Graph Neural Networks (GNNs) have achieved remarkable success in processing graph data. However, when graph contains node label noise (label error) and graph structural noise (edge error), the performance of GNNs will severely decrease. In the learning with noisy label, the methods based on sample selection and label generation reveal promising outlook. Unfortunately, most of these methods only focus on single node label nosie or graph edge noise, which cannot handle both two types of noise at the same time. Furthermore, these methods also lack measures for error accumulation during training. In this work, we propose a simple yet efficient method, named GNN Defender (GDF), to address challenges of both types of noises through cross-correction and two regularization technologies. Specifically, GDF consists of two networks (Nnet and Enet), which Nnet is responsible to predict node category and Enet is used to predict the connected probability of two nodes. For sample selection, the mean distribution distance between node category prediction and node original label is regard as the selecting indicator. Then, we consider node labels with distances less than the mean as clean labels. Similar to node, replacing indicator with the mean cosine similarity of connected to Adaptively complete clean edge selection. For cross correction, we use the clean probabilities of the edges between connected nodes as weights to generate pseudo labels for target nodes on the subgraph. The pseudo labels generated by edge information carry more credible supervision information, thereby completing the correction of node noise labels. Next, we compare the label consistency between connected node to achieve the noisy edge correction. It is worth noting that the clean probabilities of node labels are used to smooth graph structure, improving the edge correction quality. Finally, we propose category consistency regularization and subgraph clustering regularization to weaken the impact of error accumulation. Extensive experiments on single noise and mixed noise datasets show that our proposed framework far outperforms current baseline methods, with improvements ranging from 1.43% to 14.72%.

Personalized Federated Continual Learning (PFCL) is a new practical scenario that poses greater challenges in sharing and personalizing knowledge. PFCL not only relies on knowledge fusion for server aggregation at the global spatial-temporal perspective but also needs model improvement for each client according to the local requirements. Existing methods, whether in Personalized Federated Learning (PFL) or Federated Continual Learning (FCL), have overlooked the multi-granularity representation of knowledge, which can be utilized to overcome Spatial-Temporal Catastrophic Forgetting (STCF) and adopt generalized knowledge to itself by coarse-to-fine human cognitive mechanisms. Moreover, it allows more effectively to personalized shared knowledge, thus serving its own purpose. To this end, we propose a novel concept called multi-granularity prompt, i.e., coarse-grained global prompt acquired through the common model learning process, and fine-grained local prompt used to personalize the generalized representation. The former focuses on efficiently transferring shared global knowledge without spatial forgetting, and the latter emphasizes specific learning of personalized local knowledge to overcome temporal forgetting. In addition, we design a selective prompt fusion mechanism for aggregating knowledge of global prompts distilled from different clients. By the exclusive fusion of coarse-grained knowledge, we achieve the transmission and refinement of common knowledge among clients, further enhancing the performance of personalization. Extensive experiments demonstrate the effectiveness of the proposed method in addressing STCF as well as improving personalized performance.

Fraud detection poses a fundamental yet challenging problem to mitigate various risks associated with fraudulent activities. However, existing methods are limited by their reliance on static data within single geographical regions, thereby restricting the trained model’s adaptability across different regions. Practically, when enterprises expand their business into new cities or countries, training a new model from scratch can incur high computational costs and lead to catastrophic forgetting (CF). To address these limitations, we propose cross-regional fraud detection as an incremental learning problem, enabling the development of a unified model capable of adapting across diverse regions without suffering from CF. Subsequently, we introduce Cross-Regional Continual Learning (CCL), a novel paradigm that facilitates knowledge transfer and maintains performance when incrementally training models from previously learned regions to new ones. Specifically, CCL utilizes prototype-based knowledge replay for effective knowledge transfer while implementing a parameter smoothing mechanism to alleviate forgetting. Furthermore, we construct heterogeneous trade graphs (HTGs) and leverage graph-based backbones to enhance knowledge representation and facilitate knowledge transfer by uncovering intricate semantics inherent in cross-regional datasets. Extensive experiments demonstrate the superiority of our proposed method over baseline approaches and its substantial improvement in cross-regional fraud detection performance.

As the spatio-temporal data of a city is not always available, insufficient data would lead to poor performance in some urban prediction tasks. Existing works utilize transfer learning to solve the data scarcity problem, but they ignore the differences in data distributions across cities, which leads to the ineffectiveness of knowledge transfer. In this paper, we propose a domain adversarial model with knowledge transfer for spatio-temporal prediction across cities, entitledCityTrans. Specifically, 1) the self-adaptive spatio-temporal knowledge (namely ST-Knowledge) is mined, to learn the latent spatial and temporal patterns among cities; 2) the domain-adversarial training strategy is introduced to enhance domain invariance; 3) a knowledge attention mechanism is proposed to extract the transferable information from the ST-Knowledge. Note that our CityTrans is an end-to-end domain adversarial spatio-temporal network without two-stage training (i.e., pre-training and fine-tuning). Finally, we conduct extensive experiments on two spatio-temporal prediction tasks: traffic (flow and speed) prediction, and air quality prediction. Experimental results demonstrate that CityTrans outperforms state-of-the-art models on all tasks by a significant margin.

Data privacy and silos are nontrivial and greatly challenging in many real-world applications. Federated learning is a decentralized approach to training models across multiple local clients without the exchange of raw data from client devices to global servers. However, existing works focus on a static data environment and ignore continual learning from streaming data with incremental tasks. Federated Continual Learning (FCL) is an emerging paradigm to address model learning in both federated and continual learning environments. The key objective of FCL is to fuse heterogeneous knowledge from different clients and retain knowledge of previous tasks while learning on new ones. In this work, we delineate federated learning and continual learning first and then discuss their integration, i.e., FCL, and particular FCL via knowledge fusion. In summary, our motivations are four-fold: we (1) raise a fundamental problem called “spatial-temporal catastrophic forgetting” and evaluate its impact on the performance using a well-known method called federated averaging (FedAvg), (2) integrate most of the existing FCL methods into two generic frameworks, namely synchronous FCL and asynchronous FCL, (3) categorize a large number of methods according to the mechanism involved in knowledge fusion, and finally (4) showcase an outlook on the future work of FCL.

Learning with noisy labels is nontrivial for deep learning models. Sample selection is a widely investigated research topic for handling noisy labels. However, most existing methods face challenges such as imprecise selection, a lack of global selection capabilities, and the need for tedious hyperparameter tuning. In this paper, we propose CoSaR (Collaborative Selection and adversarial Regularization ), a twin-networks based model that performs globally adaptive sample selection to tackle label noise. Specifically, the collaborative selection estimates the average distribution distances between predictions and generation labels through the collaboration of two networks to address the bias of the average distribution distances and the manual tuning of hyperparameters. Adversarial regularization is integrated into CoSaR to restrict the network's tendency to fit and memorize noisy labels, thereby enhancing its collaborative selection capability. In addition, we employ a label smoothing regularization and two types of data augmentation to enhance the robustness of the model further. Extensive experiments on both synthetic and real-world noisy datasets demonstrate that the proposed model outperforms baseline methods remarkably, with an accuracy improvement ranging between +0.56% and +15.14%.

The prosperity of crowdsourcing geospatial data provides increasing opportunities to understand our cities. In particular, OpenStreetMap (OSM) has become a prominent vault of geospatial data on the Web. In this context, learning urban region representations from OSM data, which is unexplored in previous work, could be profitable for various downstream tasks. In this work, we utilize OSM buildings (footprints) complemented with points of interest (POIs) to learn region representations, as buildings' shapes, spatial distributions, and properties have tight linkages to different urban functions. However, appealing as it seems, urban buildings often exhibit complex patterns to form dense or sparse areas, which brings significant challenges for unsupervised feature extraction. To address the challenges, we propose RegionDCL1, an unsupervised framework to deeply mine urban buildings. In a nutshell, we leverage random points generated by Poisson Disk Sampling to tackle data-sparse areas and utilize triplet loss with a novel adaptive margin to preserve inter-region correlations. Furthermore, we train our model with group-level and region-level contrastive learning, making it adaptive to varying region partitions. Extensive experiments in two global cities demonstrate that RegionDCL consistently outperforms the state-of-the-art counterparts across different region partitions, and outputs effective representations for inferring urban land use and population density.

With the expansion of modern database services, multi-user access has become a crucial feature in various practical application scenarios, including enterprise applications and e-commerce platforms. However, if multiple users submit queries within a short time frame, it can result in potential issues such as redundant computation and query concurrency. Unfortunately, most existing multi-query optimization methods, which aim to enhance query processing efficiency, have not adequately addressed these two problems, especially in the setting where multiple queries are being executed concurrently. To this end, we propose a novel method named Lemo for the multi-query optimization problem. Specifically, we propose a novel value network to predict latencies of concurrent queries as the foundation model for query plan generation. Furthermore, we introduce a shared buffer manager component to cache the intermediate results of sub-queries. The shared buffer manager applies a novel replacement policy to maintain the cached buffer with the objective of maximizing the opportunity for the reuse of the cached sub-queries. Based on the shared buffer, our proposed value network can incorporate the cached results into cost estimation to further guide Lemo in generating query plans, thus avoiding redundant computation. Lemo has been integrated into PostgreSQL and experiments conducted on real datasets with PostgreSQL show that it outperforms all the baselines in efficiency.

User identity linkage, which aims to link identities of a natural person across different social platforms, has attracted increasing research interest recently. Existing approaches usually first embed the identities as deterministic vectors in a shared latent space, and then learn a classifier based on the available annotations. However, the formation and characteristics of real-world social platforms are full of uncertainties, which makes these deterministic embedding based methods sub-optimal. Besides, semi-supervised models utilize the unlabeled data to help capture the intrinsic data distribution. However, the existing semi-supervised linkage methods heavily rely on the heuristically defined similarity measurements to incorporate the innate closeness between labeled and unlabeled samples. Such manually designed assumptions may not be consistent with the actual linkage signals and further introduce the noises. To address the mentioned limitations, in this paper we propose a novel Noise-aware Semi-supervised Variational User Identity Linkage (NSVUIL) model. Specifically, we first propose a novel supervised linkage module to incorporate the available annotations. Each social identity is represented by a Gaussian distribution in the Wasserstein space to simultaneously preserve the fine-grained social profiles and model the uncertainty of identities. Then, a noise-aware self-learning module is designed to faithfully augment the few available annotations, which is capable of filtering noises from the pseudo-labels generated by the supervised module. The filtered reliable candidates are added into the labeled set to provide enhanced training guidance for the next training iteration. Empirically, we evaluate the NSVUIL model over multiple real-world datasets, and the experimental results demonstrate its superiority.

Multi-view graph-based clustering aims to provide clustering solutions to multi-view data. However, most existing methods do not give sufficient consideration to weights of different views and require an additional clustering step to produce the final clusters. They also usually optimize their objectives based on fixed graph similarity matrices of all views. In this paper, we propose a general Graph-based Multi-view Clustering (GMC) to tackle these problems. GMC takes the data graph matrices of all views and fuses them to generate a unified graph matrix. The unified graph matrix in turn improves the data graph matrix of each view, and also gives the final clusters directly. The key novelty of GMC is its learning method, which can help the learning of each view graph matrix and the learning of the unified graph matrix in a mutual reinforcement manner. A novel multi-view fusion technique can automatically weight each data graph matrix to derive the unified graph matrix. A rank constraint without introducing a tuning parameter is also imposed on the graph Laplacian matrix of the unified matrix, which helps partition the data points naturally into the required number of clusters. An alternating iterative optimization algorithm is presented to optimize the objective function. Experimental results using both toy data and real-world data demonstrate that the proposed method outperforms state-of-the-art baselines markedly.

With the increasing popularity and diversity of social media, users tend to join multiple social platforms to enjoy different types of services. User identity linkage, which aims to link identical identities across different social platforms, has attracted increasing research attentions recently. Existing methods usually focus on pairwise identity linkage between two platforms, which cannot piece up the information from multi-sources to depict the intrinsic figures of social users. In this paper, we propose a novel adversarial learning based framework MSUIL with partially shared generators to perform Semi-supervised User Identity Linkage across Multiple social networks. The isomorphism across multiple platforms is captured as the complementary to link identities. The insight is that we aim to learn the desirable projection functions (generators) to not only minimize the distance between the distributions of user identities in arbitrary pairs of platforms, but also incorporate the available annotations as the learning guidance. The projection functions of different platform pairs share partial parameters, which ensures MSUIL can capture the interdependencies among multiple platforms and improves the model efficiency. Empirically, we evaluate our proposal over multiple datasets. The experimental results demonstrate the superiority of the proposed MSUIL model.

Real-world datasets often have representations in multiple views or come from multiple sources. Exploiting consistent or complementary information from multi-view data, multi-view clustering aims to get better clustering quality rather than relying on the individual view. In this paper, we propose a novel multi-view clustering method called multi-view concept clustering based on concept factorization with local manifold regularization, which drives a common consensus representation for multiple views. The local manifold regularization is incorporated into concept factorization to preserve the locally geometrical structure of the data space. Moreover, the weight of each view is learnt automatically and a co-normalized approach is designed to make fusion meaningful in terms of driving the common consensus representation. An iterative optimization algorithm based on the multiplicative rules is developed to minimize the objective function. Experimental results on nine reality datasets involving different fields demonstrate that the proposed method performs better than several state-of-the-art multi-view clustering methods.