Accurate traffic flow forecasting is crucial for intelligent transportation systems and relies on effectively modeling complex spatio-temporal dependencies. Although recent graph-based deep learning methods have achieved promising results, most focus on pairwise neighbor relationships, limiting their ability to capture higher-order spatio-temporal interactions in the traffic network. To overcome this limitation, we propose a novel Multi-source information driven Spatio-Temporal HyperGraph learning for traffic forecasting (MSTHG), which is designed to capture richer relational and semantic information. MSTHG introduces a multi-source hypergraph fusion strategy that jointly models dynamic high-order spatial and temporal correlations. Specifically, we build a spatial hypergraph based on geographical proximity to represent high-order spatial dependencies, and a temporal-trend hypergraph leveraging mutual information to capture nonlinear similarities among traffic series. To enhance the semantic richness of node representations, we integrate key daily and weekly information along with periodic features derived from Fast Fourier Transform (FFT). Following the obtained hypergraph, node representations are learned through a hypergraph convolutional network and subsequently processed by a GRU-MLP fusion module, which is designed to capture both local and global temporal dependencies. Extensive experiments on real-world benchmark datasets demonstrate that MSTHG outperforms state-of-the-art baselines. The source code is https://github.com/April-leng/MSTHG.git.

Performing complex First-Order Logic (FOL) queries on knowledge graphs is crucial for advancing knowledge reasoning. Knowledge graphs encapsulate rich semantic interactions among entities, encompassing both explicit structural knowledge represented by triples$(e_{1}, r, e_{2})$and implicit relational knowledge through multi-hop paths$(e_{1} \stackrel{r_{1}}{\rightarrow } \cdots e_{3} \cdots \stackrel{r_{2}}{\rightarrow } e_{2})$. Traditional models often focus solely on either triple-level or path-level knowledge, overlooking the benefits of integrating both to enhance logic query answering. This oversight leads to suboptimal representation learning and inefficient query reasoning. To overcome these challenges, we introduce a newSemantic-Aware representation learning model forQuery-answeringEmbeddings (SAQE). Specifically, SAQE employs a joint learning approach that integrates triple-level and path-level knowledge semantics and captures both explicit and implicit contextual nuances within the knowledge graph, yielding more accurate and contextually relevant representations. To efficiently handle the large combinatorial search spaces in FOL reasoning, we propose a novel hierarchical reasoning optimization strategy by a multi-hop tree thus optimizing subqueries rooted at variable nodes in a divide-and-conquer manner. Theoretical analysis confirms that SAQE effectively supports various types of FOL reasoning and enhances generalizations for query answering. Extensive experiments demonstrate that our model achieves state-of-the-art performance across several established datasets.

To make Graph Neural Networks (GNNs) meet the requirements of the Web, the universality and the generalization become two important research directions. On one hand, many universal GNNs are presented for semi-supervised tasks on both homophilic and non-homophilic graphs by distinguishing homophilic and heterophilic edges with the help of labels. On the other hand, self-supervised learning (SSL) algorithms on graphs are presented by leveraging the self-supervised learning schemes from computer vision and natural language processing. Unfortunately, graph universal self-supervised learning remains resolved. Most existing SSL methods on graphs, which often employ two-layer GCN as the encoder and train the mapping functions, can't alter the low-passing filtering characteristic of GCN. Therefore, to be universal, SSL must becustomized for the graph, i.e., learning the graph. However, learning the graph via universal GNNs is disabled in SSL, since their distinguishability on homophilic and heterophilic edges disappears without the labels. To overcome this difficulty, this paper proposes novel GrAph-customized Universal Self-Supervised Learning (GAUSS) by exploiting local attribute distribution. The main idea is to replace the global parameters with locally learnable propagation. To make the propagation matrix demonstrate the affinity between the nodes, the self-representative learning framework is employed with k-block diagonal regularization. Extensive experiments on synthetic and real-world datasets demonstrate its effectiveness, universality and robustness to noises.

Real-world graphs exhibit diverse structures, including homophilic and heterophilic patterns, necessitating the development of a universal Graph Contrastive Learning (GCL) framework. Nonetheless, the existing GCLs, especially those with a local focus, lack universality due to the mismatch between the input graph structure and the homophily assumption for two primary components of GCLs. Firstly, the encoder, commonly Graph Convolution Network (GCN), operates as a low-pass filter, which assumes the input graph to be homophilic. This makes it challenging to aggregate features from neighbor nodes of the same class on heterophilic graphs. Secondly, the local positive sampling regards neighbor nodes as positive samples, which is inspired by the homophily assumption. This results in feature similarity amplification for the samples from the different classes (i.e., FALSE positive samples). Therefore, it is crucial to feed the encoder and positive sampling of GCLs with homophilic graph structures. This paper presents a novel GCL framework, named gRaph cOntraStive Exploring uNiversality (ROSEN), designed to achieve this objective. Specifically, ROSEN equips a local graph structure inference module, utilizing the Block Diagonal Property (BDP) of the affinity matrix extracted from node ego networks. This module can generate the homophilic graph structure by selectively removing disassortative edges. Extensive evaluations validate the effectiveness and universality of ROSEN across node classification and node clustering tasks.

Graph neural network (GNN) can be formulated as the multiplication of the topology-related matrix (adjacency or Laplacian matrix) and node attribute matrix, i.e., operation in node-wise. Unfortunately, this unified formula reveals two inherent drawbacks. Firstly, the topology and node attribute are not reciprocal but biased. From employment, the topology information is repeatedly employed, while the node attribute is only used once. From parameterization perspective, the node attribute is parameterized with highly expressive MLPs, while topology is not. Secondly, the graph topology can not be fully explored. Only the local pairwise relation is explored, but the mesoscopic community structure, which is one of the most prominent characteristics of networks, is ignored. To alleviate these issues, this paper proposes the Graph Reciprocal Network (GRN) by treating node attribute and topology reciprocal. Firstly, it is illustrated that the node can be regarded and utilized as another kind of attribute. Secondly, a novel node representation scheme is proposed from the theory of Quadratic Networks, with a theoretical guarantee of the fine-grained element-wise product of the representations of the topology and attribute. Extensive experiments demonstrate the superior performance and robustness of the proposed GRN.

The current challenge of Deepfake detection is the cross-domain performance on unseen Deepfake data. Instead of extracting forgery artifacts that are robust to the cross-domain scenarios as most previous works, we propose a novel method named Single-sIde Guided Meta-leArning framework for DeepFake detection (SIGMA-DF) which simulates the cross-domain scenarios during training by synthesizing virtual testing domain through meta-learning. In addition, SIGMA-DF integrates the meta-learning algorithm with a new ensemble meta-learning framework, which separately trains multiple meta-learners in the meta-train phase to aggregate multiple domain shifts in each iteration. Hence multiple cross-domain scenarios are simulated, better leveraging the domain knowledge. In addition, considering the contribution of hard samples in single-side distribution optimization, a novel weighted single-side loss function is proposed to only narrow the intra-class distance between real faces and enlarge the inter-class distance for both real and fake faces in embedding space with the awareness of sample weights. Extensive experiments are conducted on several standard Deepfake detection datasets to demonstrate that the proposed SIGMA-DF achieves state-of-the-art performance. In particular, in the cross-domain evaluation from FF++ to Celeb-DF and DFDC, our SIGMA-DF outperforms the baselines by 4.4% and 4.5% in terms of AUC, respectively.

Due to the simplicity, intuition and explanation, most Graph Neural Networks (GNNs) are proposed by following the pipeline of message passing. Although they achieve superior performances in many tasks, propagation-based GNNs possess three essential drawbacks. Firstly, the propagation tends to produce smooth effect, which meets the inductive bias of homophily, and causes two serious issues: over-smoothing issue and performance drop on networks with heterophily. Secondly, the propagations to each node are irrelevant, which prevents GNNs from modeling high-order relation, and cause the GNNs fragile to the attributes noises. Thirdly, propagation-based GNNs may be fragile to topology noise, since they heavily relay on propagation over the topology. Therefore, the propagation, as the key component of most GNNs, may be the essence of some serious issues in GNNs. To get to the root of these issue, this paper attempts to replace the propagation with a novel local operation. Quantitative experimental analysis reveals: 1) the existence of low-rank characteristic in the node attributes from ego-networks and 2) the performance improvement by reducing its rank. Motivated by this finding, this paper propose the Low-Rank GNNs, whose key component is the low-rank attribute matrix approximation in ego-network. The graph topology is employed to construct the ego-networks instead of message propagation, which is sensitive to topology noises. The proposed Low-Rank GNNs posses some attractive characteristics, including robust to topology and attribute noises, parameter-free and parallelizable. Experimental evaluations demonstrate the superior performance, robustness to noises and universality of the proposed Low-Rank GNNs.

Although existing Graph Neural Networks (GNNs) based on message passing achieve state-of-the-art, the over-smoothing issue, node similarity distortion issue and dissatisfactory link prediction performance can’t be ignored. This paper summarizes these issues as the interference between topology and attribute for the first time. By leveraging the recently proposed optimization perspective of GNNs, this interference is analyzed and ascribed to that the learned representation in GNNs essentially compromises between the topology and node attribute. To alleviate the interference, this paper attempts to break this compromise by proposing a novel objective function, which fits node attribute and topology with different representations and introduces mutual exclusion constraints to reduce the redundancy in both representations. The mutual exclusion employs the statistical dependence, which regards the representations from topology and attribute as the observations of two random variables, and is implemented with Hilbert-Schmidt Independence Criterion. Derived from the novel objective function, a novel GNN, i.e., Graph Neural Network Beyond Compromise (GNN-BC), is proposed to iteratively updates the representations of topology and attribute by simultaneously capturing semantic information and removing the common information, and the final representation is the concatenation of them. The performance improvements on node classification and link prediction demonstrate the superiority of GNN-BC on relieving the interference.

Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are based on semi-definite programming (SDP), which is generally time-consuming and degrades the scalability, especially confronting large-scale data. To overcome this challenge, we propose a stochastic algorithm called SVRG-SBB, which has the following features: i) achieving good scalability via dropping positive semi-definite (PSD) constraints as serving a fast algorithm, i.e., stochastic variance reduced gradient (SVRG) method, and ii) adaptive learning via introducing a new, adaptive step size called the stabilized Barzilai-Borwein (SBB) step size. Theoretically, under some natural assumptions, we show theO(1/T) O(1T) rate of convergence to a stationary point of the proposed algorithm, where T T is the number of total iterations. Under the further Polyak-Łojasiewicz assumption, we can show the global linear convergence (i.e., exponentially fast converging to a global optimum) of the proposed algorithm. Numerous simulations and real-world data experiments are conducted to show the effectiveness of the proposed algorithm by comparing with the state-of-the-art methods, notably, much lower computational cost with good prediction performance.

Most of the existing Graph Neural Networks (GNNs) are deliberately designed for semi-supervised learning tasks, where supervision information (labelled node) is utilized to mitigate the oversmoothing problem of message passing. Unfortunately, the oversmoothing problem tends to be more severe in unsupervised tasks, since supervision information is not available. Since community structure/cluster is an essential characteristic of network, a natural approach to reduce the oversmoothing problem is to also constrain the node embeddings to maintain their own characteristics to prevent all the node embeddings from becoming too similar to be distinguished. In this paper, a novel Optimal Transport based Graph Neural Network (OT-GNN) is proposed to overcome the oversmoothing problem in unsupervised GNNs by imposing the equal-sized clustering constraints to the obtained node embeddings. To solve the combinatorial optimization problem, the constrained objective function of unsupervised GNN is relaxed to an Optimal Transport problem, and a fast version of the Sinkhorm-Knopp algorithm is adopted to handle large networks. Extensive experiments on node clustering and classification demonstrate the superior performance of our proposed OT-GNN.

Existing topic modeling approaches possess several issues, including the overfitting issue of Probablistic Latent Semantic Indexing (pLSI), the failure of capturing the rich topical correlations among topics in Latent Dirichlet Allocation (LDA), and high inference complexity. In this paper, we provide a new method to overcome the overfitting issue of pLSI by using the amortized inference with word embedding as input, instead of the Dirichlet prior in LDA. For generative topic model, the large number of free latent variables is the root of overfitting. To reduce the number of parameters, the amortized inference replaces the inference of latent variable with a function which possesses the shared (amortized) learnable parameters. The number of the shared parameters is fixed and independent of the scale of the corpus. To overcome the limited application of amortized inference to independent and identically distributed (i.i.d) data, a novel graph neural network, Graph Attention TOpic Network (GATON), is proposed to model the topic structure of non-i.i.d documents according to the following two observations. First, pLSI can be interpreted as stochastic block model (SBM) on a specific bi-partite graph. Second, graph attention network (GAT) can be explained as the semi-amortized inference of SBM, which relaxes the i.i.d data assumption of vanilla amortized inference. GATON provides a novel scheme, i.e. graph convolution operation based scheme, to integrate word similarity and word co-occurrence structure. Specifically, the bag-of-words document representation is modeled as a bi-partite graph topology. Meanwhile, word embedding, which captures the word similarity, is modeled as attribute of the word node and the term frequency vector is adopted as the attribute of the document node. Based on the weighted (attention) graph convolution operation, the word co-occurrence structure and word similarity patterns are seamlessly integrated for topic identification. Extensive experiments demonstrate that the effectiveness of GATON on topic identification not only benefits the document classification, but also significantly refines the input word embedding.

Despite that convolutional neural networks (CNNs) have recently demonstrated high-quality object classification, the trained models suffer from their extreme unexplainability. In this paper, we propose a general method, named as semantic correlation loss, for introducing common-sense knowledge to CNN architectures. In contrast to traditional cross-entropy loss which only considers the ground-truth class, we exploit to be aware of the accuracy of all classes. By adding this simple add-on, current multi-class classification models are able to improve on the ability of “making mistakes reasonably”. In addition, a slight performance gain is also achieved. Experimental results on CUB-200-2011, CIFAR -10 and 100 are provided to demonstrate the efficacy of our proposed method. Moreover, this novel loss is able to be applied in any setting as long as the labels of training data are included in the common sense knowledge base.

Fine-Grained Visual Classification (FGVC) is a challenging task, due to the small variation of visual representations from different categories. An effective solution is utilizing the bounding boxes centering the object parts to extract the discriminative representations. However, regular rectangles contains the background when the shape of the part is irregular, which may interfere with the classification. In this paper, we propose a weighted focus-attention deep network (FA-Net) to address the problem of background interference in fine-grained classification. In our FA-Net, a focus-attention module is proposed to identify the foreground region from the class activation map and remove the background. Two branches are employed to obtain the primary and secondary attention regions with focus-attention module, and a weighted layer is utilized to integrate the attention regions. Experiment results on three challenging fine-grained classification datasets (e.g., CUB-200-2011, Stanford Dogs and FGVC Aircraft) show that our FA-Net obtains state-of-the-art results and outperforms the other fine-grained algorithms.

Crowd counting, which aims to predict the number of persons in a highly congested scene, has been widely explored and can be used in many applications like video surveillance, pedestrian flow, etc. The severe mutual occlusion among person, the large perspective distortion and the scale variations always hinder an accurate estimation. Although existing approaches have made much progress, there still has room for improvement. The drawbacks of existing methods are 2-fold: (1)the scale information, which is an important factor for crowd counting, is always insufficiently explored and thus cannot bring well-estimated results; (2)using a unified framework for the whole image may result to a rough estimation in subregions, and thus leads to inaccurate estimation. Motivated by this, we propose a new method to address these problems. We first construct a crowd-specific and scale-aware convolutional neural network, which considers crowd scale variations and integrates multi-scale feature representations in the Cross Scale Module (CSM), to produce the initial predicted density map. Then the proposed Local Refine Modules (LRMs) are performed to gradually re-estimate predictions of subregions. We conduct experiments on three crowd counting datasets (the ShanghaiTech dataset, the UCF_CC_50 dataset and the UCSD dataset). Experiments show that our proposed method achieves superior performance compared with the state-of-the-arts. Besides, we conduct experiments on counting vehicles in the TRANCOS dataset and get better results, which proves the generalization ability of the proposed method.

Humankind has always been in pursuit of fashion. Nevertheless, people are often troubled by collocating clothes, e.g., tops, bottoms, shoes, and accessories, from numerous fashion items in their closets. Moreover, it may be expensive and inconvenient to employ a fashion stylist. In this paper, we present Stile, an end-to-end intelligent fashion consultant system, to generate stylish outfits for given items. Unlike previous systems, our framework considers the global compatibility of fashion items in the outfit and models the dependencies among items in a fixed order via a bidirectional LSTM. Therefore, it can guarantee that items in the same outfit should share a similar style and neither redundant nor missing items exist in the resulting outfit for essential categories. The demonstration shows that our proposed system provides people with a practical and convenient solution to find natural and proper fashion outfits.

Scene text detection is currently a popular research topic in the computer vision community. However, it is a challenging task due to the variations of texts and clutter backgrounds. In this paper, we propose a novel framework for scene text localization. Based on the region proposal network, a Strip-based Text Detection Network (STDN) is developed with vertical anchor mechanism to predict the text/non-text strip-shaped proposals. Meanwhile, we incorporate the recurrent neural network layers in the proposed network to refine the predicted results. Specifically, hard example mining is performed to train the STDN with cascade learning, which has a remarkable improvement in precision. Besides, we exploit a clustering algorithm to generate anchor dimensions spontaneously without hand-picking, which is portable and time-saving. The text detection framework achieves the state-of-the-art performance on ICDAR2013 with 0.89 F-measure.

Lasso simultaneously conducts variable selection and supervised regression. In this paper, we extend Lasso to multiple output prediction, which belongs to the categories of structured learning. Though structured learning makes use of both input and output simultaneously, the joint feature mapping in current framework of structured learning is usually application-specific. As a result, ad hoc heuristics have to be employed to design different joint feature mapping functions for different applications, which results in the lackness of generalization ability for multiple output prediction. To address this limitation, in this paper, we propose to augment Lasso with output by decoupling the joint feature mapping function of traditional structured learning. The contribution of this paper is three-fold: 1) The augmented Lasso conducts regression and variable selection on both the input and output features, and thus the learned model could fit an output with both the selected input variables and the other correlated outputs. 2) To be more general, we set up nonlinear dependencies among output variables by generalized Lasso. 3) Moreover, the Augmented Lagrangian Method (ALM) with Alternating Direction Minimizing (ADM) strategy is used to find the optimal model parameters. The extensive experimental results demonstrate the effectiveness of the proposed method.