Retrieving external knowledge to Augment Generations of downstream task solutions (RAGs) has become a standard practice in powering knowledge-intensive applications. However, real-world knowledge often manifests in heterogeneous yet distinctive structures (e.g., tabular schemas, social networks, and document trees), the effective modeling of which demands specialized modeling, practical engineering, and domain expertise. Meanwhile, adopting RAGs in high-stakes scenarios underscores rigorous safety considerations. Despite the importance of this structural perspective, the current landscape remains fragmented. Moreover, few approaches adequately consider how structured knowledge shapes RAG's safety. Against this backdrop, our tutorial offers a structural perspective on RAGs. We begin by overviewing structured RAGs across their full lifecycle, highlighting their canonical designs. We then examine how design principles can be specialized for different knowledge structures, showcasing their unique applications and security attack/defense strategies. The tutorial slide is available https://kindlab-fly.github.io/tutorials/WSDM26/

Distribution shifts on graphs—the discrepancies in data distribution between training and employing a graph machine learning model—are ubiquitous and often unavoidable in real-world applications. These shifts may severely deteriorate model performance, posing significant challenges for reliable graph machine learning. In recent years, there has been a surge in research on graph machine learning specifically designed to tackle such distribution shifts, aiming to train models to achieve satisfactory performance on Out-of-Distribution (OOD) test data. This survey provides an up-to-date and forward-looking review of deep graph learning under distribution shifts. We categorize the field into three primary scenarios: graph OOD generalization, training-time graph OOD adaptation, and test-time graph OOD adaptation. We begin by formally formulating the problems and discussing various types of distribution shifts that can affect graph learning, such as covariate shifts and concept shifts. To provide a structured understanding of the literature, we introduce a systematic taxonomy that classifies existing methods into model-centric and data-centric approaches, investigating the techniques used in each category. We also summarize commonly used datasets in this research area to facilitate further investigation. Finally, we point out promising research directions and the corresponding challenges to encourage further study in this vital domain. Additionally, we provide a continuously updated reading list at https://github.com/kaize0409/Awesome-Graph-OOD .

Dense retrievers and rerankers are central to retrieval-augmented generation (RAG) pipelines, where accurately retrieving factual information is crucial for maintaining system trustworthiness and defending against RAG poisoning.However, little is known about how much factual competence these components inherit or lose from the large language models (LLMs) they are based on.We pair 12 publicly released embedding checkpoints with their original base LLMs and evaluate both sets on a factuality benchmark.Across every model evaluated, the embedding variants achieve markedly lower accuracy than their bases, with absolute drops ranging from 12 to 43 percentage points (median 28 pts) and typical retriever accuracies collapsing into the 25-35 % band versus the 60-70 % attained by the generative models.This degradation intensifies under a more demanding condition: when the candidate pool per question is expanded from four options to one thousand, the strongest retriever's top-1 accuracy falls from 33 % to 26 %, revealing acute sensitivity to distractor volume.Statistical tests further show that, for every embedding model, cosine-similarity scores between queries and correct completions are significantly higher than those for incorrect ones (𝑝 < 0.01), indicating decisions driven largely by surface-level semantic proximity rather than factual reasoning.To probe this weakness, we employed GPT-4.1 to paraphrase each correct completion, creating a rewritten test set that preserved factual truth while masking lexical cues, and observed that over two-thirds of previously correct predictions flipped to wrong, reducing overall accuracy to roughly one-third of its original level.Taken together, these findings reveal a systematic trade-off introduced by contrastive learning for retrievers: gains in semantic retrieval are paid for with losses in parametric factual knowledge, and the resulting models remain highly vulnerable to adversarial or even benign rephrasings.Our study underscores the need for retrieval objectives that balance similarity with factual fidelity to safeguard next-generation RAG systems against both misinformation and targeted attacks.

Multiple-choice question (MCQ) benchmarks such as MMLU and GPQA are widely used to assess the capabilities of large language models (LLMs). While accuracy remains the standard evaluation metric, recent work has introduced uncertainty quantification (UQ) methods, such as entropy, conformal prediction, and verbalized confidence, as complementary measures of model reliability and calibration. However, we find that these UQ methods, when applied to MCQ tasks, are unexpectedly fragile. Specifically, we show that fine-tuning a model on just 1,000 examples to adjust the probability of the first generated token, under the common prompting setup where the model is instructed to output only a single answer choice, can systematically distort a broad range of UQ methods across models, prompts, and domains, all while leaving answer accuracy unchanged. We validate this phenomenon through extensive experiments on five instruction-tuned LLMs, tested under standard prompting, zero-shot chain-of-thought reasoning, and a biomedical question answering setting. In all cases, models retain similar accuracy but exhibit significantly degraded calibration. These results suggest that current UQ practices for MCQs are ''one-token deep'', driven more by first-token decoding behavior than by any deeper representation of uncertainty, and are easily manipulated through minimal interventions. Our findings call for more robust and interpretable approaches to uncertainty estimation, particularly in structured formats like MCQs, where confidence signals are often reduced to token-level heuristics.

Modern machine learning techniques, particularly deep learning, have showcased remarkable efficacy across numerous knowledge discovery and data mining applications. However, the advancement of these methods is frequently impeded by resource constraint challenges in many scenarios, such as limited labeled data (data-level), small model size requirements in real-world computing platforms (model-level), and efficient mapping of the computations to heterogeneous target hardware (system-level). Addressing all these factors is crucial for effectively and efficiently deploying developed models across a broad spectrum of real-world systems, including large-scale social network analysis, recommendation systems, and real-time anomaly detection. Therefore, there is a critical need to develop efficient learning techniques to address the challenges posed by resource limitations, whether from data, model/algorithm, or system/hardware perspectives. The proposed third international workshop on ''Resource-Efficient Learning for Knowledge Discovery (RelKD 2025)'' will provide a great venue for academic researchers and industrial practitioners to share challenges, solutions, and future opportunities for resource-efficient learning.

Model extraction attacks pose significant security threats to deployed language models, potentially compromising intellectual property and user privacy. This survey provides a comprehensive taxonomy of LLM-specific extraction attacks and defenses, categorizing attacks into functionality extraction, training data extraction, and prompt-targeted attacks. We analyze various attack methodologies including API-based knowledge distillation, direct querying, parameter recovery, and prompt stealing techniques that exploit transformer architectures. We then examine defense mechanisms organized into model protection, data privacy protection, and prompt-targeted strategies, evaluating their effectiveness across different deployment scenarios. We propose specialized metrics for evaluating both attack effectiveness and defense performance, addressing the specific challenges of generative language models. Through our analysis, we identify critical limitations in current approaches and propose promising research directions, including integrated attack methodologies and adaptive defense mechanisms that balance security with model utility. This work serves NLP researchers, ML engineers, and security professionals seeking to protect language models in production environments.

The evolution of previous Click-Through Rate (CTR) models has mainly been driven by proposing complex components, whether shallow or deep, that are adept at modeling feature interactions. However, there has been less focus on improving fusion design. Instead, two naive solutions, stacked and parallel fusion, are commonly used. Both solutions rely on pre-determined fusion connections and fixed fusion operations. It has been repetitively observed that changes in fusion design may result in different performances, highlighting the critical role that fusion plays in CTR models. While there have been attempts to refine these basic fusion strategies, these efforts have often been constrained to specific settings or dependent on specific components. Neural architecture search has also been introduced to partially deal with fusion design, but it comes with limitations. The complexity of the search space can lead to inefficient and ineffective results. To bridge this gap, we introduce OptFusion, a method that automates the learning of fusion, encompassing both the connection learning and the operation selection. We have proposed a one-shot learning algorithm tackling these tasks concurrently. Our experiments are conducted over three large-scale datasets. Extensive experiments prove both the effectiveness and efficiency of OptFusion in improving CTR model performance. Our code implementation is available here https://github.com/kexin-kxzhang/OptFusion.

Recent years have seen a significant shift in Artificial Intelligence from model-centric to data-centric approaches, highlighted by the success of large foundational models. Following this trend, despite numerous innovations in graph machine learning model design, graph-structured data often suffers from data quality issues, jeopardizing the progress of Data-centric AI in graph-structured applications. Our proposed tutorial addresses this gap by raising awareness about data quality issues within the graph machine-learning community. We provide an overview of existing topology, imbalance, bias, limited data, and abnormality issues in graph data. Additionally, we highlight recent developments in foundational graph models that focus on identifying, investigating, mitigating, and resolving these issues.

Graph anomaly detection has long been an important problem in various domains pertaining to information security such as financial fraud, social spam and network intrusion. The majority of existing methods are performed in an unsupervised manner, as labeled anomalies in a large scale are often too expensive to acquire. However, the identified anomalies may turn out to be uninteresting data instances due to the lack of prior knowledge. In real-world scenarios, it is often feasible to obtain limited labeled anomalies, which have great potential to advance graph anomaly detection. However, the work exploring limited labeled anomalies and a large amount of unlabeled nodes in graphs to detect anomalies is relatively limited. Therefore, in this paper, we study an important problem of few-shot graph anomaly detection. Nonetheless, it is challenging to fully leverage the information of few-shot anomalous nodes due to the irregularity of anomalies and the overfitting issue in the few-shot learning. To tackle the above challenges, we propose a novel meta-learning based framework, Meta-g Ad,that learns to adapt the knowledge from self-supervised learning to few-shot supervised learning for graph anomaly detection. In specific, we formulate the problem as a bi-level optimization, ensuring Metagad converging to minimizing the validation loss, thus enhancing the generalization capacity. The comprehensive experiments on six real-world datasets with synthetic anomalies and “organic” anomalies (available in the datasets) demonstrate the effectiveness of Metagad in detecting anomalies with few-shot anomalies. The code is available at https://github.com/XiongxiaoXu/MetaGAD.

Anomalous node detection in a static graph faces significant challenges due to the rarity of anomalies and the substantial cost of labeling their deviant structure and attribute patterns. These challenges give rise to data-centric problems, including extremely imbalanced data distributions and intricate graph learning, which significantly impede machine learning and deep learning methods from discerning the patterns of graph anomalies with few labels. While these issues remain crucial, much of the current research focuses on addressing the induced technical challenges, treating the shortage of labeled data as a given. Distinct from previous efforts, this work focuses on tackling the data-centric problems by generating auxiliary training nodes that conform to the original graph topology and attribute distribution. We categorize this approach as data-centric, aiming to enhance existing anomaly detectors by training them on our synthetic data. However, the methods for generating nodes and the effectiveness of utilizing synthetic data for graph anomaly detection remain unexplored in the realm. To answer these questions, we thoroughly investigate the denoising diffusion model. Drawing from our observations on the diffusion process, we illuminate the shifts in graph energy distribution and establish two principles for designing denoising neural networks tailored to graph anomaly generation. From the insights, we propose a diffusion-based graph generation method to synthesize training nodes, which can be promptly integrated to work with existing anomaly detectors. The empirical results on eight widely-used datasets demonstrate our generated data can effectively enhance the nine state-of-the-art graph detectors' performance.

Graph neural networks (GNNs) based on message passing have achieved remarkable performance in graph machine learning. By combining it with the power of pseudo labeling, one can further push forward the performance on the task of semi-supervised node classification. However, most existing works assume that the training node labels are purely noise-free, while this strong assumption usually does not hold in practice. GNNs will overfit the noisy training labels and the adverse effects of mislabeled nodes can be exaggerated by being propagated to the remaining nodes through the graph structure, exacerbating the model failure. Worse still, the noisy pseudo labels could also largely undermine the model's reliability without special treatment. In this paper, we revisit the role of (1) message passing and (2) pseudo labels in the studied problem and try to address two denoising subproblems from the model architecture and algorithm perspective, respectively. Specifically, we first develop a label-noise robust GNN that discards the coupled message-passing scheme. Despite its simple architecture, this learning backbone prevents overfitting to noisy labels and also inherently avoids the noise propagation issue. Moreover, we propose a novel reliable graph pseudo labeling algorithm that can effectively leverage the knowledge of unlabeled nodes while mitigating the adverse effects of noisy pseudo labels. Based on those novel designs, we can attain exceptional effectiveness and efficiency in solving the studied problem. We conduct extensive experiments on benchmark datasets for semi-supervised node classification with different levels of label noise and show new state-of-the-art performance. The code is available at https://github.com/DND-NET/DND-NET.

In the context of long-tail classification on graphs, the vast majority of existing work primarily revolves around the development of model debiasing strategies, intending to mitigate class imbalances and enhance the overall performance. Despite the notable success, there is very limited literature that provides a theoretical tool for characterizing the behaviors of long-tail classes in graphs and gaining insight into generalization performance in real-world scenarios. To bridge this gap, we propose a generalization bound for long-tail classification on graphs by formulating the problem in the fashion of multi-task learning, i.e., each task corresponds to the prediction of one particular class. Our theoretical results show that the generalization performance of long-tail classification is dominated by the overall loss range and the task complexity. Building upon the theoretical findings, we propose a novel generic framework HierTail for long-tail classification on graphs. In particular, we start with a hierarchical task grouping module that allows us to assign related tasks into hypertasks and thus control the complexity of the task space; then, we further design a balanced contrastive learning module to adaptively balance the gradients of both head and tail classes to control the loss range across all tasks in a unified fashion. Extensive experiments demonstrate the effectiveness of HierTail in characterizing long-tail classes on real graphs, which achieves up to 12.9% improvement over the leading baseline method in balanced accuracy.

Modern machine learning techniques, particularly deep learning, have showcased remarkable efficacy across numerous knowledge discovery and data mining applications. However, the advancement of many of these methods is frequently impeded by resource constraint challenges in many scenarios, such as limited labeled data (data-level), small model size requirements in real-world computing platforms (model-level), and efficient mapping of the computations to heterogeneous target hardware (system-level). Addressing all these factors is crucial for effectively and efficiently deploying developed models across a broad spectrum of real-world systems, including large-scale social network analysis, recommendation systems, and real-time anomaly detection. Therefore, there is a critical need to develop efficient learning techniques to address the challenges posed by resource limitations, whether from data, model/algorithm, or system/hardware perspectives. The proposed international workshop on "Resource-Efficient Learning for Knowledge Discovery (RelKD 2024)" will provide a great venue for academic researchers and industrial practitioners to share challenges, solutions, and future opportunities of resource-efficient learning.

Graphs, which encode pairwise relations between entities, are a kind of universal data structure for a lot of real-world data, including social networks, transportation networks, and chemical molecules. Many important applications on these data can be treated as computational tasks on graphs. Recently, machine learning techniques are widely developed and utilized to effectively tame graphs for discovering actionable patterns and harnessing them for advancing various graph-related computational tasks. Huge success has been achieved and numerous real-world applications have benefited from it. However, since in today's world, we are generating and gathering data in a much faster and more diverse way, real-world graphs are becoming increasingly large-scale and complex. More dedicated efforts are needed to propose more advanced machine learning techniques and properly deploy them for real-world applications in a scalable way. Thus, we organize The 5th International Workshop on Machine Learning on Graphs (MLoG) (https://mlog-workshop.github.io/wsdm24.html), held in conjunction with the 17th ACM Conference on Web Search and Data Mining (WSDM), which provides a venue to gather academia researchers and industry researchers/practitioners to present the recent progress on machine learning on graphs.

Graph machine learning (Graph ML) models typically require abundant labeled instances to provide sufficient supervision signals, which is commonly infeasible in real-world scenarios since labeled data for newly emerged concepts (e.g., new categorizations of nodes) on graphs is rather limited. To efficiently learn with a small amount of data on graphs, meta-learning has been investigated in Graph ML. By transferring the knowledge learned from previous experiences to new tasks, graph meta-learning approaches have demonstrated promising performance on few-shot graph learning problems. However, most existing efforts predominately assume that all the data from the seen classes is gold labeled, yet those methods may lose their efficacy when the seen data is weakly labeled with severe label noise. As such, we aim to investigate a novel problem of weakly supervised graph meta-learning for improving the model robustness in terms of knowledge transfer. To achieve this goal, we propose Meta-GIN (Meta Graph Interpolation Network), a new graph meta-learning framework. Based on a new robustness-enhanced episodic training paradigm, Meta-GIN is meta-learned to interpolate node representations from weakly labeled data and extracts highly transferable meta-knowledge, which enables the model to quickly adapt to unseen tasks with few labeled instances. Extensive experiments demonstrate the superiority of Meta-GIN over existing graph meta-learning studies on the task of weakly supervised few-shot node classification.

The capacity to anticipate streamflow is critical to the efficient functioning of reservoir systems as it gives vital information to reservoir operators about water release quantities as well as help quantify the impact of environmental factors on downstream water quality. Yet, streamflow modelling is difficult owing to the intricate interactions between different watershed outlets. In this paper, we argue that one possible solution to this problem is to identify the causal structure of these outlets, which would allow for the identification of crucial watershed outlets while capturing the spatiotemporally informed complex relationships leading to improved hydrological resource management. However, due to the inherent complexity of spatiotemporal causal learning problems, extending existing causal discovery methods to a whole basin is a major hurdle. To address these issues, we offer STREAMS, a new framework that uses Reinforcement Learning (RL) to optimize the search space for causal discovery and an LSTM-GCN based autoencoder to infer spatiotemporal causal features for streamflow rate prediction. We conduct extensive experiments on the Brazos river basin carried out within the scope of a US Army Corps of Engineers, Engineering With Nature Initiative project, including empirical studies of generalization performance to verify the nature of the inferred relationships.

Graph anomaly detection aims to identify the atypical substructures and has attracted an increasing amount of research attention due to its profound impacts in a variety of application domains, including social network analysis, security, finance, and many more. The lack of prior knowledge of the ground-truth anomaly has been a major obstacle in acquiring fine-grained annotations (e.g., anomalous nodes), therefore, a plethora of existing methods have been developed either with a limited number of node-level supervision or in an unsupervised manner. Nonetheless, annotations for coarse-grained graph elements (e.g., a suspicious group of nodes), which often require marginal human effort in terms of time and expertise, are comparatively easier to obtain. Therefore, it is appealing to investigate anomaly detection in a weakly-supervised setting and to establish the intrinsic relationship between annotations at different levels of granularity. In this paper, we tackle the challenging problem of weakly-supervised graph anomaly detection with coarse-grained supervision by (1) proposing a novel architecture of graph neural network with attention mechanism named WEDGE that can identify the critical node-level anomaly given a few labels of anomalous subgraphs, and (2) designing a novel objective with contrastive loss that facilitates node representation learning by enforcing distinctive representations between normal and abnormal graph elements. Through extensive evaluations on real-world datasets, we corroborate the efficacy of our proposed method, improving AUC-ROC by up to 16.48% compared to the best competitor.

Graph Neural Networks (GNNs) have exhibited impressive performance in many graph learning tasks. Nevertheless, the performance of GNNs can deteriorate when the input graph data suffer from weak information, i.e., incomplete structure, incomplete features, and insufficient labels. Most prior studies, which attempt to learn from the graph data with a specific type of weak information, are far from effective in dealing with the scenario where diverse data deficiencies exist and mutually affect each other. To fill the gap, in this paper, we aim to develop an effective and principled approach to the problem of graph learning with weak information (GLWI). Based on the findings from our empirical analysis, we derive two design focal points for solving the problem of GLWI, i.e., enabling long-range propagation in GNNs and allowing information propagation to those stray nodes isolated from the largest connected component. Accordingly, we propose D2PT, a dual-channel GNN framework that performs long-range information propagation not only on the input graph with incomplete structure, but also on a global graph that encodes global semantic similarities. We further develop a prototype contrastive alignment algorithm that aligns the class-level prototypes learned from two channels, such that the two different information propagation processes can mutually benefit from each other and the finally learned model can well handle the GLWI problem. Extensive experiments on eight real-world benchmark datasets demonstrate the effectiveness and efficiency of our proposed methods in various GLWI scenarios.

Federated Learning (FL) enables multiple clients to collaboratively learn a machine learning model without exchanging their own local data. In this way, the server can exploit the computational power of all clients and train the model on a larger set of data samples among all clients. Although such a mechanism is proven to be effective in various fields, existing works generally assume that each client preserves sufficient data for training. In practice, however, certain clients can only contain a limited number of samples (i.e., few-shot samples). For example, the available photo data taken by a specific user with a new mobile device is relatively rare. In this scenario, existing FL efforts typically encounter a significant performance drop on these clients. Therefore, it is urgent to develop a few-shot model that can generalize to clients with limited data under the FL scenario. In this paper, we refer to this novel problem as federated few-shot learning. Nevertheless, the problem remains challenging due to two major reasons: the global data variance among clients (i.e., the difference in data distributions among clients) and the local data insufficiency in each client (i.e., the lack of adequate local data for training). To overcome these two challenges, we propose a novel federated few-shot learning framework with two separately updated models and dedicated training strategies to reduce the adverse impact of global data variance and local data insufficiency. Extensive experiments on four prevalent datasets that cover news articles and images validate the effectiveness of our framework compared with the state-of-the-art baselines.

Learning expressive representations for high-dimensional yet sparse features has been a longstanding problem in information retrieval. Though recent deep learning methods can partially solve the problem, they often fail to handle the numerous sparse features, particularly those tail feature values with infrequent occurrences in the training data. Worse still, existing methods cannot explicitly leverage the correlations among different instances to help further improve the representation learning on sparse features since such relational prior knowledge is not provided. To address these challenges, in this paper, we tackle the problem of representation learning on feature-sparse data from a graph learning perspective. Specifically, we propose to model the sparse features of different instances using hypergraphs where each node represents a data instance and each hyperedge denotes a distinct feature value. By passing messages on the constructed hypergraphs based on our Hypergraph Transformer (HyperFormer), the learned feature representations capture not only the correlations among different instances but also the correlations among features. Our experiments demonstrate that the proposed approach can effectively improve feature representation learning on sparse features.

Most existing deep learning models are trained based on the closed-world assumption, where the test data is assumed to be drawn i.i.d. from the same distribution as the training data, known as in-distribution (ID). However, when models are deployed in an open-world scenario, test samples can be out-of-distribution (OOD) and therefore should be handled with caution. To detect such OOD samples drawn from unknown distribution, OOD detection has received increasing attention lately. However, current endeavors mostly focus on grid-structured data and its application for graph-structured data remains under-explored. Considering the fact that data labeling on graphs is commonly time-expensive and labor-intensive, in this work we study the problem of unsupervised graph OOD detection, aiming at detecting OOD graphs solely based on unlabeled ID data. To achieve this goal, we develop a new graph contrastive learning framework GOOD-D for detecting OOD graphs without using any ground-truth labels. By performing hierarchical contrastive learning on the augmented graphs generated by our perturbation-free graph data augmentation method, GOOD-D is able to capture the latent ID patterns and accurately detect OOD graphs based on the semantic inconsistency in different granularities (i.e., node-level, graph-level, and group-level). As a pioneering work in unsupervised graph-level OOD detection, we build a comprehensive benchmark to compare our proposed approach with different state-of-the-art methods. The experiment results demonstrate the superiority of our approach over different methods on various datasets.

Few-shot node classification aims at classifying nodes with limited labeled nodes as references. Recent few-shot node classification methods typically learn from classes with abundant labeled nodes (i.e., meta-training classes) and then generalize to classes with limited labeled nodes (i.e., meta-test classes). Nevertheless, on real-world graphs, it is usually difficult to obtain abundant labeled nodes for many classes. In practice, each meta-training class can only consist of several labeled nodes, known as the extremely weak supervision problem. In few-shot node classification, with extremely limited labeled nodes for meta-training, the generalization gap between meta-training and meta-test will become larger and thus lead to suboptimal performance. To tackle this issue, we study a novel problem of few-shot node classification with extremely weak supervision and propose a principled framework X-FNC under the prevalent meta-learning framework. Specifically, our goal is to accumulate meta-knowledge across different meta-training tasks with extremely weak supervision and generalize such knowledge to meta-test tasks. To address the challenges resulting from extremely scarce labeled nodes, we propose two essential modules to obtain pseudo-labeled nodes as extra references and effectively learn from extremely limited supervision information. We further conduct extensive experiments on four node classification datasets with extremely weak supervision to validate the superiority of our framework compared to the state-of-the-art baselines.

Nowadays, fake news easily propagates through online social networks and becomes a grand threat to individuals and society. Assessing the authenticity of news is challenging due to its elaborately fabricated contents, making it difficult to obtain large-scale annotations for fake news data. Due to such data scarcity issues, detecting fake news tends to fail and overfit in the supervised setting. Recently, graph neural networks (GNNs) have been adopted to leverage the richer relational information among both labeled and unlabeled instances. Despite their promising results, they are inherently focused on pairwise relations between news, which can limit the expressive power for capturing fake news that spreads in a group-level. For example, detecting fake news can be more effective when we better understand relations between news pieces shared among susceptible users. To address those issues, we propose to leverage a hypergraph to represent group-wise interaction among news, while focusing on important news relations with its dual-level attention mechanism. Experiments based on two benchmark datasets show that our approach yields remarkable performance and maintains the high performance even with a small subset of labeled news data.

For real-world graph data, the node class distribution is inherently imbalanced and long-tailed, which naturally leads to a few-shot learning scenario with limited nodes labeled for newly emerging classes. Existing efforts are carefully designed to solve such a few-shot learning problem via data augmentation, learning transferable initialization, to name a few. However, most, if not all, of them are based on a strong assumption that all the test nodes must exclusively come from novel classes, which is impractical in real-world applications. In this paper, we study a broader and more realistic problem named generalized few-shot node classification, where the test samples can be from both novel classes and base classes. Compared with the standard fewshot node classification, this new problem imposes several unique challenges, including asymmetric classification and inconsistent preference. To counter those challenges, we propose a shot-aware graph neural network (STAGER) equipped with an uncertainty-based weight assigner module for adaptive propagation. To formulate this problem from the meta-learning perspective, we propose a new training paradigm named imbalanced episodic training to ensure the label distribution is consistent between the training and test scenarios. Experiment results on four real-world datasets demonstrate the efficacy of our model, with up to 14% accuracy improvement over baselines.

To model graph-structured data, graph learning, in particular deep graph learning with graph neural networks, has drawn much attention in both academic and industrial communities lately. The effectiveness of prevailing graph learning methods usually rely on abundant labeled data for model training. However, it is common that graphs are scarcely labeled since data annotation and labeling on graphs is always time and resource-consuming. Therefore, it is imperative to investigate graph learning with minimal human supervision for the low-resource settings where limited or even no labeled data is available. In this tutorial, we will focus on the state-of-the-art techniques of Graph Minimally-Supervised Learning, in particular a series of weakly-supervised learning, few-shot learning, and self-supervised learning methods on graph-structured data as well as their real-world applications. The objectives of this tutorial are to: (1) formally categorize the problems in graph minimally-supervised learning and discuss the challenges under different learning scenarios; (2) comprehensively review the existing and recent advances of graph minimally-supervised learning; and (3) elucidate open questions and future research directions. This tutorial introduces major topics within minimally-supervised learning and offers a guide to a new frontier of graph learning.

Node classification is of great importance among various graph mining tasks. In practice, real-world graphs generally follow the long-tail distribution, where a large number of classes only consist of limited labeled nodes. Although Graph Neural Networks (GNNs) have achieved significant improvements in node classification, their performance decreases substantially in such a few-shot scenario. The main reason can be attributed to the vast generalization gap between meta-training and meta-test due to the task variance caused by different node/class distributions in meta-tasks (i.e., node-level and class-level variance). Therefore, to effectively alleviate the impact of task variance, we propose a task-adaptive node classification framework under the few-shot learning setting. Specifically, we first accumulate meta-knowledge across classes with abundant labeled nodes. Then we transfer such knowledge to the classes with limited labeled nodes via our proposed task-adaptive modules. In particular, to accommodate the different node/class distributions among meta-tasks, we propose three essential modules to perform node-level, class-level, and task-level adaptations in each meta-task, respectively. In this way, our framework can conduct adaptations to different meta-tasks and thus advance the model generalization performance on meta-test tasks. Extensive experiments on four prevalent node classification datasets demonstrate the superiority of our framework over the state-of-the-art baselines. Our code is provided at https://github.com/SongW-SW/TENT https://github.com/SongW-SW/TENT.

Graphs are widely used for abstracting complex systems of interacting objects, such as social networks, knowledge graphs, and traffic networks, as well as for modeling molecules, manifolds, and source code. To model such graph-structured data, graph learning, in particular deep graph learning with graph neural networks, has drawn much attention in both academic and industrial communities lately. Prevailing graph learning methods usually rely on learning from "big'' data, requiring a large amount of labeled data for model training. However, it is common that graphs are associated with "small'' labeled data as data annotation and labeling on graphs is always time and resource-consuming. Therefore, it is imperative to investigate graph learning with minimal human supervision for the low-resource settings where limited or even no labeled data is available. In this tutorial, we will focus on the state-of-the-art techniques of Graph Minimally-supervised Learning, in particular a series of weakly-supervised learning, few-shot learning, and self-supervised learning methods on graph-structured data as well as their real-world applications. The objectives of this tutorial are to: (1) formally categorize the problems in graph minimally-supervised learning and discuss the challenges under different learning scenarios; (2) comprehensively review the existing and recent advances of graph minimally-supervised learning; and (3) elucidate open questions and future research directions. This tutorial introduces major topics within minimally-supervised learning and offers a guide to a new frontier of graph learning. We believe this tutorial is beneficial to researchers and practitioners, allowing them to collaborate on graph learning.

The ability to incrementally learn new classes is vital to all real-world artificial intelligence systems. A large portion of high-impact applications like social media, recommendation systems, E-commerce platforms, etc. can be represented by graph models. In this paper, we investigate the challenging yet practical problem,Graph Few-shot Class-incremental (Graph FCL) problem, where the graph model is tasked to classify both newly encountered classes and previously learned classes. Towards that purpose, we put forward a Graph Pseudo Incremental Learning paradigm by sampling tasks recurrently from the base classes, so as to produce an arbitrary number of training episodes for our model to practice the incremental learning skill. Furthermore, we design a Hierarchical-Attention-based Graph Meta-learning framework, HAG-Meta from an optimization perspective. We present a task-sensitive regularizer calculated from task-level attention and node class prototypes to mitigate overfitting onto either novel or base classes. To employ the topological knowledge, we add a node-level attention module to adjust the prototype representation. Our model not only achieves greater stability of old knowledge consolidation, but also acquires advantageous adaptability to new knowledge with very limited data samples. Extensive experiments on three real-world datasets, including Amazon-clothing, Reddit, and DBLP, show that our framework demonstrates remarkable advantages in comparison with the baseline and other related state-of-the-art methods.

Deep matching models aim to facilitate search engines retrieving more relevant documents by mapping queries and documents into semantic vectors in the first-stage retrieval. When leveraging BERT as the deep matching model, the attention score across two words are solely built upon local contextualized word embeddings. It lacks prior global knowledge to distinguish the importance of different words, which has been proved to play a critical role in information retrieval tasks. In addition to this, BERT only performs attention across sub-words tokens which weakens whole word attention representation. We propose a novel Global Weighted Self-Attention (GLOW) network for web document search. GLOW fuses global corpus statistics into the deep matching model. By adding prior weights into attention generation from global information, like BM25, GLOW successfully learns weighted attention scores jointly with query matrix Q and key matrix K. We also present an efficient whole word weight sharing solution to bring prior whole word knowledge into sub-words level attention. It aids Transformer to learn whole word level attention. To make our models applicable to complicated web search scenarios, we introduce combined fields representation to accommodate documents with multiple fields even with variable number of instances. We demonstrate GLOW is more efficient to capture the topical and semantic representation both in queries and documents. Intrinsic evaluation and experiments conducted on public data sets reveal GLOW to be a general framework for document retrieve task. It significantly outperforms BERT and other competitive baselines by a large margin while retaining the same model complexity with BERT. The source code is available at https://github.com/GLOW-deep/GLOW.

In general, graph neural networks (GNNs) adopt the message-passing scheme to capture the information of a node (i.e., nodal attributes, and local graph structure) by iteratively transforming, aggregating the features of its neighbors. Nonetheless, recent studies show that the performance of GNNs can be easily hampered by the existence of abnormal or malicious nodes due to the vulnerability of neighborhood aggregation. Thus it is necessary to learn anomaly-resistant GNNs without the prior knowledge of ground-truth anomalies, given the fact that labeling anomalies is costly and requires intensive domain knowledge. Though removing anomalies through unsupervised anomaly detection methods could be a possible solution, it may render unreasonable GNN model performance on target tasks due to the non-differentiable gap between the two learning procedures. In order to keep the effectiveness of GNNs on anomaly-contaminated graphs, in this paper, we propose a new framework named RARE-GNN (Reinforced Anomaly-REsistant Graph Neural Networks) which can detect anomalies from the input graph and learn anomaly-resistant GNNs simultaneously. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed framework.

Graph Neural Networks have recently become a prevailing paradigm for various high-impact graph analytical problems. Existing efforts can be mainly categorized as spectral-based and spatial-based methods. The major challenge for the former is to find an appropriate graph filter to distill discriminative information from input signals for learning. Recently, myriads of explorations are made to achieve better graph filters, e.g., Graph Convolutional Network (GCN), which leverages Chebyshev polynomial truncation to seek an approximation of graph filters and bridge these two families of methods. Nevertheless, it has been shown in recent studies that GCN and its variants are essentially employing fixed low-pass filters to perform information denoising. Thus their learning capability is rather limited and may over-smooth node representations at deeper layers. To tackle these problems, we develop a novel graph neural network framework AdaGNN with a well-designed adaptive frequency response filter. At its core, AdaGNN leverages a simple but elegant trainable filter that spans across multiple layers to capture the varying importance of different frequency components for node representation learning. The inherent differences among different feature channels are also well captured by the filter. As such, it empowers AdaGNN with stronger expressiveness and naturally alleviates the over-smoothing problem. We empirically validate the effectiveness of the proposed framework on various benchmark datasets. Theoretical analysis is also provided to show the superiority of the proposed AdaGNN. The open-source implementation of AdaGNN can be found here: https://github.com/yushundong/AdaGNN.

Session-based recommender systems aim to improve recommendations in short-term sessions that can be found across many platforms.A critical challenge is to accurately model user intent with only limited evidence in these short sessions.For example, is a flower bouquet being viewed meant as part of a wedding purchase or for home decoration?Such different perspectives greatly impact what should be recommended next.Hence, this paper proposes a novel sessionbased recommendation system empowered by hypergraph attention networks.Three unique properties of the proposed approach are: (i) it constructs a hypergraph for each session to model the item correlations defined by various contextual windows in the session simultaneously, to uncover item meanings; (ii) it is equipped with hypergraph attention layers to generate item embeddings by flexibly aggregating the contextual information from correlated items in the session; and (iii) it aggregates the dynamic item representations for each session to infer the general purpose and current need, which is decoded to infer the next interesting item in the session.Through experiments on three benchmark datasets, we find the proposed model is effective in generating informative dynamic item embeddings and providing more accurate recommendations compared to the state-of-the-art.

A fundamental challenge for sequential recommenders is to capture the sequential patterns of users toward modeling how users transit among items. In many practical scenarios, however, there are a great number of cold-start users with only minimal logged interactions. As a result, existing sequential recommendation models will lose their predictive power due to the difficulties in learning sequential patterns over users with only limited interactions. In this work, we aim to improve sequential recommendation for cold-start users with a novel framework named MetaTL, which learns to model the transition patterns of users through meta-learning. Specifically, the proposed MetaTL: (i) formulates sequential recommendation for cold-start users as a few-shot learning problem; (ii) extracts the dynamic transition patterns among users with a translation-based architecture; and (iii) adopts meta transitional learning to enable fast learning for cold-start users with only limited interactions, leading to accurate inference of sequential interactions.

Network anomaly detection, also known as graph anomaly detection, aims to find network elements (e.g., nodes, edges, subgraphs) with significantly different behaviors from the vast majority. It has a profound impact in a variety of applications ranging from finance, healthcare to social network analysis. Due to the unbearable labeling cost, existing methods are predominately developed in an unsupervised manner. Nonetheless, the anomalies they identify may turn out to be data noises or uninteresting data instances due to the lack of prior knowledge on the anomalies of interest. Hence, it is critical to investigate and develop few-shot learning for network anomaly detection. In real-world scenarios, few labeled anomalies are also easy to be accessed on similar networks from the same domain as of the target network, while most of the existing works omit to leverage them and merely focus on a single network. Taking advantage of this potential, in this work, we tackle the problem of few-shot network anomaly detection by (1) proposing a new family of graph neural networks – Graph Deviation Networks (GDN) that can leverage a small number of labeled anomalies for enforcing statistically significant deviations between abnormal and normal nodes on a network; and (2) equipping the proposed GDN with a new cross-network meta-learning algorithm to realize few-shot network anomaly detection by transferring meta-knowledge from multiple auxiliary networks. Extensive evaluations demonstrate the efficacy of the proposed approach on few-shot or even one-shot network anomaly detection.

Attributed networks nowadays are ubiquitous in a myriad of high-impact applications, such as social network analysis, financial fraud detection, and drug discovery. As a central analytical task on attributed networks, node classification has received much attention in the research community. In real-world attributed networks, a large portion of node classes only contains limited labeled instances, rendering a long-tail node class distribution. Existing node classification algorithms are unequipped to handle the few-shot node classes. As a remedy, few-shot learning has attracted a surge of attention in the research community. Yet, few-shot node classification remains a challenging problem as we need to address the following questions: (i) How to extract meta-knowledge from an attributed network for few-shot node classification? (ii) How to identify the informativeness of each labeled instance for building a robust and effective model? To answer these questions, in this paper, we propose a graph meta-learning framework -- Graph Prototypical Networks (GPN). By constructing a pool of semi-supervised node classification tasks to mimic the real test environment, GPN is able to perform meta-learning on an attributed network and derive a highly generalizable model for handling the target classification task. Extensive experiments demonstrate the superior capability of GPN in few-shot node classification.

Due to the expensive cost of data annotation, few-shot learning has attracted increasing research interests in recent years. Various meta-learning approaches have been proposed to tackle this problem and have become the de facto practice. However, most of the existing approaches along this line mainly focus on image and text data in the Euclidean domain. However, in many real-world scenarios, a vast amount of data can be represented as attributed networks defined in the non-Euclidean domain, and the few-shot learning studies in such structured data have largely remained nascent. Although some recent studies have tried to combine meta-learning with graph neural networks to enable few-shot learning on attributed networks, they fail to account for the unique properties of attributed networks when creating diverse tasks in the meta-training phase---the feature distributions of different tasks could be quite different as instances (i.e., nodes) do not follow the data i.i.d. assumption on attributed networks. Hence, it may inevitably result in suboptimal performance in the meta-testing phase. To tackle the aforementioned problem, we propose a novel graph meta-learning framework--Attribute Matching Meta-learning Graph Neural Networks (AMM-GNN). Specifically, the proposed AMM-GNN leverages an attribute-level attention mechanism to capture the distinct information of each task and thus learns more effective transferable knowledge for meta-learning. We conduct extensive experiments on real-world datasets under a wide range of settings and the experimental results demonstrate the effectiveness of the proposed AMM-GNN framework.

There is an increasing attention on next-item recommendation systems to infer the dynamic user preferences with sequential user interactions. While the semantics of an item can change over time and across users, the item correlations defined by user interactions in the short term can be distilled to capture such change, and help in uncovering the dynamic user preferences. Thus, we are motivated to develop a novel next-item recommendation framework empowered by sequential hypergraphs. Specifically, the framework: (i) adopts hypergraph to represent the short-term item correlations and applies multiple convolutional layers to capture multi-order connections in the hypergraph; (ii) models the connections between different time periods with a residual gating layer; and (iii) is equipped with a fusion layer to incorporate both the dynamic item embedding and short-term user intent to the representation of each interaction before feeding it into the self-attention layer for dynamic user modeling. Through experiments on datasets from the ecommerce sites Amazon and Etsy and the information sharing platform Goodreads, the proposed model can significantly outperform the state-of-the-art in predicting the next interesting item for each user.

Recommendation systems typically rely on the interactions between a crowd of ordinary users and items, ignoring the fact that many real-world communities are notably influenced by a small group of key opinion leaders, whose feedback on items wields outsize influence. With important positions in the community (e.g. have a large number of followers), their elite opinions are able to diffuse to the community and further impact what items we buy, what media we consume, and how we interact with online platforms. Hence, this paper investigates how to develop a novel recommendation system by explicitly capturing the influence from key opinion leaders to the whole community. Centering around opinion elicitation and diffusion, we propose an end-to-end Graph-based neural model - GoRec. Specifically, to preserve the multi-relations between key opinion leaders and items, GoRec elicits the opinions from key opinion leaders with a translation-based embedding method. Moreover, GoRec adopts the idea of Graph Neural Networks to model the elite opinion diffusion process for improved recommendation. Through experiments on Goodreads and Epinions, the proposed model outperforms state-of-the-art approaches by 10.75% and 9.28% on average in Top-K item recommendation.

Attributed networks are ubiquitous and form a critical component of modern information infrastructure, where additional node attributes complement the raw network structure in knowledge discovery. Recently, detecting anomalous nodes on attributed networks has attracted an increasing amount of research attention, with broad applications in various high-impact domains, such as cybersecurity, finance, and healthcare. Most of the existing attempts, however, tackle the problem with shallow learning mechanisms by ego-network or community analysis, or through subspace selection. Undoubtedly, these models cannot fully address the computational challenges on attributed networks. For example, they often suffer from the network sparsity and data nonlinearity issues, and fail to capture the complex interactions between different information modalities, thus negatively impact the performance of anomaly detection. To tackle the aforementioned problems, in this paper, we study the anomaly detection problem on attributed networks by developing a novel deep model. In particular, our proposed deep model: (1) explicitly models the topological structure and nodal attributes seamlessly for node embedding learning with the prevalent graph convolutional network (GCN); and (2) is customized to address the anomaly detection problem by virtue of deep autoencoder that leverages the learned embeddings to reconstruct the original data. The synergy between GCN and autoencoder enables us to spot anomalies by measuring the reconstruction errors of nodes from both the structure and the attribute perspectives. Extensive experiments on real-world attributed network datasets demonstrate the efficacy of our proposed algorithm.

Performing anomaly detection on attributed networks concerns with finding nodes whose patterns or behaviors deviate significantly from the majority of reference nodes. Its success can be easily found in many real-world applications such as network intrusion detection, opinion spam detection and system fault diagnosis, to name a few. Despite their empirical success, a vast majority of existing efforts are overwhelmingly performed in an unsupervised scenario due to the expensive labeling costs of ground truth anomalies. In fact, in many scenarios, a small amount of prior human knowledge of the data is often effortless to obtain, and getting it involved in the learning process has shown to be effective in advancing many important learning tasks. Additionally, since new types of anomalies may constantly arise over time especially in an adversarial environment, the interests of human expert could also change accordingly regarding to the detected anomaly types. It brings further challenges to conventional anomaly detection algorithms as they are often applied in a batch setting and are incapable to interact with the environment. To tackle the above issues, in this paper, we investigate the problem of anomaly detection on attributed networks in an interactive setting by allowing the system to proactively communicate with the human expert in making a limited number of queries about ground truth anomalies. Our objective is to maximize the true anomalies presented to the human expert after a given budget is used up. Along with this line, we formulate the problem through the principled multi-armed bandit framework and develop a novel collaborative contextual bandit algorithm, named GraphUCB. In particular, our developed algorithm: (1) explicitly models the nodal attributes and node dependencies seamlessly in a joint framework; and (2) handles the exploration-exploitation dilemma when querying anomalies of different types. Extensive experiments on real-world datasets show the improvement of the proposed algorithm over the state-of-the-art algorithms.