The widespread adoption of graph neural networks (GNNs) has brought increased attention to fairness issues related to sensitive attributes, such as gender and race, in practical scenarios. However, this concern remains largely unexplored in the context of graph clustering. Conventional fair graph clustering methods primarily depend on spectral clustering approaches. Meanwhile, we argue that existing graph learning works mainly focus on a single type of fairness, whereas graph clustering should achieve group equality-informed individual fairness. In this paper, we introduce for the first time a fairness-aware framework termed FairGC for deep graph clustering, which integrates the dual objectives of individual and group fairness while maintaining accurate clustering results. Specifically, we construct two views with distinct semantics using Siamese encoders. Then, we apply multi-step random walks on view-specific affinity graphs to capture high-order affinities of node pairs, thereby reformulating the contrastive learning with a focus on individual similarity. Besides, we utilize adversarial learning by making node representations independent of the estimated sensitive attributes to further eliminate group biases of clustering results. Extensive experiments on four benchmarks demonstrate the effectiveness and superiority of our proposed framework FairGC.

Spatial transcriptomics (ST) enables joint profiling of gene expression and spatial positions, thereby revealing spatially resolved biological functions. However, many existing ST analysis methods often fail to explicitly quantify the belief and uncertainty in decisions caused by noisy ST data, making it difficult to handle spots of varying quality in a fine-grained manner. In addition, domain identification is a fundamental and critical task in ST, but commonly used models that separate expression learning and clustering often struggle to learn cluster-friendly latent representations effectively. To address these issues, we propose PREST, a prototype-based evidence-aware integration framework for ST data. PREST performs multi-scale representation learning with fine-grained attention fusion and introduces learnable class prototypes to quantify belief and uncertainty in model decisions. We aim to align overall belief scores with latent semantic information to enhance uncertainty quantification and prototype learning, thereby promoting the learning of clustering-friendly representations. PREST further integrates an uncertainty-aware reconstruction module and spatial regularization to reduce overfitting to unreliable spots and promote denoised, discriminative representations. Extensive experiments on several benchmark datasets validate the effectiveness and superiority of our proposed PREST across various downstream tasks.

Shanwen Tan, Ziyang Dong, Wei Ju, Yiwei Fu, Hao Wu, Kun Wang, Yifan Wang, Ziyue Qiao. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Deep neural networks (DNNs) have achieved extensive progress in time series learning. However, they could suffer from performance degradation when it comes to label noise in the real world. Towards this end, this paper studies an underexplored yet realistic problem of noise-resistant time series learning and proposes a novel data-centric approach named Dual Data-centric Separation with Circular Mixup (DREAM) for this problem. The core of our DREAM is to explore and exploit the noisy data from dual data-centric views for reduced overfitting. On the one hand, we assume that samples with similar features share similar labels and infer the pseudo label of each sample using its affinity graph to capture the corresponding pseudo margin. On the other hand, we monitor the optimization status by simulating the mislabeled data to generate flexible criteria for accurate separation of clean and noisy samples. In addition, we leverage circular Mixup to interpolate between clean and noisy samples in the embedding space. These mixed samples are incorporated into a discrepancy-aware consistency learning framework to ensure robust time series representations of all the separated samples. Experimental results on a wide range of publicly accessible datasets reveal the effectiveness of our DREAM.

This paper investigates the task of unsupervised graph domain adaptation, which facilitates the transfer of knowledge from labeled source graphs to unlabeled target graphs. Recent approaches usually utilize graph contrastive learning and pseudo-labeling to learn from unlabeled target data, which could introduce potential biased representations and supervision of target graphs resulting from serious shifts across two domains. Towards this end, we propose a novel framework named Diffusion-guided Unbiased Discriminative Learning (DisCo) for unsupervised graph domain adaptation. The core of our DisCo is to leverage both feature disentanglement and cross-domain diffusion signals to remove the potential biases for target graphs. In particular, we first utilize adversarial feature disentanglement to extract causal features that are orthogonal to domain biases. More importantly, we retrieve the labels of cross-domain source graphs to generate the conditions, which would be utilized to optimize a diffusion model for label denoising. The consistency between pseudo-labels and denoised labels is measured to reduce the potential biases during domain alignment. Extensive experiments on several real-world benchmarks demonstrate that our proposed DisCo consistently outperforms competing state-of-the-art baselines.

Graph-level classification involves analyzing the property of the whole graph, which is typically solved by using graph neural networks (GNNs). Existing efforts generally assume a balanced class distribution. However, real-world data often exhibit long-tailed distributions, i.e., tail classes have significantly fewer samples than head classes, and thus directly applying GNNs is eventually biased toward the head classes, resulting in limited generalization over the tail classes. Moreover, the predictions of existing algorithms are usually not trustworthy, and the trained classifiers remain ignorant to their predictive confidence. Towards this end, in this paper we develop a principled framework called GraphEVER for long-tailed graph-level classification. Technically, GraphEVER incorporates the beliefs of multiple experts and leverages the idea of subjective logic within the Dempster-Shafer Evidence Theory (DST). It can provide the evidence and uncertainty estimation for each expert, where the evidence is parameterized by a Dirichlet distribution to model class probability distribution, and the uncertainty is quantified via a well-defined theoretical framework. In this way, diverse experts can be integrated under DST to endow the classifier with both reliability and robustness. Moreover, we propose an evidence-based routing mechanism to dynamically assign experts, such that the tail classes can receive more attention, while the head classes can reduce redundant engaged experts, further cutting down the computational cost and improving the efficiency. Extensive experiments on seven datasets verify the superiority of our proposed framework.

Out-of-Distribution (OOD) detection, which seeks to identify samples deviating from the In-Distribution (ID) training distribution at test time, is crucial for building robust machine learning systems. While extensive efforts have been made for Euclidean data, OOD detection on graph-structured data remains relatively underexplored. On the one hand, the specific properties of a graph may be attributed to its substructures. On the other hand, acquiring labeled data for graph learning is typically time-consuming and labor-intensive. Toward this end, in this paper, we propose a novel kernel-enhanced graph substructure learning framework termed KEGOD for unsupervised graph OOD detection. Specifically, we introduce a learnable graph generator to construct the augmented graph view that preserves distinguishable structure information. Then, for both the input graph and augmented view, a graph neural network (GNN) branch and a graph kernel (GK) branch are incorporated to explore graph latent patterns. By performing multi-branch concordance learning on the extracted graph patterns, our KEGOD captures complementary ID structural semantics in both implicit and explicit manners, enabling reliable detection of OOD graphs through semantic inconsistency. Finally, we build a self-adaptive training mechanism to automatically control diverse sensitivities of the graph patterns. Experimental results on several public graph datasets reveal the superiority of our KEGOD. Our code is available at~ https://github.com/jamesyifan/KEGOD.

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

Out-of-distribution (OOD) detection has garnered increasing concern for identifying test samples that exhibit a distributional shift from the training dataset in practical deep learning applications. With the significant advancements in graph deep learning for graph representation, graph OOD detection has emerged as a research problem. Graph contrastive learning (GCL) is applied to graph OOD detection due to its capacity for learning discriminative representations in a self-supervised manner, thereby eliminating the need for time-consuming and labor-intensive label information. However, existing methods often neglect the explicit consideration of underlying semantics behind graph data distribution for OOD detection. We argue that simple data augmentations for GCL may risk disrupting the intrinsic graph structure while retaining redundant structural information, which hinders semantic discrimination between graphs. Additionally, Euclidean space embedding struggles to maintain hierarchical structural consistency, making it challenging to meaningfully capture the hierarchical semantic distribution of graph data. In response to these issues, we propose a novel framework termed HGOOD-D, which aims to explore latent semantic hierarchies in hyperbolic space for graph OOD detection. Specifically, we design a bottleneck graph extractor grounded in the information bottleneck (IB) principle, which captures the minimal sufficient information to distinguish graph patterns. Based on this, we introduce hierarchical contrastive learning to capture the hierarchical semantics within graph data distribution. These methods are based on hyperbolic space embedding that can preserve complex inter-relationships in graph hierarchies, thereby mitigating data distortion. Comprehensive evaluations on ten widely used benchmark datasets show that HGOOD-D consistently surpasses current state-of-the-art approaches in graph OOD detection.

Graph neural networks (GNNs) have emerged as powerful tools for graph classification tasks. However, contemporary graph classification methods are predominantly studied in fully supervised scenarios, while there could be label ambiguity and noise in real-world applications. In this work, we explore the weakly supervised problem of partial label learning on graphs, where each graph sample is assigned a collection of candidate labels. A novel method calledDistribution Divergence-based Graph Contrast (DEER) is proposed to address this issue. At the heart of our DEER is to measure the divergence among the underlying semantic distributions in the hidden space and this metric enables the identification of accurate positive graph pairs for effective graph contrastive learning. Specifically, we generate graph representations of augmented graph views that retain semantics and can be regarded as samples from the underlying semantic distributions. We employ a non-parametric metric to measure distribution divergence, which is then combined with pseudo-labeling to generate unbiased and target-oriented graph pairs. Furthermore, we introduce a label-correction method to eliminate noisy candidate labels, updating target labels using posterior distributions in a soft manner. Comprehensive experiments on various benchmarks demonstrate the superiority of our DEER in different settings compared to a range of state-of-the-art baselines.

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

Junyu Luo, Bohan Wu, Xiao Luo, Zhiping Xiao, Yiqiao Jin, Rong-Cheng Tu, Nan Yin, Yifan Wang, Jingyang Yuan, Wei Ju, Ming Zhang. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

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

Graph domain adaptation is a key subfield of graph transfer learning that aims to bridge domain gaps by transferring knowledge from a label-rich source graph to an unlabeled target graph. However, most existing methods assume balanced labels in the source graph, which often fails in practice and leads to biased knowledge transfer. To address this, in this paper, we propose a prototype-anchored learning and alignment framework for class-imbalanced graph domain adaptation. Specifically, we incorporate pointwise node mutual information into the graph encoder to capture high-order topological proximity and learn generalized node representations. Leveraging this, we then introduce categorical prototypes with adversarial proto-instances for prototype-anchored learning and recalibration to represent the source graph under an imbalanced class distribution. Finally, we introduce a weighted prototype contrastive adaptation strategy that aligns target pseudo-labels with source prototypes to handle class imbalance during adaptation. Extensive experiments show that our PALA outperforms the state-of-the-art methods. Our code is available at https://github.com/maxin88scu/PALA.

Deep graph clustering, which aims to uncover the underlying structure within graphs and partition nodes into distinct groups, is a challenging research spot. However, the formation of the cluster in real-world graphs typically governed by the highly complex interaction of many underlying latent factors. Existing methods typically rely on the features and structure associated with the graph, and neglect the entanglement of these factors, resulting in sub-optimal clustering performance. In this paper, we propose a novel deep graph clustering framework named DisenCluster, which learns disentangled representations to simultaneously consider node separation results from diverse perspectives. Specifically, we introduce a disentangled graph encoder that iteratively identifies the latent factors of the input graph by modeling the distribution over different factors for each edge. Subsequently, we utilize a factor-wise contrastive loss to encourage clustering-friendly disentangled representations, allowing us to derive different clustering results based on the corresponding factor. These results are then structured as anchor graphs and seamlessly integrated into a unified graph. Finally, we formulate the framework as a continuous relaxation of the high-order graph cut problem and optimize the objective to obtain effective cluster assignments. Results from experiments on a variety of publicly available datasets further reveal the effectiveness and superiority of our DisenCluster compared with baselines.

Graph classification is a fundamental machine learning problem with extensive applications in multimedia and biochemical analysis. Contemporary graph classification models usually require precise graph labels for supervision, even after self-supervised pre-training. However, in practical applications, the extensive precise annotation of graphs could be expensive or impractical. To exploit data efficiently, this work studies partial label graph learning, in which each graph is linked to a set of candidate labels but only one of them is accurate. Label ambiguity would bring difficulties in extracting graph semantics and the risk of overfitting noisy partial labels. Here, we present a novel approach called Coupled Dual Separation (CODE). To improve graph semantics mining under label ambiguity, our CODE contains a message passing branch and a graph kernel branch, which explore graph semantics implicitly and explicitly, respectively. To facilitate information exchange, we utilize one branch to separate partially labeled graphs into an informative set and an uninformative set, which provides guidance for the optimization of the other branch. Furthermore, to mitigate the risk of overfitting, parameters in coupled branches are partitioned into critical and non-critical ones for separated optimization procedures. Extensive experiments on several benchmark datasets validate the effectiveness of the proposed CODE.

This paper studies the problem of graph out-of-distribution generalization, which aims to enhance the performance of graph neural networks (GNNs) under distribution shifts. Existing approaches usually learn graph representations from a casual graph, which may not explicitly utilize environment information explicitly. Furthermore, they could suffer from performance degradation when confusing semantics related to target labels and environments. In this paper, we propose a novel approach named Dual Prompt Learning with Information Bottleneck (DATE) for graph out-of-distribution generalization. The core of our DATE is to utilize dual prompts to extract task-oriented semantics and model distribution shifts, respectively. In particular, we first pre-train a GNN using contrastive learning with pretext tokens introduced. More importantly, we not only introduce a task-oriented prompt based on LLMs to generate environment-invariant representations, but also learn the environment-oriented prompts to simulate subgraphs in different environments. To optimize our prompts, we introduce a graph information bottleneck framework, which minimizes the mutual information between environment-invariant representations and environment semantics with the most semantics preserved. Extensive experiments on various benchmark datasets validate the effectiveness of our DATE against various state-of-the-art approaches.

Graph transfer learning, especially in unsupervised domain adaptation, aims to transfer knowledge from a label-abundant source graph to an unlabeled target graph. However, most existing approaches overlook the common issue of label imbalance in the source domain, typically assuming a balanced label distribution that rarely holds in practice. Moreover, they face challenges arising from biased knowledge in the source graph and substantial domain distribution shifts. To remedy the above challenges, we propose a dual-branch prototype-enhanced contrastive framework for class-imbalanced graph domain adaptation in this paper. Specifically, we introduce a dual-branch graph encoder to capture both local and global information, generating class-specific prototypes from a distilled anchor set. Then, a prototype-enhanced contrastive learning framework is introduced. On the one hand, we encourage class alignment between the two branches based on constructed prototypes to alleviate the bias introduced by class imbalance. On the other hand, we infer the pseudo-labels for the target domain and align sample pairs across domains that share similar semantics to reduce domain discrepancies. Experimental results show that our ImGDA outperforms the state-of-the-art methods across multiple datasets and settings. The code is available at: https://github.com/maxin88scu/ImGDA.

Graph neural networks (GNNs) have shown remarkable success in graph-level classification tasks. However, most of the existing GNN-based studies are based on balanced datasets, while many real-world datasets exhibit long-tailed distributions. In such datasets, the tail classes receive limited attention during training, leading to prediction bias and degraded performance. To address this issue, a range of long-tailed learning strategies have been proposed, such as data re-balancing and transfer learning. However, these approaches encounter several challenges, including insufficient representation capacity for tail classes and their evaluation solely on uniform test data, limiting their capacity to handle unknown class distributions. To tackle these challenges, we introduce a novel framework, namely Knowledge-diverse Experts (KDEX) for long-tailed graph classification. Our KDEX leverages a dynamic memory module to enable the transfer of knowledge from head to tail, which improves the representation ability of the tail. To deal with unknown test distributions, KDEX introduces a knowledge-diverse expert training approach to train experts with different capacities in managing various test distributions. Moreover, we train the hierarchical router in a self-supervised manner to dynamically aggregate each knowledge-diverse expert during testing. Experimental results on multiple benchmarks reveal that our KDEX outperforms current baselines in both standard and test-agnostic long-tailed graph classification.

This paper studies the problem of semi-supervised learning on graphs, which has recently aroused widespread interest in relational data mininThe focal point of exploration in this area has been the utilization of graph neural networks (GNNs), which stand out for excellent performance. Previous methods, however, typically rely on the limited labeled data while ignoring the abundant structural information in unlabeled nodes inherently on graphs, easily resulting in overfitting, especially in scenarios where only a few label nodes are available. Even worse, GNNs, despite their success, are constrained by their ability to solely capture local neighborhood information through message-passing mechanisms, thereby falling short in modeling higher-order dependencies among nodes. To circumvent the above drawbacks, we propose a simple yet effective framework calledHypergraph COnsistencyLeArning (HOLA). Specifically, we employ a collaborative distillation framework consisting of a teacher network and a student network. To achieve effective interaction, we propose momentum distillation, a self-training method that enables the student network to learn from pseudo-targets generated by a momentum teacher network. Further, a novel hypergraph structure learning network is developed to model complex high-order relations among nodes with relational consistency learning, thereby transferring the knowledge to the student network. Extensive experiments conducted on a variety of benchmark datasets demonstrate the superior performance of the HOLA over various state-of-the-art methods.

Graph classification is a critical task in numerous multimedia applications, where graphs are employed to represent diverse types of multimedia data, including images, videos, and social networks. Nevertheless, in the real world, labeled graph data are always limited or scarce. To address this issue, we focus on the semi-supervised graph classification task, which involves both supervised and unsupervised models learning from labeled and unlabeled data. In contrast to recent approaches that transfer the entire knowledge from the unsupervised model to the supervised one, we argue that an effective transfer should only retain the relevant semantics that align well with the supervised task. We introduce a novel framework termed DisenSemi in this article, which learns disentangled representation for semi-supervised graph classification. Specifically, a disentangled graph encoder is proposed to generate factorwise graph representations for both supervised and unsupervised models. Then, we train two models via supervised objective and mutual information (MI)-based constraints, respectively. To ensure the meaningful transfer of knowledge from the unsupervised encoder to the supervised one, we further define an MI-based disentangled consistency regularization between two models and identify the corresponding rationale that aligns well with the current graph classification task. Experiments conducted on various publicly available datasets demonstrate the effectiveness of our DisenSemi.

Molecular property prediction plays a crucial role in drug discovery, but is always challenged by the limited number of effective labels. Compared with existing methods, we argue that the auxiliary properties of the molecule and the heterogeneous structure of different property prediction tasks have always been ignored. In this article, we propose a novel framework termed Meta-DREAM for few-shot molecular property prediction, which tailors to learning the transferable knowledge within different clusters of tasks. Specifically, we first construct a heterogeneous molecule relation graph (HMRG) with molecule-property and molecule-molecule relations to utilize many-to-many correlations between properties and molecules. The meta-learning episode can, then, be reformulated as a subgraph of HMRG. Next, we propose a disentangled graph encoder to explicitly discriminate the underlying factors of the task. In addition, we introduce a soft clustering module to group each factorized task representation into appropriate clusters and preserve knowledge generalization within a cluster and customization among clusters. In this way, each disentangled factor serves as a cluster-aware parameter gate for the task-specific meta-learner. Extensive experiments on five commonly used molecular datasets show that Meta-DREAM consistently outperforms existing state-of-the-art methods and verifies the effectiveness of each module.

In this paper, we study semi-supervised graph classification, which aims at accurately predicting the categories of graphs in scenarios with limited labeled graphs and abundant unlabeled graphs. Despite the promising capability of graph neural networks (GNNs), they typically require a large number of costly labeled graphs, while a wealth of unlabeled graphs fail to be effectively utilized. Moreover, GNNs are inherently limited to encoding local neighborhood information using message-passing mechanisms, thus lacking the ability to model higher-order dependencies among nodes. To tackle these challenges, we propose a Hypergraph-Enhanced DuAL framework named HEAL for semi-supervised graph classification, which captures graph semantics from the perspective of the hypergraph and the line graph, respectively. Specifically, to better explore the higher-order relationships among nodes, we design a hypergraph structure learning to adaptively learn complex node dependencies beyond pairwise relations. Meanwhile, based on the learned hypergraph, we introduce a line graph to capture the interaction between hyperedges, thereby better mining the underlying semantic structures. Finally, we develop a relational consistency learning to facilitate knowledge transfer between the two branches and provide better mutual guidance. Extensive experiments on real-world graph datasets verify the effectiveness of the proposed method against existing state-of-the-art methods.

Point-of-Interest (POI) recommendation plays a vital role in various location-aware services. It has been observed that POI recommendation is driven by both sequential and geographical influences. However, since there is no annotated label of the dominant influence during recommendation, existing methods tend to entangle these two influences, which may lead to sub-optimal recommendation performance and poor interpretability. In this paper, we address the above challenge by proposing DisenPOI, a novel Disentangled dual-graph framework for POI recommendation, which jointly utilizes sequential and geographical relationships on two separate graphs and disentangles the two influences with self-supervision. The key novelty of our model compared with existing approaches is to extract disentangled representations of both sequential and geographical influences with contrastive learning. To be specific, we construct a geographical graph and a sequential graph based on the check-in sequence of a user. We tailor their propagation schemes to become sequence-/geo-aware to better capture the corresponding influences. Preference proxies are extracted from check-in sequence as pseudo labels for the two influences, which supervise the disentanglement via a contrastive loss. Extensive experiments on three datasets demonstrate the superiority of the proposed model.

Citing and describing related literature are crucial to scientific writing. Many existing approaches show encouraging performance in citation recommendation, but are unable to accomplish the more challenging and onerous task of citation text generation. In this paper, we propose a novel disentangled representation based model DisenCite to automatically generate the citation text through integrating paper text and citation graph. A key novelty of our method compared with existing approaches is to generate context-specific citation text, empowering the generation of different types of citations for the same paper. In particular, we first build and make available a graph enhanced contextual citation dataset (GCite) with 25K edges in different types characterized by citation contained sections over 4.8K research papers. Based on this dataset, we encode each paper according to both textual contexts and structure information in the heterogeneous citation graph. The resulted paper representations are then disentangled by the mutual information regularization between this paper and its neighbors in graph. Extensive experiments demonstrate the superior performance of our method comparing to state-of-the-art approaches. We further conduct ablation and case studies to reassure that the improvement of our method comes from generating the context-specific citation through incorporating the citation graph.

Point-of-Interest (POI) recommendation, which benefits from the proliferation of GPS-enabled devices and location-based social networks (LBSNs), plays an increasingly important role in recommender systems. It aims to provide users with the convenience to discover their interested places to visit based on previous visits and current status. Most existing methods usually merely leverage recurrent neural networks (RNNs) to explore sequential influences for recommendation. Despite the effectiveness, these methods not only neglect topological geographical influences among POIs, but also fail to model high-order sequential substructures. To tackle the above issues, we propose a Kernel-Based Graph Neural Network (KBGNN) for next POI recommendation, which combines the characteristics of both geographical and sequential influences in a collaborative way. KBGNN consists of a geographical module and a sequential module. On the one hand, we construct a geographical graph and leverage a message passing neural network to capture the topological geographical influences. On the other hand, we explore high-order sequential substructures in the user-aware sequential graph using a graph kernel neural network to capture user preferences. Finally, a consistency learning framework is introduced to jointly incorporate geographical and sequential information extracted from two separate graphs. In this way, the two modules effectively exchange knowledge to mutually enhance each other. Extensive experiments conducted on two real-world LBSN datasets demonstrate the superior performance of our proposed method over the state-of-the-arts. Our codes are available at https://github.com/ ang6ang/KBGNN.

This paper studies semi-supervised graph classification, a crucial task with a wide range of applications in social network analysis and bioinformatics. Recent works typically adopt graph neural networks to learn graph-level representations for classification, failing to explicitly leverage features derived from graph topology (e.g., paths). Moreover, when labeled data is scarce, these methods are far from satisfactory due to their insufficient topology exploration of unlabeled data. We address the challenge by proposing a novel semi-supervised framework called Twin Graph Neural Network (TGNN). To explore graph structural information from complementary views, our TGNN has a message passing module and a graph kernel module. To fully utilize unlabeled data, for each module, we calculate the similarity of each unlabeled graph to other labeled graphs in the memory bank and our consistency loss encourages consistency between two similarity distributions in different embedding spaces. The two twin modules collaborate with each other by exchanging instance similarity knowledge to fully explore the structure information of both labeled and unlabeled data. We evaluate our TGNN on various public datasets and show that it achieves strong performance.

Large amounts of user behavior data provide opportunities for user behavior modeling and have great potential in many downstream applications such as advertising and anomaly detection. Compared with traditional methods, embedding-based methods are used more often recently because of their efficiency and scalability. These methods build a “behavior-entity” bipartite graph and learn static embeddings for nodes in the graph. However, behavior patterns in the real world could not be static because entity properties such as user interests usually evolve along with time. In this paper, we formulate user behaviors as a temporal event sequence and propose a stream network embedding approach to capture the evolving nature of user behaviors. Representation of each event is built and used to update the embeddings of nodes. Two contextual behavior modeling tasks are studied for dynamic user behaviors, and experimental results with real-world data demonstrate the effectiveness of our proposed approach over several competitive baselines.

Click-through rate (CTR) prediction plays a critical role in recommender systems and other applications. Recently, modeling user behavior sequences attracts much attention and brings great improvements in the CTR field. Many existing works utilize attention mechanism or recurrent neural networks to exploit user interest from the sequence, but fail to recognize the simple truth that a user's real-time interests are inherently diverse and fluid. In this paper, we propose DisenCTR, a novel dynamic graph-based disentangled representation framework for CTR prediction. The key novelty of our method compared with existing approaches is to model evolving diverse interests of users. Specifically, we construct a time-evolving user-item interaction graph induced by historical interactions. And based on the rich dynamics supplied by the graph, we propose a disentangled graph representation module to extract diverse user interests. We further exploit the fluidity of user interests and model the temporal effect of historical behaviors using Mixture of Hawkes Process. Extensive experiments on three real-world datasets demonstrate the superior performance of our method comparing to state-of-the-art approaches.

Heterogeneous information network has been widely used to alleviate sparsity and cold start problems in recommender systems since it can model rich context information in user-item interactions. Graph neural network is able to encode this rich context information through propagation on the graph. However, existing heterogeneous graph neural networks neglect entanglement of the latent factors stemming from different aspects. Moreover, meta paths in existing approaches are simplified as connecting paths or side information between node pairs, overlooking the rich semantic information in the paths. In this paper, we propose a novel disentangled heterogeneous graph attention network DisenHAN for top-N recommendation, which learns disentangled user/item representations from different aspects in a heterogeneous information network. In particular, we use meta relations to decompose high-order connectivity between node pairs and propose a disentangled embedding propagation layer which can iteratively identify the major aspect of meta relations. Our model aggregates corresponding aspect features from each meta relation for the target user/item. With different layers of embedding propagation, DisenHAN is able to explicitly capture the collaborative filtering effect semantically. Extensive experiments on three real-world datasets show that DisenHAN consistently outperforms state-of-the-art approaches. We further demonstrate the effectiveness and interpretability of the learned disentangled representations via insightful case studies and visualization.

In recent years, along with the rapid development of Massive Open Online Courses (MOOCs), a large number of students participate in MOOC courses. MOOC course forum is very important for students discussing questions related to online courses when they don’t have enough offline resources to get help from. There are a lot of qualified discussion threads in MOOC courses which are helpful for understanding the course materials. However, students are always overwhelmed by the enormous amount of threads. A solution to alleviate this problem is to recommend a list of threads for each student that she/he may be interested in. Existing thread recommender systems are insufficient to capture the complex relationships among the threads in a student’s visit session, and do not simultaneously take into account the student’s global preference, as well as her/his current interest in that session. In this work, we propose a novel neural network framework for session-based thread recommendation (STR-SA for short). The proposed method, which recommends threads to a student based on the threads viewed by the student in the current session, learns the relationships among threads by applying self-attention mechanism. Furthermore, we capture the global preference of the student by combining the viewed threads, and consider the latest-viewed thread as the current interest of the student. Extensive experiments conducted on three course-forum datasets show that the proposed model STR-SA significantly outperforms other representative methods for MOOC thread recommendation.

Online communities such as Facebook and Twitter are enormously popular and have become an essential part of the daily life of many of their users. Through these platforms, users can discover and create information that others will then consume. In that context, recommending relevant information to users becomes critical for viability. However, recommendation in online communities is a challenging problem: 1) users' interests are dynamic, and 2) users are influenced by their friends. Moreover, the influencers may be context-dependent. That is, different friends may be relied upon for different topics. Modeling both signals is therefore essential for recommendations. We propose a recommender system for online communities based on a dynamic-graph-attention neural network. We model dynamic user behaviors with a recurrent neural network, and context-dependent social influence with a graph-attention neural network, which dynamically infers the influencers based on users' current interests. The whole model can be efficiently fit on large-scale data. Experimental results on several real-world data sets demonstrate the effectiveness of our proposed approach over several competitive baselines including state-of-the-art models.