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Pengbo Li, Hang Yu, Xiangfeng Luo

Graph-based fraud detection is crucial in identifying illegal activities in social networks, finance, and other sectors. Despite recent progress in this area, most of current researches typically require a large amount of annotated data to demonstrate its benefits. In practice, obtaining sufficient high-quality annotated data is challenging, limiting the effectiveness of model training. Therefore, leveraging extremely limited label information is crucial to enhance model performance. We propose a context-aware graph neural network (CGNN) to address this. CGNN performs category semantic decomposition on the contextual neighbor features of the center node to enrich the category semantics. In the neighbor message aggregation stage, the denoising attention mechanism enables the center node to adaptively aggregate heterophilic and homophilic information from neighbors. Particularly for unlabeled data, feature augmentation within the category subspace and consistency regularization driven by entropy minimization ensure that such data can further enhance model performance under explicit semantic guidance. We demonstrate on four real-world datasets that CGNN significantly outperforms other baseline methods with extremely limited labels.

Muzhi Li, Cehao Yang, Chengjin Xu, Zixing Song, Xuhui Jiang, Jian Guo, Ho-fung Leung, Irwin King

Inductive knowledge graph completion (KGC) aims to predict missing triples with unseen entities. Recent works focus on modeling reasoning paths between the head and tail entity as direct supporting evidence. However, these methods depend heavily on the existence and quality of reasoning paths, which limits their general applicability in different scenarios. In addition, we observe that latent type constraints and neighboring facts inherent in KGs are also vital in inferring missing triples. To effectively utilize all useful information in KGs, we introduce CATS, a novel context-aware inductive KGC solution. With sufficient guidance from proper prompts and supervised fine-tuning, CATS activates the strong semantic understanding and reasoning capabilities of large language models to assess the existence of query triples, which consist of two modules. First, the type-aware reasoning module evaluates whether the candidate entity matches the latent entity type as required by the query relation. Then, the subgraph reasoning module selects relevant reasoning paths and neighboring facts, and evaluates their correlation to the query triple. Experiment results on three widely used datasets demonstrate that CATS significantly outperforms state-of-the-art methods in 16 out of 18 transductive, inductive, and few-shot settings with an average absolute MRR improvement of 7.2%.

Muquan Li, Dongyang Zhang, Qiang Dong, Xiurui Xie, Ke Qin

Contemporary deep learning, characterized by the training of cumbersome neural networks on massive datasets, confronts substantial computational hurdles. To alleviate heavy data storage burdens on limited hardware resources, numerous dataset compression methods such as dataset distillation (DD) and coreset selection have emerged to obtain a compact but informative dataset through synthesis or selection for efficient training. However, DD involves an expensive optimization procedure and exhibits limited generalization across unseen architectures, while coreset selection is limited by its low data keep ratio and reliance on heuristics, hindering its practicality and feasibility. To address these limitations, we introduce a newly versatile framework for dataset compression, namely Adaptive Dataset Quantization (ADQ). Specifically, we first identify the sub-optimal performance of naive Dataset Quantization (DQ), which relies on uniform sampling and overlooks the varying importance of each generated bin. Subsequently, we propose a novel adaptive sampling strategy through the evaluation of generated bins' representativeness score, diversity score and importance score, where the former two scores are quantified by the texture level and contrastive learning-based techniques, respectively. Extensive experiments demonstrate that our method not only exhibits superior generalization capability across different architectures, but also attains state-of-the-art results.

Lu Li, Jiale Liu, Xingyu Ji, Maojun Wang, Zeyu Zhang

Signed Graph Neural Networks (SGNNs) have been shown to be effective in analyzing complex patterns in real-world situations where positive and negative links coexist. However, SGNN models suffer from poor explainability, which limit their adoptions in critical scenarios that require understanding the rationale behind predictions. To the best of our knowledge, there is currently no research work on the explainability of the SGNN models. Our goal is to address the explainability of decision-making for the downstream task of link sign prediction specific to signed graph neural networks. Since post-hoc explanations are not derived directly from the models, they may be biased and misrepresent the true explanations. Therefore, in this paper we introduce a Self-Explainable Signed Graph transformer (SE-SGformer) framework, which can not only outputs explainable information while ensuring high prediction accuracy. Specifically, we propose a new Transformer architecture for signed graphs and theoretically demonstrate that using positional encoding based on signed random walks has greater expressive power than current SGNN methods and other positional encoding graph Transformer-based approaches. We construct a novel explainable decision process by discovering the K-nearest (farthest) positive (negative) neighbors of a node to replace the neural network-based decoder for predicting edge signs. These K positive (negative) neighbors represent crucial information about the formation of positive (negative) edges between nodes and thus can serve as important explanatory information in the decision-making process. We conducted experiments on several real-world datasets to validate the effectiveness of SE-SGformer, which outperforms the state-of-the-art methods by improving 2.2% prediction accuracy and 73.1% explainablity accuracy in the best-case scenario.

Junliang Li, Yajun Yang, Yujia Zhang, Qinghua Hu, Alan Zhao, Hong Gao

Information popularity prediction, aiming to predict the growth of user participation in a trending topic diffusion, is a fundamental task in social networks. Existing methods often treat information diffusion as a single independent process, ignoring the ``public opinion field effect'' where multiple trending topics coexist and compete for user attention simultaneously. Inspired by Hawkes theory, we propose a novel Hawkes-process-based learning model for information popularity prediction, which takes into account both the temporal correlation among users' propagation behaviors in several topics diffusion and public opinion field effect in social networks. We first propose an improved neural Hawkes process to capture comprehensive propagation law from multiple dimensions and then propose a novel public opinion field paradigm based on the improved Hawkes process and cascade structure. We design a novel learning framework incorporating the public opinion field paradigm to extract high-quality representations for information popularity prediction. Extensive experiments on four real-world datasets validate that our model significantly outperforms the state-of-the-art competitors.

Jiuqiang Li

Bundle recommendation aims to improve user experience by suggesting complementary items that users are likely to purchase together. Although recent advances in recommendation systems have shown promise, there are still significant challenges: i) The dynamic nature of user preferences and interactions introduces noise that can distort the effectiveness of recommendations. ii) Existing methods frequently exhibit limited robustness when addressing the sparsity of user interactions with bundles in real-world scenarios. To tackle these issues, we introduce a disentangled contrastive bundle recommendation (DCBR) framework with conditional diffusion. First, we propose a conditional bundle diffusion model for denoising the user-bundle interaction graph, introducing a bundle latent consistency constraint during the optimization process to mitigate the degradation of original interaction information. Subsequently, we design a triple-view denoised graph learning module to obtain effective representations from multiple views. Furthermore, we present a dual-level disentangled contrastive learning paradigm, which addresses the latent relationships at two levels: between views (inter-view) and within each view (intra-view). By maximizing the consistency between positive samples in these contrastive views, we generate disentangled contrastive signals, overcoming interaction sparsity and alleviating noise issues. Our experimental evaluations on three benchmark datasets reveal that DCBR significantly outperforms state-of-the-art methods.

Jingyu Li, Zhiyong Feng, Dongxiao He, Hongqi Chen, Qinghang Gao, Guoli Wu

Interactive Recommendation (IR) has gained significant attention recently for its capability to quickly capture dynamic interest and optimize both short and long term objectives. IR agents are typically implemented through Deep Reinforcement Learning (DRL), because DRL is inherently compatible with the dynamic nature of IR. However, DRL is currently not perfect for IR. Due to the large action space and sample inefficiency problem, training DRL recommender agents is challenging. The key point is that useful features cannot be extracted as high-quality representations for the recommender agent to optimize its policy. To tackle this problem, we propose Contrastive Representation for Interactive Recommendation (CRIR). CRIR efficiently extracts latent, high-level preference ranking features from explicit interaction, and leverages the features to enhance users’ representation. Specifically, the CRIR provides representation through one representation network, and refines it through our proposed Preference Ranking Contrastive Learning (PRCL). The key insight of PRCL is that it can perform contrastive learning without relying on computations involving high-level representations or large potential action sets. Furthermore, we also propose a data exploiting mechanism and an agent training mechanism to better adapt CRIR to the DRL backbone. Extensive experiments have been carried out to show our method's superior improvement on the sample efficiency while training an DRL-based IR agent.

Hourun Li, Yifan Wang, Zhiping Xiao, Jia Yang, Changling Zhou, Ming Zhang, Wei Ju

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.

Guanghan Li, Xun Zhang, Yufei Zhang, Yifan Yin, Guojun Yin, Wei Lin

Large language models (LLMs), endowed with exceptional reasoning capabilities, are adept at discerning profound user interests from historical behaviors, thereby presenting a promising avenue for the advancement of recommendation systems. However, a notable discrepancy persists between the sparse collaborative semantics typically found in recommendation systems and the dense token representations within LLMs. In our study, we propose a novel framework that harmoniously merges traditional recommendation models with the prowess of LLMs. We initiate this integration by transforming ItemIDs into sequences that align semantically with the LLMs' space, through the proposed Alignment Tokenization module. Additionally, we design a series of specialized supervised learning tasks aimed at aligning collaborative signals with the subtleties of natural language semantics. To ensure practical applicability, we optimize online inference by pre-caching the top-K results for each user, reducing latency and improving efficiency. Extensive experimental evidence indicates that our model markedly improves recall metrics and displays remarkable scalability of recommendation systems.

Zhenyu Lei, Yushun Dong, Jundong Li, Chen Chen

Spatial-temporal graphs are widely used in a variety of real-world applications. Spatial-Temporal Graph Neural Networks (STGNNs) have emerged as a powerful tool to extract meaningful insights from this data. However, in real-world applications, most nodes may not possess any available temporal data during training. For example, the pandemic dynamics of most cities on a geographical graph may not be available due to the asynchronous nature of outbreaks. Such a phenomenon disagrees with the training requirements of most existing spatial-temporal forecasting methods, which jeopardizes their effectiveness and thus blocks broader deployment. In this paper, we propose to formulate a novel problem of inductive forecasting with limited training data. In particular, given a spatial-temporal graph, we aim to learn a spatial-temporal forecasting model that can be easily generalized onto those nodes without any available temporal training data. To handle this problem, we propose a principled framework named ST-FiT. ST-FiT consists of two key learning components: temporal data augmentation and spatial graph topology learning. With such a design, ST-FiT can be used on top of any existing STGNNs to achieve superior performance on the nodes without training data. Extensive experiments verify the effectiveness of ST-FiT in multiple key perspectives.

Songyuan Lei, Xinglong Chang, Zhizhi Yu, Dongxiao He, Cuiying Huo, Jianrong Wang, Di Jin

The cold-start recommendation has been challenging due to the limited historical interactions for new users and new items. Recently, methods based on meta-learning and graph neural networks have been effective in this problem. However, these methods mainly focus on the missing user-item interactions in cold-start scenarios, overlooking the missing of user/item feature information, which significantly limits the quality and effectiveness of node embeddings. To address this issue, we propose a new method called Feature-Structure Adaptive Completion Graph Neural Network (FS-GNN), which is designed to tackle the cold-start problem by simultaneously addressing the missing feature and structure information in a bipartite graph composed of users and items. Specifically, we first design a trainable feature completion module that leverages the knowledge emergence abilities of large language models to enhance node embedding and mitigate the impact of missing features. Then, we incorporate a three-channel structure completion module to simultaneously complete the structures among users-users, items-items, as well as users-items. Finally, we adaptively integrate the feature and structure completion modules in an end-to-end fashion, so as to minimize cross-module interference when completing features and structures simultaneously. This generates more comprehensive and robust embeddings for users and items in recommendation tasks. Experimental results on multiple public benchmark datasets demonstrate significant improvements in our proposed FS-GNN in cold-start scenarios, outperforming or being competitive with state-of-the-art methods.

Yeon-Chang Lee, Hojung Shin, Sang-Wook Kim

Graph Neural Networks (GNNs) have become essential tools for graph representation learning in various domains, such as social media and healthcare. However, they often suffer from fairness issues due to inherent biases in node attributes and graph structure, leading to unfair predictions. To address these challenges, we propose a novel GNN framework, DAB-GNN, that Disentangles, Amplifies, and deBiases attribute, structure, and potential biases in the GNN mechanism. DAB-GNN employs a disentanglement and amplification module that isolates and amplifies each type of bias through specialized disentanglers, followed by a debiasing module that minimizes the distance between subgroup distributions to ensure fairness. Extensive experiments on five datasets demonstrate that DAB-GNN significantly outperforms ten state-of-the-art competitors in terms of achieving an optimal balance between accuracy and fairness.

Sanghyuck Lee, Sangkeun Park, Jaesung Lee

The growing trend of sharing short videos on social media platforms, where users capture and share moments from their daily lives, has led to an increase in research efforts focused on micro-video recommendations. However, conventional methods oversimplify the modeling of skip behavior, categorizing interactions solely as positive or negative based on whether skipping occurs. This study was motivated by the importance of the first few seconds of micro-videos, leading to a refinement of signals into three distinct categories: highly positive, less positive, and negative. Specifically, we classify skip interactions occurring within a short time as negatives, while those occurring after a delay are categorized as less positive. The proposed dual-level graph and hierarchical ranking loss are designed to effectively learn these fine-grained interactions. Our experiments demonstrated that the proposed method outperformed three conventional methods across eight evaluation measures on two public datasets.

Hyunji Lee, Luca Soldaini, Arman Cohan, Minjoon Seo, Kyle Lo

Information retrieval methods often rely on a single embedding model trained on large, general-domain datasets like MSMARCO. While this approach can produce a retriever with reasonable overall performance, they often underperform models trained on domain-specific data when testing on their respective domains. Prior work in information retrieval has tackled this through multi-task training, but the idea of routing over a mixture of domain-specific expert retrievers remains unexplored despite the popularity of such ideas in language model generation research. In this work, we introduce RouterRetriever, a retrieval model that leverages a mixture of domain-specific experts by using a routing mechanism to select the most appropriate expert for each query. RouterRetriever is lightweight and allows easy addition or removal of experts without additional training. Evaluation on the BEIR benchmark demonstrates that RouterRetriever outperforms both models trained on MSMARCO (+2.1 absolute nDCG@10) and multi-task models (+3.2). This is achieved by employing our routing mechanism, which surpasses other routing techniques (+1.8 on average) commonly used in language modeling. Furthermore, the benefit generalizes well to other datasets, even in the absence of a specific expert on the dataset. RouterRetriever is the first work to demonstrate the advantages of routing over a mixture of domain-specific expert embedding models as an alternative to a single, general-purpose embedding model, especially when retrieving from diverse, specialized domains.

Iiro Kumpulainen, Sebastian Dalleiger, Jilles Vreeken, Nikolaj Tatti

Stochastic Block Models (SBMs) are a popular approach to modeling single real-world graphs. The key idea of SBMs is to partition the vertices of the graph into blocks with similar edge densities within, as well as between different blocks. However, what if we are given not one but multiple graphs that are unaligned and of different sizes? How can we find out if these graphs share blocks with similar connectivity structures? In this paper, we propose the shared stochastic block modeling (SSBM) problem, in which we model n graphs using SBMs that share parameters of s blocks. We show that fitting an SSBM is NP-hard, and consider two approaches to fit good models in practice. In the first, we directly maximize the likelihood of the shared model using a Markov chain Monte Carlo algorithm. In the second, we first fit an SBM for each graph and then select which blocks to share. We propose an integer linear program to find the optimal shared blocks and to scale to large numbers of blocks, we propose a fast greedy algorithm. Through extensive empirical evaluation on synthetic and real-world data, we show that our methods work well in practice.

Mingjin Kuai, Jun Long, Zhan Yang

Unsupervised deep cross-modal hash retrieval aims to map multi-modal features into binary hash codes without labels, which is of interest due to its storage efficiency, query speed and convenient applications. However, existing approaches suffer from two main limitations: (1) Slightly insufficient consideration of text instance similarity, along with independent or redundant fusion to learn multi-modal similarity information. (2) They ignore the noisy adjacent correlations between multi-modal instances, leading to a lack of discriminative power in the generated hash codes. To address these challenges, we propose a new approach called Statistical Model-driven Similarity Hashing (SMSH). Specifically, we introduce Jaccard similarity when constructing the text similarity matrix. It reduces the similarity error between text instances while better considering the asymmetry of the elements in the text features. After that, we integrate the original similarity information between various modalities to construct a unified similarity matrix. The gaps between modalities are bridged while reducing the redundant information in them. In addition, we introduce a Statistical Model-driven Similarity Enhancement (SMSE) approach, which reduces the noise of similarity relations between multi-modal instances by using a Gaussian Mixture Model to keep instances with lower semantic similarity as far away from each other as possible. Experiments on three benchmark datasets demonstrate the excellent performance of the SMSH method.

Yaxuan Kong, Zepu Wang, Yuqi Nie, Tian Zhou, Stefan Zohren, Yuxuan Liang, Peng Sun, Qingsong Wen

Traditional recurrent neural network architectures, such as long short-term memory neural networks (LSTM), have historically held a prominent role in time series forecasting (TSF) tasks. While the recently introduced sLSTM for Natural Language Processing (NLP) introduces exponential gating and memory mixing that are beneficial for long term sequential learning, its potential short memory issue is a barrier to applying sLSTM directly in TSF. To address this, we propose a simple yet efficient algorithm named P-sLSTM, which is built upon sLSTM by incorporating patching and channel independence. These modifications substantially enhance sLSTM's performance in TSF, achieving state-of-the-art results. Furthermore, we provide theoretical justifications for our design, and conduct extensive comparative and analytical experiments to fully validate the efficiency and superior performance of our model.

Lingdu Kong, Xiaochun Yang, Tieying Li, Bin Wang, Xiangmin Zhou

Video Moment Retrieval (VMR) aims to identify a temporal segment in an untrimmed video that best matches a given textual query. Bias in VMR is a critical issue, where the model achieves favorable results even if disregarding the video input. Existing evaluation methods, such as Resplitting, have attempted to address bias by creating out-of-distribution (OOD) datasets. However, these methods provide an incomplete definition of bias and do not quantify bias. To this end, we provide a comprehensive definition of bias in VMR, encompassing both data bias and model bias. Besides, our evaluation metrics can analyze the magnitude of these biases better. To address both data and model biases comprehensively, we introduce Reverse Distribution based VMR (ReDis-VMR). This novel approach dynamically generates datasets with inverse distributions tailored to different models based on Gaussian kernel estimation. As a result, it enables a more accurate evaluation of model performance. Building on ReDis-VMR, we further propose the Dynamic Expandable Adjustment (DEA) pipeline. DEA incrementally expands the model structure to enhance its focus on video and text features, and it incorporates a fair loss to minimize the influence of concentrated data distributions. The experimental results on bias ratio demonstrate that our ReDis method achieves state-of-the-art performance in bias elimination, while the results on moment retrieval confirm the effectiveness of our DEA framework across three evaluation methods, two datasets, and three baselines.

Taeri Kim, Jiho Heo, Hyunjoon Kim, Sang-Wook Kim

We focus on the medication recommendation problem aiming to recommend accurate medications for a patient’s current visit. Most existing methods for this problem utilize the patient’s current health status, medications prescribed at her past visits, and an Electronic Health Records (EHR) graph which represents whether medications have been co-prescribed. However, we point out their two limitations: (1) they have difficulty in utilizing only the medications which have been prescribed in health status similar to the patient’s current health status, regardless of whether they are prescribed at her past visits or at other patients’ visits; (2) for two medications that have ever been co-prescribed, their EHR graph does not consider the degree to which one medication is prescribed together when the other is prescribed. To address these two limitations, we propose a novel medication recommendation framework, named HI-DR (pronounced as ‘Hi Doctor’), composed of following two core ideas: (Idea 1) Health status-aware attentIon; (Idea 2) an electronic health recorDs gRaph+. Extensive experiments on real-world datasets demonstrate the significant superiority of HI-DR (up to 18.69% higher accuracy than the best competitor) and the effectiveness of two core ideas in HI-DR.

Taeho Kim, Hyeongjun Jang, Juwon Yu, Taeuk Kim, Hyunyoung Lee, Ji-hui Im, Sang-Wook Kim

Conversational Recommender Systems (CRS) aim to provide tailored recommendation responses via a chat interface, including both the user's preferred item and its accompanying explanation. However, due to its generative nature, CRS are prone to responding with factually incorrect explanations (i.e., hallucinations). To solve this problem, we propose incorporating a passage retrieval module into CRS with the objective of enhancing the factuality and informativeness of system responses. Specifically, we outline essential directions for employing a passage retrieval module in CRS to address the following critical issues: (1) the risk of passage retrieval not aligning with the user preference; (2) the absence of supervision for training a passage retrieval module. As a solution, we introduce ESPRESSO, a novel passage retrieval approach for CRS, to effectively tackle the above issues with two core ideas: adaptive item selection and relevance-based groupwise learning. Our extensive experiments show that ESPRESSO effectively resolves issues, achieving up to 36% higher Hit@3 accuracy than the best of 8 competing methods. Additionally, we verify that leveraging passages retrieved by ESPRESSO significantly improves the response quality of CRS.