Graph anomaly detection (GAD), which aims to identify abnormal nodes that deviate from the majority, has become increasingly important in high-stakes Web domains. However, existing GAD methods follow a ''one model per dataset'' paradigm, leading to high computational costs, substantial data demands, and poor generalization when transferred to new datasets. This calls for a foundation model that enables a ''one-for-all'' GAD solution capable of detecting anomalies across diverse graphs without retraining. Yet, achieving this is challenging due to the large structural and feature heterogeneity across domains. In this paper, we propose TFM4GAD, a simple yet effective framework that adapts tabular foundation models (TFMs) for graph anomaly detection. Our key insight is that the core challenges of foundation GAD, handling heterogeneous features, generalizing across domains, and operating with scarce labels, are the exact problems that modern TFMs are designed to solve via synthetic pre-training and powerful in-context learning. The primary challenge thus becomes structural: TFMs are agnostic to graph topology. TFM4GAD bridges this gap by ''flattening'' the graph, constructing an augmented feature table that enriches raw node features with Laplacian embeddings, local and global structural characteristics, and anomaly-sensitive neighborhood aggregations. This augmented table is processed by a TFM in a fully in-context regime. Extensive experiments on multiple datasets with various TFM backbones reveal that TFM4GAD surprisingly achieves significant performance gains over specialized GAD models trained from scratch. Our work offers a new perspective and a practical paradigm for leveraging TFMs as powerful, generalist graph anomaly detectors.

Recently, Retrieval-Augmented Generation (RAG) has achieved remarkable success in addressing the challenges of Large Language Models (LLMs) without necessitating retraining. By referencing an external knowledge base, RAG refines LLM outputs, effectively mitigating issues such as “hallucination,” lack of domain-specific knowledge, and outdated information. However, the complex structure of relationships among different entities in databases presents challenges for RAG systems. In response, GraphRAG leverages structural information across entities to enable more precise and comprehensive retrieval, capturing relational knowledge and facilitating more accurate, context-aware responses. Given the novelty and potential of GraphRAG, a systematic review of current technologies is imperative. This article provides the first comprehensive overview of GraphRAG methodologies. We formalize the GraphRAG workflow, encompassing Graph-Based Indexing, Graph-Guided Retrieval, and Graph-Enhanced Generation. We then outline the core technologies and training methods at each stage. Additionally, we examine downstream tasks, application domains, evaluation methodologies, and industrial use cases of GraphRAG. Finally, we explore future research directions to inspire further inquiries and advance progress in the field. In order to track recent progress, we set up a repository at https://github.com/pengboci/GraphRAG-Survey .

Temporal subgraph matching aims to identify occurrences of a query graph within a large target graph, subject to certain temporal constraints that require that timestamps on the edges increase in accordance with the direction of the path. Current research on temporal subgraph matching typically identifies each non-temporal match and then filters out occurrences by examining all paths within each occurrence for compliance with temporal constraints. However, this approach proves to be highly inefficient, as it involves excessive unnecessary computation on non-temporal occurrences that do not meet the temporal constraints and can be pruned early during the matching process. Moreover, the constraint examination on all paths within each occurrence further results in numerous redundant timestamp comparisons. Therefore, a high-performance solution is demanded to overcome these drawbacks. In this paper, we introduce TeMatch, a high-performance framework designed to be compatible with any enumeration-based solution for temporal subgraph matching. TeMatch features a novel topological representation of temporal constraints in the query graph, along with three temporal-aware subgraph matching algorithms that enable rapid constraint checking and enhance early pruning and filtration. Extensive experiments reveal that TeMatch efficiently harnesses temporal information to enable early pruning and achieves a speedup of 313.57x while being parallel-friendly, highly compatible, and yielding identical matching results.

Sequential recommendation (SR) aims to predict the next purchasing item according to users' dynamic preference learned from their historical user-item interactions. To improve the performance of recommendation, learning dynamic heterogeneous cross-type behavior dependencies is indispensable for recommender system. However, there still exists some challenges in Multi-Behavior Sequential Recommendation (MBSR). On the one hand, existing methods only model heterogeneous multi-behavior dependencies at behavior-level or item-level, and modeling interaction-level dependencies is still a challenge. On the other hand, the dynamic multi-grained behavior-aware preference is hard to capture in interaction sequences, which reflects interaction-aware sequential pattern. To tackle these challenges, we propose a Multi-Grained Preference enhanced Transformer framework (M-GPT). First, M-GPT constructs an interaction-level graph of historical cross-typed interactions in a sequence. Then graph convolution is performed to derive interaction-level multi-behavior dependency representation repeatedly, in which the complex correlation between historical cross-typed interactions at specific orders can be well learned. Secondly, a novel multifaceted transformer architecture equipped with multi-grained user preference extraction is proposed to encode the interaction-aware sequential pattern enhanced by capturing temporal behavior-aware multi-grained preference . Experiments on the real-world datasets indicate that our method M-GPT consistently outperforms various state-of-the-art recommendation methods. Our code is available at: https://github.com/hchchchchchchc/MGPT.

Graph Transformers (GTs) have recently achieved significant success in the graph domain by effectively capturing both long-range dependencies and graph inductive biases. However, these methods face two primary challenges: (1) multi-view chaos, which results from coupling multi-view information (positional, structural, attribute), thereby impeding flexible usage and the interpretability of the propagation process. (2) local-global chaos, which arises from coupling local message passing with global attention, leading to issues of overfitting and over-globalizing. To address these challenges, we propose a high-level decoupled perspective of GTs, breaking them down into three components and two interaction levels: positional attention, structural attention, and attribute attention, alongside local and global interaction. Based on this decoupled perspective, we design a decoupled graph triple attention network named DeGTA, which separately computes multi-view attentions and adaptively integrates multi-view local and global information. This approach offers three key advantages: enhanced interpretability, flexible design, and adaptive integration of local and global information. Through extensive experiments, DeGTA achieves state-of-the-art performance across various datasets and tasks, including node classification and graph classification. Comprehensive ablation studies demonstrate that decoupling is essential for improving performance and enhancing interpretability. Our code is available at: https://github.com/wangxiaotang0906/DeGTA

Recently, research on Text-Attributed Graphs (TAGs) has gained significant attention due to the prevalence of free-text node features in real-world applications and the advancements in Large Language Models (LLMs) that bolster TAG methodologies. However, current TAG approaches face two primary challenges: (i) Heavy reliance on label information and (ii) Limited cross-domain zero/few-shot transferability. These issues constrain the scaling of both data and model size, owing to high labor costs and scaling laws, complicating the development of graph foundation models with strong transferability. In this work, we propose the GraphCLIP framework to address these challenges by learning graph foundation models with strong cross-domain zero/few-shot transferability through a self-supervised contrastive graph-summary pretraining method. Specifically, we generate and curate large-scale graph-summary pair data with the assistance of LLMs, and introduce a novel graph-summary pretraining method, combined with invariant learning, to enhance graph foundation models with strong cross-domain zero-shot transferability. For few-shot learning, we propose a novel graph prompt tuning technique aligned with our pretraining objective to mitigate catastrophic forgetting and minimize learning costs. Extensive experiments show the superiority of GraphCLIP in both zero-shot and few-shot settings, while evaluations across various downstream tasks confirm the versatility of GraphCLIP. Our code is available at: https://github.com/ZhuYun97/GraphCLIP.

Monotonic graph algorithms, such as shortest path, BFS, and reachability, are fundamental to graph analytics and are widely used across domains. Recent systems employ pruning techniques to accelerate the processing of these algorithms. However, state-of-the-art monotonic graph engines are restricted to in-memory execution and cannot scale to graphs that exceed main memory capacity. In contrast, existing out-of-core graph engines are designed for general-purpose workloads and lack effective pruning mechanisms tailored to monotonic graph algorithms. To bridge this gap, we present Gem, an out-of-core graph engine designed for monotonic graph algorithms. Gem introduces a PageRank-based graph sketch that captures key topological features inmemory with minimal preprocessing overhead. Building on this sketch, we propose a novel graph abstraction that enables the direct derivation of tight bounds for monotonic graph algorithms, supporting effective pruning at both the vertex and partition levels. Comprehensive evaluations on six real-world datasets, including the 42.5-billion-edge ClueWeb graph, show that Gem significantly outperforms existing systems. It achieves up to 135.40× speedup over GridGraph and 12.58× over Wonderland in out-of-core settings, and also delivers substantial improvements in other modes: up to 10.41× over RisGraph in memory and 20.64× over CGgraph out-of-GPU memory.

Community detection in heterogeneous information networks (HINs) poses significant challenges due to the diversity of entity types and the complexity of their interrelations. While traditional algorithms may perform adequately in some scenarios, many struggle with the high memory usage and computational demands of large-scale HINs. To address these challenges, we introduce a novel framework, SCAR, which efficiently uncovers community structures in HINs without requiring network materialization. SCAR leverages insights from meta-paths to interpret multi-relational data through compact vertex-based sketches, significantly reducing computational overhead and materialization overhead. We propose a sketch-based technique for estimating changes in modularity, improving both the precision and speed in community detection. Our extensive evaluations on diverse real-world datasets provide detailed comparative metrics, demonstrating that SCAR outperforms several state-of-the-art methods, including Gdy, Louvain, Leiden, Infomap, Walktrap, and Networkit, in execution time and memory consumption while maintaining competitive accuracy. Overall, SCAR offers a robust and scalable solution for revealing community structures in large HINs, with applications across various domains, including social networks, academic collaboration networks, and e-commerce platforms.

The k -core model has garnered widespread adoption for preserving essential cohesive subgraphs owing to its linear-time computability, making it particularly suitable for hypergraph analysis. However, considering the continuously evolving characteristics of real-world hypergraphs, recent research efforts have focused on developing efficient algorithms that can maintain the core value of each vertex amid structural alterations. Despite these efforts, frequent insertions and deletions in dynamic hypergraphs continue to pose significant inefficiencies, primarily due to the increased traversal overhead incurred by hyperedge insertion algorithms. This exacerbates performance disparities between handling hyperedge insertions and deletions, underscoring the persistent challenge of effective k -core analysis in hypergraphs. To effectively address these challenges, we have gained key insights that enable us to define a specific order, termed the hypergraph k -order, which significantly reduces redundant vertex traversal and narrows down the search space during hyperedge insertions. Based on the proposed hypergraph k -order, we define two indices, the order index and the pivotal index, aimed at minimizing traversal costs and expediting the hyperedge insertion algorithm. Moreover, it is essential to recognize that the recomputation of the support degree ( sd ) for all vertices following each hyperedge deletion can significantly diminish the performance efficiency of deletion algorithms. To address this, we introduce an optimized approach that leverages the incremental maintenance of the support degree ( sd ) value to expedite the hyperedge deletion process. By leveraging these optimizations, we introduce a novel Order-based Traversal core Maintenance methodology, designated as OTM , which markedly enhances the efficiency of core maintenance in dynamic hypergraphs. Our comprehensive evaluation, which covers 12 real-world hypergraph datasets and a synthetic dataset, reveals that OTM achieves staggering speedup, outperforming the state-of-the-art approach with a 41,420 \(\times\) speedup in the insertion algorithm and 8,284 \(\times\) speedup in the deletion algorithm, underscoring its remarkable efficiency and effectiveness.

Message passing scheme is a general idea for Graph Neural Networks (GNNs) to learn node representations. During message passing, given a target node, we transform and aggregate the feature vectors of its neighbors and generate a representation vector for the target node. However, real-world graph data is usually constructed from complicated scenarios based on manually pre-defined rules; it is often the case that noisy information gets involved in message passing, thereby resulting in sub-optimal performance for GNNs and also impacting their trustworthiness and reliability. In this study, we present an effective learnable threshold technique that explicitly optimizes heterogeneous graph structure with the goal to maximize performance improvement of GNNs for downstream tasks. We give an explanation about the design of the learnable threshold and show the ability that our model can be applied to large-scale graphs. Experiments on seven datasets show that our model has a powerful ability to deal with homogeneous graphs with low homophily ratio and dense graphs. With the verification of robustness analysis, our model can resist the noisy information, which proves the robustness of our model.

It is neither effective nor profitable for individuals to attempt to bypass Ant Group's comprehensive risk control system. However, there are still criminals (such as underground loan sharks and professional hackers,), who may teach borrowers how to use hacking techniques to circumvent Ant Group and its partners' risk control system. Despite the fact that our risk control system has greatly reduced fraud losses for merchants in practice, a significant number of intermediary-related frauds still occur. During our investigation into fraud events at Zhima Credit Renting (ZCR), we discovered that more than 30 percent of fraud cases were directly linked to malicious intermediaries. To address this issue, we propose an anti-abettor fraud detection framework, called FAF (Fraud-Abettor-Fraud), specifically designed to combat intermediary-related frauds on an industry-wide scale. We have developed a series of algorithms under the FAF framework, which outperform commonly used risk detection methods and meet real-world business requirements. In this paper, we use ZCR's risk management application as a real-world example-which has deployed FAF for over 1 year-to demonstrate the superiority of the FAF framework compared to existing methods.

Deep Neural Networks (DNNs) generalization is known to be closely related to the flatness of minima, leading to the development of Sharpness-Aware Minimization (SAM) for seeking flatter minima and better generalization. In this paper, we revisit the loss of SAM and propose a more general method, called WSAM, by incorporating sharpness as a regularization term. We prove its generalization bound through the combination of PAC and Bayes-PAC techniques, and evaluate its performance on various public datasets. The results demonstrate that WSAM achieves improved generalization, or is at least highly competitive, compared to the vanilla optimizer, SAM and its variants. The code is available at this link https://github.com/intelligent-machine-learning/dlrover/tree/master/atorch/atorch/optimizers.

Data augmentation has been widely studied as it can be used to improve the generalizability of graph representation learning models. However, existing works focus only on the data augmentation on homogeneous graphs. Data augmentation for heterogeneous graphs remains under-explored. Considering that heterogeneous graphs contain different types of nodes and links, ignoring the type information and directly applying the data augmentation methods of homogeneous graphs to heterogeneous graphs will lead to suboptimal results. In this paper, we propose a novel Multi-Aspect Heterogeneous Graph Augmentation framework named MAHGA. Specifically, MAHGA consists of two core augmentation strategies: structure-level augmentation and metapath-level augmentation. Structure-level augmentation pays attention to network schema aspect and designs a relation-aware conditional variational auto-encoder that can generate synthetic features of neighbors to augment the nodes and the node types with scarce links. Metapath-level augmentation concentrates on metapath aspect, which constructs metapath reachable graphs for different metapaths and estimates the graphons of them. By sampling and mixing up based on the graphons, MAHGA yields intra-metapath and inter-metapath augmentation. Finally, we conduct extensive experiments on multiple benchmarks to validate the effectiveness of MAHGA. Experimental results demonstrate that our method improves the performances across a set of heterogeneous graph learning models and datasets.

Temporal graphs attach time information to edges and are commonly used for implementing time-critical applications that can not be effectively processed by traditional static and dynamic graph processing engines. State-of-the-art solutions that target temporal path problems remain ad-hoc and often suboptimal. A unified and high-performance solution that could efficiently process general temporal path problems via a universal optimization strategy and relieve practitioners from heavy optimization efforts is in urgent demand. In this paper, we make two key observations: (1) temporal path problems can be described as topological-optimum problems and solved by a universal single scan execution model; and (2) data redundancy commonly occurs in the native format of the transformed temporal graphs, which is unnecessary for information propagation and can be eliminated for better memory utilization and execution efficiency. Based on these core insights, we propose TegRaph, the first general-purpose temporal graph computing engine to provide a unified optimization strategy and execution model for general temporal path problems and their applications. TegRaph not only presents temporal information-aware graph representation that naturally fits temporal graphs but also offers general system-level supports such as out-of-core execution. Extensive evaluation reveals that TegRaph can achieve significant speedups over the state-of-the-art designs with up to two orders of magnitude (241×) with the throughput of two hundred million edges per second.

Heterogeneous Graph Neural networks (HGNNs) have been popularly used in processing complicated networks such as academic networks, social networks, and knowledge graphs. Despite their success, the design of the neural architectures of HGNNs still requires rich domain knowledge and heavy manual work. In this paper, we propose a Heterogeneous Graph Neural Architecture Search algorithm (HGNAS for short) which enables automatic design of the best neural architectures with minimal human effort. Specifically, HGNAS first defines a general HGNN framework on top of existing popular HGNNs. A search space of HGNAS is designed based on the general framework that includes multiple groups of message encoding and aggregation functions. Then, HGNAS uses a policy network as the controller to sample and find the best neural architecture from the designed search space by maximizing the expected accuracy of the selected architectures on a validation dataset. Moreover, we introduce effective methods to improve HGNAS from three aspects, i.e., the optimization of hyper-parameters, the improvement of search space, and the selection of message receptive fields. Experiments on public datasets show that HGNAS is capable of designing novel HGNNs that rival the best human-invented HGNNs. More interestingly, HGNAS finds some sparse yet powerful neural architectures for HGNNs on the benchmark datasets.

The large-scale recommender system mainly consists of two stages: matching and ranking. The matching stage (also known as the retrieval step) identifies a small fraction of relevant items from billion-scale item corpus in low latency and computational cost. Item-to-item collaborative filtering (item-based CF) and embedding-based retrieval (EBR) have been long used in the industrial matching stage owing to its efficiency. However, item-based CF is hard to meet personalization, while EBR has difficulty in satisfying diversity. In this paper, we propose a novel matching architecture, Path-based Deep Network (named PDN), through incorporating both personalization and diversity to enhance matching performance. Specifically, PDN is comprised of two modules: Trigger Net and Similarity Net. PDN utilizes Trigger Net to capture the user's interest in each of his/her interacted item. Similarity Net is devised to evaluate the similarity between each interacted item and the target item based on these items' profile and CF information. The final relevance between the user and the target item is calculated by explicitly considering user's diverse interests, \ie aggregating the relevance weights of the related two-hop paths (one hop of a path corresponds to user-item interaction and the other to item-item relevance). Furthermore, we describe the architecture design of the proposed PDN in a leading real-world E-Commerce service (Mobile Taobao App). Based on offline evaluations and online A/B test, we show that PDN outperforms the existing solutions for the same task. The online results also demonstrate that PDN can retrieve more personalized and more diverse items to significantly improve user engagement. Currently, PDN system has been successfully deployed at Mobile Taobao App and handling major online traffic.