论文检索

Chenhao Zhang, Wei Gao

Dynamic point cloud compression (DPCC) is crucial in applications like autonomous driving and AR/VR. Current compression methods face challenges with complexity management and rate control. This paper introduces a novel dynamic coding framework that supports variable bitrate and computational complexities. Our approach includes a slimmable framework with multiple coding routes, allowing for efficient Rate-Distortion-Complexity Optimization (RDCO) within a single model. To address data sparsity in inter-frame prediction, we propose the coarse-to-fine motion estimation and compensation module that deconstructs geometric information while expanding the perceptive field. Additionally, we propose a precise rate control module that content-adaptively navigates point cloud frames through various coding routes to meet target bitrates. The experimental results demonstrate that our approach reduces the average BD-Rate by 5.81% and improves the BD-PSNR by 0.42 dB compared to the state-of-the-art method, while keeping the average bitrate error at 0.40%. Moreover, the average coding time is reduced by up to 44.6% compared to D-DPCC, underscoring its efficiency in real-time and bitrate-constrained DPCC scenarios.

Bowen Zhang, Zhichao Huang, Guangning Xu, Xiaomao Fan, Mingyan Xiao, Genan Dai, Hu Huang

Graph classification is a pivotal challenge in machine learning, especially within the realm of graph-based data, given its importance in numerous real-world applications such as social network analysis, recommendation systems, and bioinformatics. Despite its significance, graph classification faces several hurdles, including adapting to diverse prediction tasks, training across multiple target domains, and handling small-sample prediction scenarios. Current methods often tackle these challenges individually, leading to fragmented solutions that lack a holistic approach to the overarching problem. In this paper, we propose an algorithm aimed at addressing the aforementioned challenges. By incorporating insights from various types of tasks, our method aims to enhance adaptability, scalability, and generalizability in graph classification. Motivated by the recognition that the underlying subgraph plays a crucial role in GNN prediction, while the remainder is task-irrelevant, we introduce the Core Knowledge Learning (CKL) framework for graph adaptation and scalability learning. CKL comprises several key modules, including the core subgraph knowledge submodule, graph domain adaptation module, and few-shot learning module for downstream tasks. Each module is tailored to tackle specific challenges in graph classification, such as domain shift, label inconsistencies, and data scarcity. By learning the core subgraph of the entire graph, we focus on the most pertinent features for task relevance. Consequently, our method offers benefits such as improved model performance, increased domain adaptability, and enhanced robustness to domain variations. Experimental results demonstrate significant performance enhancements achieved by our method compared to state-of-the-art approaches. Specifically, our method achieves notable improvements in accuracy and generalization across various datasets and evaluation metrics, underscoring its effectiveness in addressing the challenges of graph classification.

Baoming Zhang, MingCai Chen, Jianqing Song, Shuangjie Li, Jie Zhang, Chongjun Wang

Graph Neural Networks (GNNs) have demonstrated remarkable ability in semi-supervised node classification. However, most existing GNNs rely heavily on a large amount of labeled data for training, which is labor-intensive and requires extensive domain knowledge. In this paper, we first analyze the restrictions of GNNs generalization from the perspective of supervision signals in the context of few-shot semi-supervised node classification. To address these challenges, we propose a novel algorithm named NormProp, which utilizes the homophily assumption of unlabeled nodes to generate additional supervision signals, thereby enhancing the generalization against label scarcity. The key idea is to efficiently capture both the class information and the consistency of aggregation during message passing, via decoupling the direction and Euclidean norm of node representations. Moreover, we conduct a theoretical analysis to determine the upper bound of Euclidean norm, and then propose homophilous regularization to constraint the consistency of unlabeled nodes. Extensive experiments demonstrate that NormProp achieve state-of-the-art performance under low-label rate scenarios with low computational complexity.

Yilong Zang, Lingfei Ren, Yue Li, Zhikang Wang, David Antony Selby, Zheng Wang, Sebastian Josef Vollmer, Hongzhi Yin, Jiangning Song, Junhang Wu

Graph neural networks (GNNs) have shown promise in integrating protein-protein interaction (PPI) networks for identifying cancer genes in recent studies. However, due to the insufficient modeling of the biological information in PPI networks, more faithfully depiction of complex protein interaction patterns for cancer genes within the graph structure remains largely unexplored. This study takes a pioneering step toward bridging biological anomalies in protein interactions caused by cancer genes to statistical graph anomaly. We find a unique graph anomaly exhibited by cancer genes, namely weight heterogeneity, which manifests as significantly higher variance in edge weights of cancer gene nodes within the graph. Additionally, from the spectral perspective, we demonstrate that the weight heterogeneity could lead to the "flattening out" of spectral energy, with a concentration towards the extremes of the spectrum. Building on these insights, we propose the HIerarchical-Perspective Graph Neural Network (HIPGNN) that not only determines spectral energy distribution variations on the spectral perspective, but also perceives detailed protein interaction context on the spatial perspective. Extensive experiments are conducted on two reprocessed datasets STRINGdb and CPDB, and the experimental results demonstrate the superiority of HIPGNN.

Yanwei Yue, Guibin Zhang, Haoran Yang, Dawei Cheng

Graph Neural Networks (GNNs) demonstrate superior performance in various graph learning tasks, yet their wider real-world application is hindered by the computational overhead when applied to large-scale graphs. To address the issue, the Graph Lottery Hypothesis (GLT) has been proposed, advocating the identification of subgraphs and subnetworks, i.e., winning tickets, without compromising performance. The effectiveness of current GLT methods largely stems from the use of iterative magnitude pruning (IMP), which offers greater stability and better performance than one-shot pruning. However, identifying GLTs is highly computationally expensive, due to the iterative pruning and retraining required by IMP. In this paper, we reevaluate the correlation between one-shot pruning and IMP: while one-shot tickets are suboptimal compared to IMP, they offer a fast track to tickets with a stronger performance. We introduce a one-shot pruning and denoising framework to validate the efficacy of the fast track. Compared to current IMP-based GLT methods, our framework achieves a double-win situation of graph lottery tickets with higher sparsity and faster speeds. Through extensive experiments across 4 backbones and 6 datasets, our method demonstrates a 1.32%-45.62% improvement in weight sparsity and a 7.49%-22.71% increase in graph sparsity, along with a 1.7-44× speedup over IMP-based methods and 95.3%-98.6% MAC savings.

Weiqi Yue, Yuyu Yin, Xin Zhang, Binbin Shi, Tingting Liang, Jian Wan

Large Language Models (LLMs) offer groundbreaking advancements in recommender systems through superior text analysis and decision-making support. However, integrating LLMs into recommender systems still suffers from the problems of identifier uninterpretability and lack of transparency. To address these issues and fully leverage the capabilities of LLMs, we propose a chain of thought (CoT) based recommendation framework called CoT4Rec which employs LLMs as data enhancers for user preference analysis. Initially, we design a CoT reasoning strategy that can derive more behaviorally-aligned user preference features by clustering users’ historical interactions. Subsequently, we propose a two-stage recommendation model that not only makes full use of the world knowledge embedded in LLMs but also generates a logically transparent reasoning path. By integrating a user preference analyzer early in the recommendation pipeline, the model deeply analyzes users' historical interactions, helping to enhance the personalization and transparency of the recommender system. CoT4Rec demonstrates superior performance over existing state-of-the-art models in recommendation tasks across four public datasets, achieving improvements ranging from 2.2% to 12.2%.

Qi Yuan, Yang Liu, Yateng Tang, Xinhuan Chen, Xuehao Zheng, Qing He, Xiang Ao

Credit risk assessment has increasingly become a prominent research field due to the dramatically increased incidents of financial default. Traditional graph-based methods have been developed to detect defaulters within user-merchant commercial payment networks. However, these methods face challenges in detecting complex risks, primarily due to their neglect of user-to-user fund transfer interactions and the under-utilization of temporal information. In this paper, we propose a novel framework named Dynamic Graph Neural Network with Static Relations (DGNN-SR) for credit risk assessment, which can encode the dynamic transaction graph and the static fund transfer graph simultaneously. To fully harness the temporal information, DGNN-SR employs a multi-view time encoder to explore the semantics of both relative and absolute time. To enhance the dynamic representations with static relations, we devise an adaptive re-weighting strategy to incorporate the static relations into the dynamic representations of time encoder, which extracts more discriminative features for risk assessment. Extensive experiments on two real-world business datasets demonstrate that our proposed method achieves a 0.85% - 2.5% improvement over existing SOTA methods.

Huanhuan Yuan, Pengpeng Zhao, Jiaqing Fan, Junhua Fang, Guanfeng Liu, Victor S. Sheng

Collaborative reasoning enhances recommendation performance by combining the strengths of symbolic learning and deep neural learning. However, current collaborative reasoning models rely on parameterized networks to simulate logical operations within the reasoning process, which (1) do not comply with all axiomatic principles of classical logic and (2) limit the model's generalizability. To address these limitations, a Fuzzy logic approach tailored for Collaborative Reasoning (FuzzCR) is proposed in this work, aiming to augment the recommendation system with cognitive abilities. Specifically, this method redefines the sequential recommendation task as a logical query answering process to facilitate a more structured and logical progression of reasoning. Moreover, learning-free fuzzy logical operations are implemented for the designed reasoning process. Taking advantage of the inherent properties of fuzzy logic, these logical operations satisfy fundamental logical rules and ensure complete reasoning. After training, these operations can be applied to flexible reasoning processes, rather than being confined to fixed computation graphs, thereby exhibiting good generalizability. Extensive experiments conducted on publicly available datasets demonstrate the superiority of this method in solving the sequential recommendation task.

Zhizhi Yu, Chundong Liang, Xinglong Chang, Dongxiao He, Di Jin, Jianguo Wei

Fraud detection that aims to discern frauds from the majority of benigns has become an increasingly prominent research field. Recently, Graph Neural Networks (GNNs) have been widely applied in graph-based fraud detection due to their outstanding data analysis and mining capabilities. However, owing to the inherent homophily-heterophily mixture and class imbalance of fraud graphs, most GNNs with homophily assumption inevitably suffer from local abnormal signal loss during information propagation, posing significant challenges in situations where frauds are rare and valuable. To address the aforementioned issues, we present a novel dynamic neighborhood modeling via node-subgraph contrastive learning for graph-based fraud detection, dubbed DCL-GFD. Specifically, we first design a node abnormality estimation module from the perspective of feature, which analyses the likelihood of a node belonging to fraud or benign by comparing the feature similarity between the target node and its corresponding subgraph. We then present a dynamic neighborhood modeling mechanism guided by the abnormal probability of a node to adaptively group and aggregate neighborhood information. By this means, the target node can effectively aggregate the neighbor information from the perspective of fraud or benign, thereby preserving as much fraud characteristics that occupy minority population as possible. Extensive experiments across four real-world fraud detection datasets demonstrate the superiority and effectiveness of our proposed DCL-GFD over state-of-the-art baselines.

Xiaoyan Yu, Yifan Wei, Shuaishuai Zhou, Zhiwei Yang, Li Sun, Hao Peng, Liehuang Zhu, Philip S. Yu

The vast, complex, and dynamic nature of social message data has posed challenges to social event detection (SED). Despite considerable effort, these challenges persist, often resulting in inadequately expressive message representations (ineffective) and prolonged learning durations (inefficient). In response to the challenges, this work introduces an unsupervised framework, HyperSED (Hyperbolic SED). Specifically, the proposed framework first models social messages into semantic-based message anchors, and then leverages the structure of the anchor graph and the expressiveness of the hyperbolic space to acquire structure- and geometry-aware anchor representations. Finally, HyperSED builds the partitioning tree of the anchor message graph by incorporating differentiable structural information as the reflection of the detected events. Extensive experiments on public datasets demonstrate HyperSED's competitive performance, along with a substantial improvement in efficiency compared to the current state-of-the-art unsupervised paradigm. Statistically, HyperSED boosts incremental SED by an average of 2%, 2%, and 25% in NMI, AMI, and ARI, respectively; enhancing efficiency by up to 37.41 times and at least 12.10 times, illustrating the advancement of the proposed framework.

Penghang Yu, Zhiyi Tan, Guanming Lu, Bing-Kun Bao

Graph Neural Network (GNN)-based methods have recently emerged as effective approaches for multimedia recommendation. Typically, these methods employ message passing on the user-item interaction graph, and model user preferences by exploiting co-occurrence patterns. Despite their effectiveness, we argue that they insufficiently exploit the individual information, potentially limiting recommendation performance. To validate our argument, we first analyze existing methods from spectral graph theory. We identify that existing methods focus on capturing global structural features, but underutilize local structural features that convey individual information. Further detailed experiments reveal that such an underutilization leads to overly similar user preferences modeling. Furthermore, we propose a novel Principal Graph Learning (PGL) framework to address this issue. The idea is to enhance user preference modeling by effectively mining and utilizing principal local structural features. PGL first extracts the principal subgraph from the user-item interaction graph using two novel extraction operators: global-aware and local-aware subgraph extraction. It then employs message passing on the principal subgraph to comprehensively model user perference, with the aim of simultaneously capturing co-occurrence patterns and individual information. Compared to existing methods, PGL achieves an average performance improvement of 9%.

Jianxiang Yu, Yuxiang Ren, Chenghua Gong, Jiaqi Tan, Xiang Li, Xuecang Zhang

Text-attributed graphs have recently garnered significant attention due to their wide range of applications in web domains. Existing methodologies employ word embedding models for acquiring text representations as node features, which are subsequently fed into Graph Neural Networks (GNNs) for training. Recently, the advent of Large Language Models (LLMs) has introduced their powerful capabilities in information retrieval and text generation, which can greatly enhance the text attributes of graph data. Furthermore, the acquisition and labeling of extensive datasets are both costly and time-consuming endeavors. Consequently, few-shot learning has emerged as a crucial problem in the context of graph learning tasks. In order to tackle this challenge, we propose a lightweight paradigm called LLM4NG, which adopts a plug-and-play approach to establish supervision signals by leveraging LLMs for node generation. Specifically, we utilize LLMs to extract semantic information from the labels and generate samples that belong to these categories as exemplars. Subsequently, we employ an edge predictor to capture the structural information inherent in the raw dataset and integrate the newly generated samples into the original graph. This approach harnesses LLMs for enhancing class-level information and seamlessly introduces labeled nodes and edges without modifying the raw dataset, thereby facilitating the node classification task in few-shot scenarios. Extensive experiments demonstrate the outstanding performance of our proposed paradigm, particularly in low-shot scenarios. For instance, in the 1-shot setting of the ogbn-arxiv dataset, LLM4NG achieves a 76% improvement over the baseline model.

Suhee Yoon, Sanghyu Yoon, Ye Seul Sim, Sungik Choi, Kyungeun Lee, Hye-Seung Cho, Hankook Lee, Woohyung Lim

Out-of-distribution (OOD) detection, determining whether a given sample is part of the in-distribution (ID) or not, has been newly explored by a generative model-based outlier synthesizing approach, especially with diffusion models. Nonetheless, existing diffusion models often produce outliers that are considerably distant from the ID in pixel-space, showing limited efficacy for capturing subtle distinctions between ID and OOD. To address these issues, we propose a novel framework, Semantic Outlier generation via Nuisance Awareness (SONA), which directly utilizes informative pixel-space ID images in diffusion models. Thereby, the generated outliers achieve two crucial properties: (i) they closely resemble the ID mainly in nuisances, while (ii) represent discriminative semantic information. To facilitate the separate effect on semantics and nuisances, we introduce SONA guidance, providing region-specific guidance. Extensive experiments demonstrate the effectiveness of our framework, achieving an impressive AUROC of 87% on near-OOD datasets, which surpasses the performance of baseline methods by a significant margin of approximately 6%.

Yuyang Ye, Zhi Zheng, Yishan Shen, Tianshu Wang, Hengruo Zhang, Peijun Zhu, Runlong Yu, Kai Zhang, Hui Xiong

Recent advances in Large Language Models (LLMs) have demonstrated significant potential in the field of Recommendation Systems (RSs). Most existing studies have focused on converting user behavior logs into textual prompts and leveraging techniques such as prompt tuning to enable LLMs for recommendation tasks. Meanwhile, research interest has recently grown in multimodal recommendation systems that integrate data from images, text, and other sources using modality fusion techniques. This introduces new challenges to the existing LLM-based recommendation paradigm which relies solely on text modality information. Moreover, although Multimodal Large Language Models (MLLMs) capable of processing multi-modal inputs have emerged, how to equip MLLMs with multi-modal recommendation capabilities remains largely unexplored. To this end, in this paper, we propose the Multimodal Large Language Model-enhanced Sequential Multimodal Recommendation (MLLM-MSR) model. To capture the dynamic user preference, we design a two-stage user preference summarization method. Specifically, we first utilize an MLLM-based item-summarizer to extract image feature given an item and convert the image into text. Then, we employ a recurrent user preference summarization generation paradigm to capture the dynamic changes in user preferences based on an LLM-based user-summarizer. Finally, to enable the MLLM for multi-modal recommendation task, we propose to fine-tune a MLLM-based recommender using Supervised Fine-Tuning (SFT) techniques. Extensive evaluations across various datasets validate the effectiveness of MLLM-MSR, showcasing its superior ability to capture and adapt to the evolving dynamics of user preferences.

Jianan Ye, Zhaorui Tan, Yijie Hu, Xi Yang, Guangliang Cheng, Kaizhu Huang

Tabular anomaly detection under the one-class classification setting poses a significant challenge, as it involves accurately conceptualizing "normal" derived exclusively from a single category to discern anomalies from normal data variations. Capturing the intrinsic correlation among attributes within normal samples presents one promising method for learning the concept. To do so, the most recent effort relies on a learnable mask strategy with a reconstruction task. However, this wisdom may suffer from the risk of producing uniform masks, i.e., essentially nothing is masked, leading to less effective correlation learning. To address this issue, we presume that attributes related to others in normal samples can be divided into two non-overlapping and correlated subsets, defined as CorrSets, to capture the intrinsic correlation effectively. Accordingly, we introduce an innovative method that disentangles CorrSets from normal tabular data. To our knowledge, this is a pioneering effort to apply the concept of disentanglement for one-class anomaly detection on tabular data. Extensive experiments on 20 tabular datasets show that our method substantially outperforms the state-of-the-art methods and leads to an average performance improvement of 6.1% on AUC-PR and 2.1% on AUC-ROC.

Guangze Ye, Wen Wu, Guoqing Wang, Xi Chen, Hong Zheng, Liang He

The group recommendation (GR) aims to suggest items for a group of users in social networks. Existing work typically considers individual preferences as the sole factor in aggregating group preferences. Actually, social influence is also an important factor in modeling users' contributions to the final group decision. However, existing methods either neglect the social influence of individual members or bundle preferences and social influence together as a unified representation. As a result, these models emphasize the preferences of the majority within the group rather than the actual interaction items, which we refer to as the preference bias issue in GR. Moreover, the self-supervised learning (SSL) strategies they designed to address the issue of group data sparsity fail to account for users' contextual social weights when regulating group representations, leading to suboptimal results. To tackle these issues, we propose a novel model based on Disentangled Modeling of Preferences and Social Influence for Group Recommendation (DisRec). Concretely, we first design a user-level disentangling network to disentangle the preferences and social influence of group members with separate embedding propagation schemes based on (hyper)graph convolution networks. We then introduce a social-based contrastive learning strategy, selectively excluding user nodes based on their social importance to enhance group representations and alleviate the group-level data sparsity issue. The experimental results demonstrate that our model significantly outperforms state-of-the-art methods on two real-world datasets.

Zhe-Rui Yang, Jindong Han, Chang-Dong Wang, Hao Liu

Graph unlearning, which aims to eliminate the influence of specific nodes, edges, or attributes from a trained Graph Neural Network (GNN), is essential in applications where privacy, bias, or data obsolescence is a concern. However, existing graph unlearning techniques often necessitate additional training on the remaining data, leading to significant computational costs, particularly with large-scale graphs. To address these challenges, we propose a two-stage training-free approach, Erase then Rectify (ETR), designed for efficient and scalable graph unlearning while preserving the model utility. Specifically, we first build a theoretical foundation showing that masking parameters critical for unlearned samples enables effective unlearning. Building on this insight, the Erase stage strategically edits model parameters to eliminate the impact of unlearned samples and their propagated influence on intercorrelated nodes. To further ensure the GNN's utility, the Rectify stage devises a gradient approximation method to estimate the model's gradient on the remaining dataset, which is then used to enhance model performance. Overall, ETR achieves graph unlearning without additional training or full training data access, significantly reducing computational overhead and preserving data privacy. Extensive experiments on seven public datasets demonstrate the consistent superiority of ETR in model utility, unlearning efficiency, and unlearning effectiveness, establishing it as a promising solution for real-world graph unlearning challenges.

Yimeng Yang, Haokai Ma, Lei Meng, Shuo Xu, Ruobing Xie, Xiangxu Meng

Multimodal recommendation (MMRec) aims to integrate multimodal information of items to address the inherent data sparsity issue in collaborative-based recommendation. Traditional MMRec methods typically capture the structure-level item representations from the observed user behaviors within the multimodal graph, overlooking the potential impact of negative instances for personalized preference understanding. In light of the outstanding generative ability and step-by-step inference characteristic of Diffusion Models (DMs), we propose a Curriculum Conditioned Diffusion framework for Multimodal Recommendation (CCDRec), which precisely excavates the modality-aware distribution-level correlation among multi-modalities and elegantly integrates the reverse phase of DMs into negative sampling to highlight the most suitable instances in a curricular manner. Specifically, CCDRec proposes the Diffusion-controlled Multimodal Aligning module (DMA) to align multimodal knowledge with collaborative signals by capturing the fine-grained relationships among multi-modalities in the probabilistic distribution space. Furthermore, CCDRec designs the Negative-sensitive Diffusive Inferring module (NDI) to progressively synthesize the negative sample pool with diverse hardness to support the following knowledge-aware negative sampling. To gradually ramp up the training complexity, CCDRec further introduces a Curricular Negative Sampler (CNS) to tally the curriculum learning paradigm with the reverse phase of DMA, thereby adaptively sampling the gold-standard negative instances to enhance optimization. Extensive experiments on three datasets with four diverse backbones demonstrate the effectiveness and robustness of our CCDRec. The visualization analyses also clarify the underlying mechanism of our DMA in multimodal representation alignment and CNS in curricular negative discovery. The code and the corresponding dataset will be uploaded in the Appendix.

Jinyu Yang, Ruijia Wang, Cheng Yang, Bo Yan, Qimin Zhou, Yang Juan, Chuan Shi

Heterogeneous graphs (HGs) that contain various node and edge types are ubiquitous in real-world scenarios. Considering the common label sparsity problem in HGs, some researchers propose to pretrain on source HGs to extract general knowledge and then fine-tune on a target HG for knowledge transfer. However, existing methods often assume that source and target HGs share a single heterogeneity, meaning that they have the same types of nodes and edges, which contradicts the real-world scenarios requiring cross-heterogeneity transfer. Although a recent study has made some preliminary attempts in cross-heterogeneity learning, its definition of general knowledge heavily rely on human knowledge, which lacks flexibility and further leads to a suboptimal transfer. To address the problem, we propose a novel Language Model-enhanced Cross-Heterogeneity learning model, namely LMCH. Specifically, we first design a metapath-based corpus construction method to unify HG representations as languages. The corpora of source HGs are then used to fine-tune a pretrained Language Model (LM), enabling the LM to autonomously extract general knowledge across different HGs. Furthermore, to fully utilize the extensive unlabeled nodes in a few-labeled target HG, we propose an iterative training pipeline with the help of an extra Graph Neural Network (GNN) predictor, enhanced by LM-GNN contrastive alignment at the end of each iteration. Extensive experiments on four real-world datasets have demonstrated the superior performance of LMCH over state-of-the-art methods.

Hui-Yue Yang, Hui Chen, Ao Wang, Kai Chen, Zijia Lin, Yongliang Tang, Pengcheng Gao, Yuming Quan, Jungong Han, Guiguang Ding

Segment Anything Model (SAM) has made great progress in anomaly segmentation tasks due to its impressive generalization ability. However, existing methods that directly apply SAM through prompting often overlook the domain shift issue, where SAM performs well on natural images but struggles in industrial scenarios. Parameter-Efficient Fine-Tuning (PEFT) offers a promising solution, but it may yield suboptimal performance by not adequately addressing the perception challenges during adaptation to anomaly images. In this paper, we propose a novel Self-Perception Tuning (SPT) method, aiming to enhance SAM's perception capability for anomaly segmentation. The SPT method incorporates a self-drafting tuning strategy, which generates an initial coarse draft of the anomaly mask, followed by a refinement process. Additionally, a visual-relation-aware adapter is introduced to improve the perception of discriminative relational information for mask generation. Extensive experimental results on several benchmark datasets demonstrate that our SPT method can significantly outperform baseline methods, validating its effectiveness.