scale graph processing on CPU-GPU heterogeneous platforms has attracted considerable attention. However, disparities in memory bandwidth and parallel computational capabilities between CPUs and GPUs, coupled with the irregular structure of graphs and the inherent unpredictability of graph algorithms, often lead to inefficient utilization of CPU-GPU hardware resources, ultimately degrading graph processing performance. To address this, we propose and implement CoDgraph, a co-design framework for high-performance graph processing on CPU-GPU heterogeneous platforms. Specifically, we introduce a fine-grained partitioning strategy to balance workloads, minimize communication overhead, and enhance data locality. Next, we develop an adaptive co-scheduling computing scheme, leveraging a cost model that accounts for CPU and GPU hardware resources to improve system utilization. Finally, to further optimize largescale graph processing, we design and implement an efficient overlapping pipeline execution mode that employs asynchronous parallel execution. Extensive evaluations demonstrate that CoDgraph outperforms state-of-the-art CPU and CPU-GPU graph processing systems, including Ligra (CoDgraph is$14.59 \times$faster on average) and Subway (CoDgraph is$4.17 \times$faster on average). In addition, CoDgraph also has comparable performance to the advanced in-memory graph processing Tigr on GPU and shows good scalability for different graph scales and CPU-GPU heterogeneous platforms.

The paradigm of ``pre-training and prompt-tuning", with its effectiveness and lightweight characteristics, has rapidly spread from the language field to the graph field. Several pioneering studies have designed specialized prompt functions for diverse downstream graph tasks based on various graph pre-training strategies. These prompts concentrate on the compatibility between the pre-training pretext and downstream graph tasks, aiming to bridge the gap between them. However, designing prompts blindly to adapt to downstream tasks based on this concept neglects crucial security issues. By conducting covert attacks on downstream graph data, we find that even when the downstream task data closely matches that of the pre-training tasks, it is still feasible to generate highly misleading prompts using simple deceptive techniques. In this paper, we shift the primary focus of graph prompts from compatibility to vulnerability issues in adversarial attack scenarios. We design a highly extensible shield defense system for the prompts, which enhances their robustness from two perspectives:Direct Handling and Indirect Amplification. When downstream graph data contains unreliable biases, the former directly combats invalid information by incorporating hybrid multi-defense prompts to the input graph's feature space, while the latter adopts a training strategy to bypass the invalid components and amplifies valid part. We provide a theoretical derivation that proves their feasibility, indicating that unbiased prompts exist under certain conditions on unreliable data. Extensive experiments across various scenarios of adversarial attacks (including adaptive and non-adaptive attacks) indicate that the prompts within our defense system exhibit enhanced resilience and superiority. This paper explores a new perspective in graph prompt learning, offering a novel option for robust prompt tuning in downstream tasks.

Masked Graph Autoencoders (MGAEs), represented by GraphMAE and GraphMAE2, which utilize masked feature (or structure) reconstruction strategies, have demonstrated the potential to surpass contrastive learning. However, current masked reconstruction strategies primarily rely on random strategies, only prove effective on reliable graph data. Therefore, these popular methods face immediate robustness deficiencies issues. Firstly, when the graph is unreliable or under adversarial attacks, the selection of nodes for masked reconstruction has a significant impact on downstream tasks. Secondly, the reconstructed features contains redundant components. In this paper, to overcome the non-robustness caused by randomness, we provide a theoretical analysis and evaluation of the robustness of state-of-the-art MGAEs. Additionally, we design two lightweight plug-and-play tools: Box-Based Weighted Reliability Ranking Masking Strategy and Decoupled Feature Reconstruction. Without incurring additional time overhead, these tools provide a defense armor against adversarial attacks for MGAEs, significantly boosting the robustness performance of downstream tasks. Extensive experiments on real-world graphs attacked by various attacks demonstrate our designs have a considerable robust expressive ability. Especially on datasets with large perturbations, the defense performance could even be improved by up to 20%.

With the continuous growth of user scale and application data, the demand for large-scale concurrent graph processing is increasing. Typically, large-scale concurrent graph processing jobs need to process corresponding snapshots of dynamically changing graph data to obtain information at different time points. To enhance the throughput of such applications, current solutions concurrently process multiple graph snapshots on the GPU. However, when dealing with rapidly changing graph data, transferring multiple snapshots of concurrent jobs to the GPU results in high data transfer overhead between CPU and GPU. Additionally, the execution mode of existing work suffers from underutilization of GPU computational resources. In this work, we introduce CGCGraph, which can be integrated into existing GPU graph processing systems like Subway, to enable efficient concurrent graph snapshot processing jobs and enhance overall system resource utilization. The key idea is to offload unshared graph data of multiple concurrent snapshots to the CPU, reducing CPU-GPU transfer overhead. By implementing CPU-GPU co-execution, there is potential for enhanced utilization of GPU computing resources. Specifically, CGCGraph leverages kernel fusion to process shared graph data concurrently on the GPU, while executing all snapshots in parallel on the CPU, with each snapshot assigned a dedicated thread. This approach enables efficient concurrent processing within a novel CPU-GPU co-execution model, incorporating three optimization strategies targeting storage, computation, and synchronization. We integrate CGCGraph with Subway, an existing system designed for out-of-GPU-memory static graph processing. Experimental results show that the integration of CGCGraph with current GPU-based systems obtains performance improvements ranging from 1.7 to 4.5 times.

Recently, Message Passing Neural Networks (MPNNs) have become significant popular frameworks in graph neural networks (GNNs) for solve the graph representation learning(GRL). However, MPNNs overlook the importance of graph topology information and make it challenging to effectively exchange information between nodes with similar structure. To address this issue, we propose a novel model, that serves as an optimization technique being compatible with almost every MPNN model. Our method captures both local and global structural information simultaneously. Additionally, we adopt a topology-aware graph to integrate the local and global structural information into MPNNs. Subsequently, we introduce a model named Structure-Aware Graph Representation Learning (SAGRL), that can capture and exchange graph structural information between nodes with similar structures. We demonstrate the result of our method separately on node classification and graph classification tasks, validating the effectiveness of our approach. Furthermore, we employ visualization and ablation experiments to further validate our method.

Triangle counting is a fundamental problem in graph mining, essential for analyzing graph streams with arbitrary edge orders. However, exact counting becomes impractical due to the massive size of real-world graph streams. To address this, approximate algorithms have been developed, but existing distributed streaming algorithms lack adaptability and struggle with edge deletions. In this article, we propose DTC, a novel family of single-pass distributed streaming algorithms for global and local triangle counting in fully dynamic graph streams. Our DTC-AR algorithm accurately estimates triangle counts without prior knowledge of graph size, leveraging multi-machine resources. Additionally, we introduce DTC-FD, an algorithm tailored for fully dynamic graph streams, incorporating edge insertions and deletions. Using Random Pairing and future edge insertion compensation, DTC-FD achieves unbiased and accurate approximations across multiple machines. Experimental results demonstrate significant improvements over baselines. DTC-AR achieves up to 2029.4× and 27.1× more accuracy, while maintaining the best trade-off between accuracy and storage space. DTC-FD reduces estimation errors by up to 32.5× and 19.3×, scaling linearly with graph stream size. These findings highlight the effectiveness of our proposed algorithms in tackling triangle counting in real-world scenarios. The source code and datasets are released and available at https://github.com/Anonymousview/Real-Time-and-Accurate-Distributed-Triangle-Counting-in-Fully-Dynamic-Graph-Streams.

Superconducting rapid single-flux-quantum (RSFQ) logic has shown great potential for high-energy-efficient computing systems. To ensure correct operations at ultra-high frequencies, it is necessary to incorporate length-matching constraints into the routing problem. Existing routing algorithms, however, can only address 2-pin connections or support the conventional horizontal/vertical routing model, which substantially limits the optimization space for routing solutions. This paper presents JRouter, an RSFQ router that considers the two-layer planar Manhattan routing model while simultaneously coping with splitter (SPL) placement and length-matching multi-terminal routing. JRouter contains a track-assignment-based initial routing that minimizes the initial routing width while avoiding conflicts in the horizontal constraint graph. Moreover, JRouter implements an SPL-tree-based hierarchical routing with an iterative maximum-flow-based formulation to insert the detours for multi-terminal routing. A routing region extension algorithm is also developed to insert the detours for unsatisfied connections. According to the experimental results, JRouter achieves an average routing width reduction of 35.71% and 22.46% on a 16-bit RSFQ Sklansky adder compared to Kito's and Kou's routing algorithms. For randomly generated benchmarks, JRouter reduces the routing width by an average of 38.77%, 38.20%, 21.65%, and 7.01% compared to Kito's, Kou's, and two of Yan's routing algorithms, respectively, while maintaining reasonable runtime.

Triangle counting is a fundamental graph mining problem, widely used in many real-world application scenarios.Due to the large scale of graph streams and limited memory space, it is appropriate to achieve the estimation of global and local triangles by sampling.Existing streaming algorithms for triangle counting can be generalized into two categories.One is Reservoir-based methods employing a fixed memory budget, whose size is difficult to set for accurate estimation without any prior knowledge about graph streams.The other is Bernoullibased methods, which sample edges by a given probability with uncontrollable memory budget.In this work, we propose a novel and bounded-sampling-ratio method, called BSR-Sample, by adaptively resizing memory budget upwards over evolving graph streams.BSR-Sample can keep the sampling ratio always greater than or equal to a specified threshold with available memory space.Then, we design BSR-TC, a single-pass streaming algorithm for both global and local triangle counting, based on BSR-Sample.Experimental results show that BSR-TC achieves accuracy of at least 99.8% for global triangles, when the ratio of initial memory budget to whole graph streams ≥ 0.002% and given threshold = 20%.And our proposed BSR-TC can gain more advantage than the state-of-the-art algorithms over the continuous growth of graph streams.

Triangle counting is a fundamental graph mining problem, widely employed in various real-world application scenarios. Given the large scale of graph streams and limited memory space, it is feasible to achieve the estimation of global and local triangles by sampling. Existing streaming algorithms for triangle counting can be generalized into two categories: Reservoir-based methods and Bernoulli-based methods. The former use a fixed memory budget, whose size is difficult to set for accurate estimation without any prior knowledge about graph streams. The latter sample edges by a specified probability, but memory budget is uncontrollable for following a binomial distribution. In this work, we propose a novel and bounded-sampling-ratio algorithm for both global and local triangle counting, called BSR-TC, by adaptively resizing memory budget upwards over evolving graph streams. Specifically, our proposed single-pass BSR-TC can gain more advantage than the state-of-the-art algorithms over the continuous growth of graph streams. Experimental results show that BSR-TC achieves accuracy of at least 99.8% for global triangles, when the ratio of initial memory budget against whole graph streams [Formula: see text] and given [Formula: see text], respectively.

In this paper, a utility maximization-based resource scheduling and sharing (UM-RSS) scheme is proposed for downlink multiuser multiple-input-multiple-output orthogonal frequency-division multiple access (MU-MIMO-OFDMA) systems. Before performing the UM-RSS scheduling scheme, we first allocate the best antenna sequence for every served user by a suboptimal multiuser antenna selection (MAS) algorithm according to the channel state information (CSI). To balance efficiency and fairness, the integrated scheduling algorithm of UM-RSS is responsible for assigning subcarriers to different users, as well as distributing the assigned subcarriers among multiple services for the same user. For a MU-MIMO-OFDMA system, the joint spatial and frequency scheduling may improve the system spectrum efficiency by exploiting the multiuser diversity gain in both frequency and spatial domain. Finally, numeric result simulated show that the UM-RSS scheduling scheme outperforms traditional scheduling schemes in terms of system throughput, system spectral efficiency, and fairness criterion.