Deep anchor-based multi-view clustering methods enhance the scalability of neural networks by utilizing representative anchors to reduce the computational complexity of large-scale clustering. Despite their scalability advantages, existing approaches often incorporate anchor structures in a heuristic or task-agnostic manner, either through post-hoc graph construction or as auxiliary components for message passing. Such designs overlook the core structural demands of anchor-based clustering, neglecting key optimization principles. To bridge this gap, we revisit the underlying optimization problem of large-scale anchor-based multi-view clustering and unfold its iterative solution into a novel deep network architecture, termed LargeMvC-Net. The proposed model decomposes the anchor-based clustering process into three modules: RepresentModule, NoiseModule, and AnchorModule, corresponding to representation learning, noise suppression, and anchor estimation. Each module is derived by unfolding a step of the original optimization procedure into a dedicated network component, providing structural clarity and optimization traceability. In addition, an unsupervised reconstruction loss aligns each view with the anchor-induced latent space, encouraging consistent clustering structures across views. Extensive experiments on several large-scale multi-view benchmarks show that LargeMvC-Net consistently outperforms state-of-the-art methods in terms of both effectiveness and scalability. The source data, code. https://github.com/dushide/LargeMvC-Net_ACMMM_2025, and extended version http://arxiv.org/abs/2507.20980 are available.
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Infrared and visible image fusion (IVIF) aims to extract fine details from visible images and complementary information from infrared images. Most existing methods directly extract relevant and complementary features from each modality using neural networks, often overlooking the guidance process and the distinct frequency-domain characteristics of these features. To address this, we propose HRFusion-a novel frequency-domain framework that extracts complementary features from hybrid features using prior-constrained relevant features, effectively enhancing complementary information and reducing redundancy. In HRFusion, hybrid and relevant features are robustly extracted to guide the subsequent fusion stage. By leveraging frequency differences between complementary and relevant features, we introduce the Enhanced Complementary Frequency Network (ECFNet), which uses optimized Variational Mode Decomposition (VMD) to effectively separate and process these signals for fusion. The overall architecture is built with the proposed DTBlock, which captures both global and local features. Extensive experiments show that our method achieves state-of-the-art performance on the TNO, MSRS, M3FD, and Harvard Brain datasets, outperforming recent approaches. Code is available at https://github.com/liuuuuu777/HRFusion.
Deep long-tailed recognition (DLTR) has garnered increasing attention due to the inherent imbalance in many real-world problems (e.g., multimedia processing). Recently, some multi-objective optimization (MOO)-based solutions have been proposed to address conflicts during representation learning in DLTR. However, these methods face two primary challenges: (1) their effectiveness is subject to the power of MOO, which is arguable in recent literature, and (2) MOO approaches are resource-intensive due to frequent gradient operations. In this paper, we propose a novel approach: conflict-Buffering OptimizatiOn by Symmetry Teleportation (BOOST), which avoids altering complicated gradient combinations as previous methods did. A major challenge in this approach is the absence of off-the-shelf symmetry teleportation algorithms suitable for modern deep neural networks. To address this, we cast symmetry teleportation as the optimization of low-rank adaptation (LoRA). Specifically, we first divide categories into multiple groups and detect conflicts among them. When a conflict arises, we employ LoRA to identify an alternative point on the same loss level set, reducing conflicts and facilitating balanced optimization. To achieve this, we decouple symmetry teleportation into two objectives-loss invariance and balanced gradient maximization-and design corresponding objectives for LoRA optimization. Besides, we propose a trajectory reuse strategy to continually benefit from advanced optimizers. Extensive experiments demonstrate that BOOST achieves state-of-the-art performance across multiple mainstream DLTR datasets.
Anchor-based strategies have become the dominant paradigm for large-scale multi-view clustering, where the quality and representational capacity of anchors are crucial to clustering performance. Existing methods typically learn anchors adaptively, focusing only on dynamically selecting anchors from the original data. However, these methods often lack an information-theoretic metric to evaluate how effectively the selected anchors capture the intrinsic characteristics of their respective clusters. Moreover, few approaches attempt to enhance the internal structure of anchor matrix to further improve clustering performance. To address these challenges, we propose a novel Anchor-Driven High-Throughput Encoding (ADHTE) framework that optimizes anchors by maximizing their throughput encoding capacity. In this method, the High-Throughput Encoding rate serves as a metric for anchor effectiveness, and we employ a deep neural network to optimize the anchor matrix. In addition, we predefine a clustering indicator matrix to construct a consistent anchor matrix across views, thereby ensuring anchor alignment. Furthermore, we propose an edge-alignment learning scheme to produce a bipartite graph with consistent edges across views. Extensive experiments on eight benchmark datasets demonstrate that the proposed ADHTE framework exhibits superior effectiveness and robustness compared to other state-of-the-art methods. The code of this paper is released on https://github.com/enjoypiker/ADHTE.
In the field of computer vision, Vision Graph Neural Networks (ViG) have demonstrated significant potential in image understanding. By treating the divided image patches as nodes and constructing connection relationships based on neighbor attributes, ViG can efficiently model global dependencies within images with the help of graph attributes. However, most existing ViG methods have the problem of high computational complexity in graph construction and may not be able to effectively and fully explore the graph structure information. Besides, the heavy reliance on manual annotation labels limits the application potential of ViG in practical scenarios. To this end, in this paper, we propose a novel self-supervised vision graph contrastive learning method (S2ViG) based on image mixing strategy for efficient vision graph representation learning. It aims to use self-supervised method to alleviate the dependence on manual annotation and enhance the understanding of the global structure of the graph using two different vision graph construction methods. Specifically, we first employ image mixing strategy to uncover latent semantic relationships among multiple images. Then, we construct dynamic graph structures for image patches from local and global perspectives to obtain augmented contrastive samples. Finally, the multilevel contrastive loss is constructed to optimize the network. Experimental results show that our method achieves excellent performance on multiple datasets such as ImageNet-1K and CIFAR.
Graph condensation has emerged as an intriguing technique to save the expensive training costs of Graph Neural Networks (GNNs) by substituting a condensed small graph with the original graph. Despite the promising results achieved, previous methods usually employ an entangled paradigm of redundant parameters (nodes, edges, GNNs), which incurs complex joint optimization during condensation. This paradigm has considerably impeded the scalability of graph condensation, making it challenging to condense extremely large-scale graphs and generate high-fidelity condensed graphs. Therefore, we propose to disentangle the condensation process into a two-stage GNN-free paradigm, independently condensing nodes and generating edges while eliminating the need to optimize GNNs at the same time. The node condensation module avoids the complexity of GNNs by focusing on node feature alignment with anchors of the original graph, while the edge translation module constructs the edges of the condensed nodes by transferring the original structure knowledge with neighborhood anchors. This simple yet effective approach achieves at least 10 times faster than state-of-the-art methods with comparable accuracy on medium-scale graphs. Moreover, the proposed DisCo can successfully scale up to the Ogbn-papers100M graph containing over 100 million nodes with flexible reduction rates and improves performance on the second-largest Ogbn-products dataset by over 5%. Extensive downstream tasks and ablation study on five common datasets further demonstrate the effectiveness of the proposed DisCo framework. Our code is available at https://github.com/BangHonor/DisCo.
The increasing prevalence of large-scale graphs poses a significant challenge for graph neural network training, attributed to their substantial computational requirements. In response, graph condensation (GC) emerges as a promising data-centric solution aiming to substitute the large graph with a small yet informative condensed graph to facilitate data-efficient GNN training. However, existing GC methods suffer from intricate optimization processes, necessitating excessive computing resources and training time. In this paper, we revisit existing GC optimization strategies and identify two pervasive issues therein: (1) various GC optimization strategies converge to coarse-grained class-level node feature matching between the original and condensed graphs; (2) existing GC methods rely on a Siamese graph network architecture that requires time-consuming bi-level optimization with iterative gradient computations. To overcome these issues, we propose a training-free GC framework termed Class-partitioned Graph Condensation (CGC), which refines the node distribution matching from the class-to-class paradigm into a novel class-to-node paradigm, transforming the GC optimization into a class partition problem which can be efficiently solved by any clustering methods. Moreover, CGC incorporates a pre-defined graph structure to enable a closed-form solution for condensed node features, eliminating the need for back-and-forth gradient descent in existing GC approaches. Extensive experiments demonstrate that CGC achieves an exceedingly efficient condensation process with advanced accuracy. Compared with the state-of-the-art GC methods, CGC condenses the Ogbn-products graph within 30 seconds, achieving a speedup ranging from 102 × to 104 × and increasing accuracy by up to 4.2%.
Instruction Vulnerability Prediction for WebAssembly with Semantic Enhanced Code Property Graph
PDF ↗WebAssembly (Wasm) is a universal low-level bytecode designed to build modern web systems. Recent studies have shown that technologies such as voltage scaling and RowHammer attacks are expected to increase the likelihood of bit flips, which may cause unacceptable or catastrophic system failures. This raises concerns about the impact of bit flips on Wasm programs, which run as instructions in web systems, and it is an undeveloped topic since the features of Wasm differ from traditional programs. In this paper, we propose a novel paradigm, namely IVPSEG, to understand the error propagation of bit flips within Wasm programs. Specifically, we first use Large Language Models (LLMs) to automatically extract instruction embeddings containing semantic knowledge of each instruction's context. Then, we exploit these embeddings and program structure (control execution and data transfer) to construct a semantic enhanced code property graph, which implicates the potential path of error propagation. Based on this graph, we utilize graph neural networks and attention diffusion to optimize instruction embeddings by capturing different error propagation patterns for instruction vulnerability prediction. In particular, we build a Wasm compilation and fault generation system to simulate bit flips at Wasm runtime. Our experimental results with 14 benchmark programs and test cases show IVPSEG outperforms the state-of-the-art methods in terms of accuracy (average 13.06%ͽ ), F1-score (average 14.93%↑), and model robustness.
The problem of maximizing the adoption of a product through viral marketing in social networks has been studied heavily through postulated network models. We present a novel data-driven formulation of the problem. We use Graph Neural Networks (GNNs) to model the adoption of products by utilizing both topological and attribute information. The resulting Dynamic Viral Marketing (DVM) problem seeks to find the minimum budget and minimal set of dynamic topological and attribute changes in order to attain a specified adoption goal. We show that DVM is NP-Hard and is related to the existing influence maximization problem. Motivated by this connection, we develop the idea of Dynamic Gradient Influencing (DGI) that uses gradient ranking to find optimal perturbations and targets low-budget and high influence non-adopters in discrete steps. We use an efficient strategy for computing node budgets and develop the ''Meta-Influence'' heuristic for assessing a node's downstream influence. We evaluate DGI against multiple baselines and demonstrate gains on average of 24% on budget and 37% on AUC on real-world attributed networks. Our code is publicly available at https://github.com/saurabhsharma1993/dynamic_viral_marketing.
Ranking is an essential and practical task on dynamic graphs, which aims to prioritize future interaction candidates for given queries. While existing solutions achieve promising ranking performance, they leverage a single listwise loss to jointly optimize candidate sets, which leads to the gradient vanishing issue; and they employ neural networks to model complex temporal structures within a shared latent space, which fails to accurately capture multi-scale temporal patterns due to the frequency aliasing issue. To address these issues, we propose BandRank, a novel and robust band-pass disentangled ranking approach for dynamic graphs in the frequency domain. Concretely, we propose a band-pass disentangled representation (BPDR) approach, which disentangles complex temporal structures into multiple frequency bands and employs non-shared frequency-enhanced multilayer perceptrons (MLPs) to model each band independently. We prove that our BPDR approach ensures effective multi-scale learning for temporal structures by demonstrating its multi-scale global convolution property. Besides, we design a robust Harmonic Ranking (HR) loss to jointly optimize candidate sets and continuously track comparisons between real and virtual candidates, where we theoretically guarantee its ability to alleviate the gradient vanishing issue. Extensive experimental results show that our BandRank achieves an average improvement of 21.31% against eight baselines while demonstrating superior robustness across different learning scenarios.
Signed graph clustering is a critical technique for discovering community structures in graphs that exhibit both positive and negative relationships. We have identified two significant challenges in this domain: i) existing signed spectral methods are highly vulnerable to noise, which is prevalent in real-world scenarios; ii) the guiding principle "an enemy of my enemy is my friend", rooted in Social Balance Theory, often narrows or disrupts cluster boundaries in mainstream signed graph neural networks. Addressing these challenges, we propose the Deep Signed Graph Clustering framework (DSGC), which leverages Weak Balance Theory to enhance preprocessing and encoding for robust representation learning. First, DSGC introduces Violation Sign-Refine to denoise the signed network by correcting noisy edges with high-order neighbor information. Subsequently, Density-based Augmentation enhances semantic structures by adding positive edges within clusters and negative edges across clusters, following Weak Balance principles. The framework then utilizes Weak Balance principles to develop clustering-oriented signed neural networks to broaden cluster boundaries by emphasizing distinctions between negatively linked nodes. Finally, DSGC optimizes clustering assignments by minimizing a regularized clustering loss. Comprehensive experiments on synthetic and real-world datasets demonstrate DSGC consistently outperforms all baselines, establishing a new benchmark in signed graph clustering.
The rapid spread of rumors on social media has posed significant challenges to maintaining public trust and information integrity. Since an information cascade process is essentially a propagation tree, recent rumor detection models leverage graph neural networks to additionally capture information propagation patterns, thus outperforming text-only solutions. Given the variations in topics and social impact of the root node, different source information naturally has distinct outreach capabilities, resulting in different heights of propagation trees. This variation, however, impedes the data-driven design of existing graph-based rumor detectors. Given a shallow propagation tree with limited interactions, it is unlikely for graph-based approaches to capture sufficient cascading patterns, questioning their ability to handle less popular news or early detection needs. In contrast, a deep propagation tree is prone to noisy user responses, and this can in turn obfuscate the predictions. In this paper, we propose a novel Epidemiology-informed Network (EIN) that integrates epidemiological knowledge to enhance performance by overcoming data-driven methods' sensitivity to data quality. Meanwhile, to adapt epidemiology theory to rumor detection, it is expected that each user's stance toward the source information will be annotated. To bypass the costly and time-consuming human labeling process, we take advantage of large language models to generate stance labels, facilitating optimization objectives for learning epidemiology-informed representations. Our experimental results demonstrate that the proposed EIN not only outperforms state-of-the-art methods on real-world datasets but also exhibits enhanced robustness across varying tree depths.
Modern online advertising systems often involve a substantial number of advertisers in each auction, which results in scalability issues. To address this challenge, two-stage auctions have been designed and implemented in practice. These auctions enable efficient allocation of ad slots among numerous candidate advertisers in a short response time. This approach employs a fast yet coarse model in the first stage to select a small subset of advertisers, followed by a slow, more refined model to determine the final winners. However, existing two-stage auction mechanisms primarily focus on optimizing welfare, overlooking other critical objectives of the platform, such as revenue. In this paper, we propose ad-wise selection metrics, named Max-Wel and Max-Rev, which optimize the platform's welfare and revenue, respectively. These metrics are based on each ad's contribution to the corresponding objective function. We also provide theoretical guarantees for the proposed metrics. Our method is applicable to both welfare and revenue optimizations and can be easily implemented using neural networks. Through extensive experiments conducted on both synthetic and industrial data, we demonstrate the advantages of our proposed selection metrics compared to existing baselines.
MSDZip: Universal Lossless Compression for Multi-source Data via Stepwise-parallel and Learning-based Prediction
PDF ↗With the rapid development of the Internet, the huge amount of Multi-Source Data (MSD) brings challenges in data sharing and storing. Lossless data compression is the major way to solve those problems. Nowadays, neural-network technologies bring significant advantage in data modeling, making learning-based lossless compressors (LLCs) for multi-source data have emerged continuously. Compared with traditional compressors, the LLCs are more useful to catch complex redundancy patterns in MSD, and thus have great potential in enhancing compression ratio. However, existing LLCs still suffer from unsatisfactory compression ratios and lower throughput. To solve those problems, we propose a novel universal MSD lossless compressor called MSDZip via Stepwise-parallel and learning-based prediction technologies, it introduces two major designs: 1) We propose a Local-Global-Deep Mixing block in the learning-based prediction module to establish dependencies for MSD symbols, where designed Deep Mixing block solves the problem of unstable weights in the perceptual layers caused by cold-start problem to enhance the compression ratio significantly. 2) We design a Stepwise-parallel multi-GPU-accelerated compression strategy to address the compression speed and graphics memory constraints of single GPU in the face of large-scale data. The Stepwise-parallel module passes the source MSD to learning-based prediction model through the data chunking strategy, where the model of the previous chunk is used to guide the compression of the next chunk in parallel. We compare MSDZip with 5 classical learning-based and 6 traditional compressors on 12 well-studied real-world datasets. The experimental results demonstrate that MSDZip optimizes 3.418%-69.874% in terms of compression ratio and 31.171%-495.649% in terms of throughput compared to advanced LLCs. The source code of MSDZip and the linkages of the experimental datasets are available at https://github.com/mhuidong/MSDZip.
Toward Effective Digraph Representation Learning: A Magnetic Adaptive Propagation based Approach
PDF ↗The q-parameterized magnetic Laplacian serves as the foundation of directed graph (digraph) convolution, enabling this kind of digraph neural network (MagDG) to encode node features and structural insights by complex-domain message passing. Despite their success, limitations still exist: (1) The performance of MagDGs depends on selecting an appropriate q-parameter to construct suitable graph propagation equations in the complex domain. This parameter tuning limits model flexibility and significantly increases manual effort. (2) Most approaches treat all nodes with the same complex-domain propagation and aggregation rules, neglecting their unique digraph contexts. This oversight results in sub-optimal performance. To address the above issues, we propose two key techniques: (1) MAP is crafted to be a plug-and-play complex-domain propagation optimization strategy, enabling seamless integration into any MagDG to improve predictions while enjoying high running efficiency. (2) MAP++ is a new digraph learning framework, further incorporating a learnable mechanism to achieve adaptively edge-wise propagation and node-wise aggregation in the complex domain for better performance. Extensive experiments on 12 datasets demonstrate that MAP enjoys flexibility for it can be incorporated with any MagDG, and scalability as it can deal with web-scale digraphs. MAP++ achieves SOTA predictive performance on 4 different downstream tasks.
Ubiquitous sensors and mobile devices have spurred the growth of Web-of-Things (WoT) services in smart cities, making accurate spatio-temporal traffic predictions increasingly crucial. Leveraging advances in deep learning, recent Spatio-Temporal Graph Neural Networks (STGNNs) have achieved remarkable results. However, these methods address scenario-specific spatio-temporal heterogeneity by designing model architectures, often overlooking the importance of selecting optimal spatio-temporal knowledge (i.e., model inputs). In this paper, we propose an automated framework for spatio-temporal knowledge optimization to address this challenge. Our framework seamlessly integrates with downstream models, enhancing their performance across various prediction tasks. Specifically, we design a knowledge search space composed of parameters that represent scenario-specific spatio-temporal correlations within data. Additionally, we employ a bandit-based multi-fidelity algorithm for knowledge optimization to solve the constraint of limited resource. Furthermore, we adopt a meta-learner to extract transferable meta-knowledge about optimal knowledge, facilitating efficient exploration of the search space. Extensive experiments on five widely used real-world datasets demonstrate the effectiveness of our proposed framework. To the best of our knowledge, we are the first to automatically optimize spatio-temporal knowledge for spatio-temporal traffic prediction.
The distributionally robust optimization (DRO)-based graph neural network methods improve recommendation systems' out-of-distribution (OOD) generalization by optimizing the model's worst-case performance. However, these studies fail to consider the impact of noisy samples in the training data, which results in diminished generalization capabilities and lower accuracy. Through experimental and theoretical analysis, this paper reveals that current DRO-based graph recommendation methods assign greater weight to noise distribution, leading to model parameter learning being dominated by it. When the model overly focuses on fitting noise samples in the training data, it may learn irrelevant or meaningless features that cannot be generalized to OOD data. To address this challenge, we design a Distributionally Robust Graph model for OOD recommendation (DRGO). Specifically, our method first employs a simple and effective diffusion paradigm to alleviate the noisy effect in the latent space. Additionally, an entropy regularization term is introduced in the DRO objective function to avoid extreme sample weights in the worst-case distribution. Finally, we provide a theoretical proof of the generalization error bound of DRGO as well as a theoretical analysis of how our approach mitigates noisy sample effects, which helps to better understand the proposed framework from a theoretical perspective. We conduct extensive experiments on four datasets to evaluate the effectiveness of our framework against three typical distribution shifts, and the results demonstrate its superiority in both independently and identically distributed distributions (IID) and OOD.
Clustering over a graph seeks to partition the nodes therein into disjoint groups such that nodes within the same cluster are tightly-knit, while those across clusters are distant from each other. In practice, graphs are often attended with rich attributes, which are termed attributed graphs. By leveraging the complementary nature of graph topology and node attributes in such graphs, graph neural networks (GNNs) have obtained encouraging performance in graph clustering. However, existing GNN-based approaches strongly rely on the homophilic assumption of the input graph, and thus, largely fail on heterophilic graphs and others embodying numerous missing or noisy links, which are widely present in real life. To bridge this gap, this paper presents DGAC, an effective graph-agnostic solution for graph clustering. Particularly, DGAC overcomes the limitations of prior works by exploiting the high-order connectivity of nodes within not only the input graph G but also the affinity graph H underlying the attribute data. To achieve this goal, we first unify the embedding and clustering generations into a coherent framework optimizing the Dirichlet Energy on both G and H. Based thereon, theoretically-grounded solvers are developed for efficient constructions of the embeddings and clusters via graph diffusion operations, which aggregate features from specific neighbors, enabling the capture of high-order semantics from G or H. On top of that, DGAC includes three training loss functions that facilitate effective feature extraction and clustering. Extensive experiments, comparing DGAC against 12 baselines over 12 homophilic or heterophilic graph datasets, showcase that DGAC consistently and considerably outperforms all competitors in terms of clustering quality measured against ground truth labels.
Graph Neural Networks (GNNs)-based recommendation algorithms typically assume that training and testing data are drawn from independent and identically distributed (IID) spaces. However, this assumption often fails in the presence of out-of-distribution (OOD) data, resulting in significant performance degradation. In this study, we construct a Structural Causal Model (SCM) to analyze interaction data, revealing that environmental confounders (e.g., the COVID-19 pandemic) lead to unstable correlations in GNN-based models, thus impairing their generalization to OOD data. To address this issue, we propose a novel approach, graph representation learning via causal diffusion (CausalDiffRec) for OOD recommendation. This method enhances the model's generalization on OOD data by eliminating environmental confounding factors and learning invariant graph representations. Specifically, we use backdoor adjustment and variational inference to infer the real environmental distribution, thereby eliminating the impact of environmental confounders. This inferred distribution is then used as prior knowledge to guide the representation learning in the reverse phase of the diffusion process to learn the invariant representation. In addition,we provide a theoretical derivation that proves optimizing the objective function of CausalDiffRec can encourage the model to learn environment-invariant graph representations, thereby achieving excellent generalization performance in recommendations under distribution shifts. Our extensive experiments validate the effectiveness of CausalDiffRec in improving the generalization of OOD data, and the average improvement is up to 10.69% on Food, 18.83% on KuaiRec, 22.41% on Yelp2018, and 11.65% on Douban datasets.
Foundation models pre-trained on massive datasets, including large language models (LLMs), vision-language models (VLMs), and large multimodal models, have demonstrated remarkable success in diverse downstream tasks. However, recent studies have shown fundamental limitations of these models: (1) limited representational capacity(2) lower adaptability, and (3) diminishing scalability. These shortcomings raise a critical question: is Euclidean geometry truly the optimal inductive bias for all foundation models, or could incorporating alternative geometric spaces enable models to better align with the intrinsic structure of real-world data and improve reasoning processes? Hyperbolic spaces, a class of non-Euclidean manifolds characterized by exponential volume growth with respect to distance, offer a mathematically grounded solution. These spaces enable low-distortion embeddings of hierarchical structures (e.g., trees, taxonomies) and power-law distributions with substantially fewer dimensions compared to Euclidean counterparts. Recent advances have leveraged these properties to enhance foundation models, including improving LLMs' complex reasoning ability, VLMs' zero-shot generalization, and cross-modal semantic alignment, while maintaining parameter efficiency. This paper provides a comprehensive review of hyperbolic neural networks and their recent development for foundation models. We further outline key challenges and research directions to advance the field.