Adaptive Neighbor Research Articles

Detection of E-Commerce Fraud Review via Self-Paced Graph Contrast Learning

Abstract Recently, graph neural networks (GNNs) have been widely used for e-commerce review fraud detection by aggregating the neighborhood information of nodes in various relationships to highlight the suspiciousness of nodes. However, existing GNN-based detection methods are susceptible to sample class imbalance and fraud camouflage problems, resulting in poor quality of constructed graph structures and inability to learn reliable node embeddings. To address the above problems, we propose a novel e-commerce review fraud detection method based on self-paced graph contrast learning (SPCL-GNN). Firstly, the method constructs a subgraph by initially selecting nodes through a labeled balanced extractor. Secondly, the subgraph connections are filtered and complemented by combining self-paced graph contrast learning and an adaptive neighbor sampler to obtain an optimized graph structure. Again, an attention mechanism is introduced in intra- and inter-relationship aggregation to focus on the importance of aggregation under different relationships. Finally, the quality of the node embedding representation is further improved by maximizing the mutual information between the local and global representations. Experimental results on the Amazon and YelpChi datasets show that SPCL-GNN significantly outperforms the baseline.

STGNet: Short‐term residential load forecasting with spatial–temporal gated fusion network

AbstractWith the continuous transformation of the global power system, ensuring the flexibility and stability of the power system has become a direction that the global energy industry has been striving for. This puts forward higher requirements for load forecasting, especially high‐precision short‐term residential load forecasting. However, the high volatility and uncertainty of electric consumption make this problem challenging. Existing methods usually focus on capturing temporal correlations and ignore the underlying spatial correlations in historical loads, which is crucial for accurate forecasting. In this paper, we propose a novel spatial–temporal gated fusion network (STGNet) for short‐term residential load forecasting including not only individual residential load but also aggregated load. By seeking both adaptive neighbors and temporal similarity neighbors, we construct a dynamic graph based on a data‐driven method. On this basis, we provide an adaptive graph gated fusion mechanism to capture fine‐grained node‐specific spatial dependence more accurately and comprehensively. Finally, a bidirectional gated recurrent unit (BiGRU) is utilized to learn the temporal dependence of load data. Extensive experiments on real‐world datasets are conducted and demonstrate the superiority of STGNet over the existing approaches.

Adaptive Label Propagation for Group Anomaly Detection in Large-Scale Networks

This paper concentrates on group anomalies in general large-scale networks. Existing algorithms on group anomalies mainly focus on homogeneous or bipartite networks, and thus are difficult to apply to heterogeneous networks directly. Moreover, these algorithms follow the non-overlapping hypothesis of groups implicitly, which is improper in many scenarios. In this paper, we introduce a novel algorithm called Adaptive Label Propagation (ALP) to solve these problems. ALP is designed based on label propagation (LP) frameworks, for the reason that LP-based frameworks are simple in thought and easy to scale. ALP is able to find overlapping groups by label propagation with belonging coefficients, and can be applied to heterogeneous networks for its design of adaptive neighbor weighting. Assigning different weights to neighbors in label propagation is a challenging task. Inspired by the combinatorial multi-armed bandit mechanism, ALP views the neighbors of each node as arms to be selected, and iteratively updates their weights by evaluating their expected rewards in following iterations. Experiments are conducted on four real-world networks. The results show that LP-based methods are effective for detecting group anomalies, and the comparison results with several state-of-the-art label propagation based community detection methods show the effectiveness of the proposed method

Discriminative projection fuzzy K-Means with adaptive neighbors

Fuzzy K-Means (FKM) based on fuzzy theory is a classic method to effectively handle overlapping regions between clusters. However, redundant features and noises brought by increasing data dimensions affect the effectiveness of FKM. To cope with this issue, we propose a Discriminative Projection Fuzzy K-Means with adaptive neighbors (DPFKM) model, which embeds a discriminative subspace into FKM to facilitate learning of global structure and the most discriminative information. Firstly, a novel projection space with uncorrelated constraints are adopted to promote statistical independence among the data in the subspace as well as to enhance the ability of FKM to discern and utilize discriminative information. Secondly, the Frobenius norm is introduced as the regularization term to eliminate discrete solutions, while preserving the fuzziness and enhancing the sparsity of FKM. Finally, we propose a novel optimization method to finetune the model, with a particular focus on adaptive adjustment of the regularization parameter based on the proximity relationship between the samples and clusters. Comprehensive experiments are conducted on multiple data sets, and the results can prove the superiority of the proposed model.

Adaptive Neighbor Graph Aggregated Graph Attention Network for Heterogeneous Graph Embedding

Graph attention network can generate effective feature embedding by specifying different weights to different nodes. The key of the research on heterogeneous graph embedding is the way to combine its rich structural information with semantic relations to aggregate the neighborhood information. Most of the existing heterogeneous graph representation learning methods guide the selection of neighbors by defining various meta-paths on heterogeneous graphs. However, these models only consider the information contained in the nodes under different paths and ignore the potential semantic relationships of nodes in different neighbor graph structures, which leads to the underutilization of graph structure information. In this article, we propose a novel adaptive framework named Neighbor Graph Aggregated Graph Attention Network (NGGAN) to fully exploit graph topological details in heterogeneous graph, and aggregates their information to obtain an effective embedding. The key idea is to use different levels of sampling methods to define neighborhood, and use neighbor graphs to represent the complex structural interaction between nodes. In this way, the high-order relationship between nodes and the latent semantics of neighbor graphs can be fully explored. Afterward, a hierarchical attention mechanism is applied to adaptively learn the importance of different objects, including node information, path information, and neighbor graph information. Multiple downstream tasks are performed on four real-world heterogeneous graph datasets, and the experimental results demonstrate the effectiveness of NGGAN.

A segmentation method based on the deep fuzzy segmentation model in combined with SCANDLE clustering

To enhance the low clustering accuracy of the fuzzy clustering segmentation algorithm for analyzing high spatial resolution remote sensing images (HSRRSIs), a deep fuzzy segmentation model (DFSM)combined with Spectral Clustering with Adaptive Neighbors for Deep Learning (SCANDLE) clustering is proposed. The DFSM is used to over-segment the image, and the automatic coding structure is used to adaptively fuse the image features, minimizing the internal compactness and maximizing the external separability of the clustering, yielding better results. Meanwhile, the SCANDLE clustering model is used to cluster the over-segmentation results, and the matrix construction algorithm for adaptive neighborhood allocation is used to map the frame of the connected layer and optimally combine the over-segmentation images to realize the final segmentation results. The new method can accurately segment HSRRSIs with good segmentation performance.

Searching natural neighbors in an accelerated way

Natural neighbor (NaN) is an adaptive neighbor concept for automatically determining k value for k-nearest neighbors, which has been proved effective in clustering analysis, classification, and outlier detection. However, the existing algorithms for searching NaN employ the global search strategy, which increases unnecessary consumption of time on non-critical points. To solve this problem, we propose a novel accelerated algorithm by utilizing a local search strategy, called ASNN. ASNN is based on the fact that if remote points have reverse nearest neighbors, others are likely to have reverse nearest neighbors. Firstly, it extracts the remote points by defining remote radius. Secondly, only the neighbors of remote points are searched, so that ASNN can quickly obtain the natural neighbor eigenvalue (NaNE). Compared to existing algorithms for searching NaNs, ASNN drastically reduces the number of query points and time consumption. The efficiency and effectiveness of ASNN are proved on synthetic and real datasets.

Mutual structure learning for multiple kernel clustering

Multiple kernel clustering (MKC) has garnered considerable attention in recent years, aiming to obtain an optimal partition from multiple base kernels. Existing MKC methods typically focus on either learning the pairwise structure by constructing an optimal kernel from the base kernels or learning the cluster structure by integrating multiple base partitions into a consensus one for clustering. However, these previous approaches overlook the potential for mutual structure learning between the two aspects, i.e., pairwise and cluster structure, of clustering. To address this limitation, we propose a novel multiple kernel clustering method, referred to as MSL-MKC, which simultaneously learns pairwise and cluster structure to achieve an improved consensus partition matrix for clustering. Specifically, MSL-MKC extends the adaptive neighbor graph learning approach into kernel space to construct a discriminative similarity graph from multiple base kernels for pairwise structure learning. The consensus partition is formulated by aligning it with multiple base partitions for cluster structure learning. We utilize a Laplacian regularization term to preserve the pairwise structure in the consensus partition and inject the cluster structure into the discriminative similarity graph. The proposed method integrates similarity graph learning, partition fusion, and Laplacian regularization into a unified framework, optimized using an iterative algorithm. Experimental results on various benchmark datasets demonstrate the efficacy of MSL-MKC. The demo code of this work is publicly available at https://github.com/guanyuezhen/MSL-MKC.

Robust communication-efficient decentralized learning with heterogeneity

In this paper, we propose a robust communication-efficient decentralized learning algorithm, named RCEDL, to address data heterogeneity, communication heterogeneity and communication efficiency simultaneously in real-world scenarios. To the best of our knowledge, this is the first work to address the above challenges in a united framework. In detail, we design a compressed cross-gradient aggregation mechanism with delay to resolve the Non-IID issues, a blocking-resilient mechanism which allows receiving delayed parameters and gradients, and a communication-efficient mechanism including parameters compression and adaptive neighbors selection methods to reduce the communication cost as much as possible. In addition, we also provide convergence analysis of RCEDL and prove its convergence rate O(1NK) same with the state-of-the-art decentralized learning algorithms. Finally, we conduct extensive experiments to evaluate RCEDL algorithm on two widely used datasets CIFAR-10 and MNIST under different experimental settings. Compared with the state-of-the-art baseline methods, the proposed RCEDL is much more robust with higher accuracy and at least 3.4 × communication cost reduction under the heterogeneous environment.

Reinforcement-Learning-Based Adaptive Neighbor Discovery Algorithm for Directional Transmission-Enabled Internet of Underwater Things

Reinforcement-Learning-Based Adaptive Neighbor Discovery Algorithm for Directional Transmission-Enabled Internet of Underwater Things

Multiview Unsupervised Shapelet Learning for Multivariate Time Series Clustering.

Multivariate time series clustering has become an important research topic in the time series learning task, which aims to discover the correlation among multiple sequences and partition multivariate time series data into several subsets. Although there are currently some methods that can handle this task, most of them fail to discover informative subsequences from multivariate time series instances. In this paper, we first propose a novel unsupervised shapelet learning with adaptive neighbors (USLA) model for learning salient multivariate subsequences (i.e., multivariate shapelets), where the importance of each variate can be auto-determined when given a candidate multivariate shapelet. USLA performs multivariate shapelet-transformed representation learning and local structure learning simultaneously, but the performance of USLA with multivariate shapelets of different lengths is comparable to that of isometric multivariate shapelets. In fact, the shapelet-transformed representations learned from multivariate shapelets of different lengths can all represent multivariate time series instances separately and often contain complementary information to each other. Therefore, we develop a novel multiview USLA (MUSLA) model which treats shapelet-transformed representations learned from shapelets of different lengths as different views. In this way, MUSLA learns the importance of each view and the neighbor graph matrix among multiview representations when candidate multivariate shapelets of different lengths are determined. Experimental results show that MUSLA outperforms other state-of-the-art multivariate time series algorithms on real-world multivariate time series datasets.

Spectral Clustering With Adaptive Neighbors for Deep Learning.

Spectral clustering is a well-known clustering algorithm for unsupervised learning, and its improved algorithms have been successfully adapted for many real-world applications. However, traditional spectral clustering algorithms are still facing many challenges to the task of unsupervised learning for large-scale datasets because of the complexity and cost of affinity matrix construction and the eigen-decomposition of the Laplacian matrix. From this perspective, we are looking forward to finding a more efficient and effective way by adaptive neighbor assignments for affinity matrix construction to address the above limitation of spectral clustering. It tries to learn an affinity matrix from the view of global data distribution. Meanwhile, we propose a deep learning framework with fully connected layers to learn a mapping function for the purpose of replacing the traditional eigen-decomposition of the Laplacian matrix. Extensive experimental results have illustrated the competitiveness of the proposed algorithm. It is significantly superior to the existing clustering algorithms in the experiments of both toy datasets and real-world datasets.

Multi-view spectral clustering based on adaptive neighbor learning and low-rank tensor decomposition

Multi-view spectral clustering based on adaptive neighbor learning and low-rank tensor decomposition

One-step unsupervised clustering based on information theoretic metric and adaptive neighbor manifold regularization

One-step unsupervised clustering based on information theoretic metric and adaptive neighbor manifold regularization

Semi-supervised non-negative matrix tri-factorization with adaptive neighbors and block-diagonal learning

Graph-regularized non-negative matrix factorization (GNMF) is proved to be effective for the clustering of nonlinear separable data. Existing GNMF variants commonly improve model performance by adding different additional constraints or refining the model factorization form, which can lead to problems such as increased algorithm complexity or insufficient performance release. In this paper, we propose semi-supervised non-negative matrix tri-factorization with adaptive neighbors and block-diagonal (ABNMTF). Different from existing methods, in ABNMTF the similarity graph matrix is learned from the original data by adaptive neighbors k-nearest model, and a block diagonal matrix is constructed based on a few labeled data to update the similarity matrix. Our approach reconstructs the block diagonal structure into the adaptive similarity matrix, which enables simultaneous learning of the similarity matrix and label binding during factorization, engendering a distinguishable subspace representation matrix and therefore improving the clustering performance without significantly increasing the complexity of the algorithm. We also represent an optimization method to solve the ABNMTF and provide analyses of convergence and computational complexity. Extensive experiments on 8 real image datasets show that the proposed algorithm reports superior performance against several state-of-the-art approaches. Code has been made available at:https://github.com/LstinWh/ABNMTF.

Unsupervised Feature Selection With Weighted and Projected Adaptive Neighbors.

In the field of data mining, how to deal with high-dimensional data is a fundamental problem. If they are used directly, it is not only computationally expensive but also difficult to obtain satisfactory results. Unsupervised feature selection is designed to reduce the dimension of data by finding a subset of features in the absence of labels. Many unsupervised methods perform feature selection by exploring spectral analysis and manifold learning, such that the intrinsic structure of data can be preserved. However, most of these methods ignore a fact: due to the existence of noise features, the intrinsic structure directly built from original data may be unreliable. To solve this problem, a new unsupervised feature selection model is proposed. The graph structure, feature weights, and projection matrix are learned simultaneously, such that the intrinsic structure is constructed by the data that have been feature weighted and projected. For each data point, its nearest neighbors are acquired in the process of graph construction. Therefore, we call them adaptive neighbors. Besides, an additional constraint is added to the proposed model. It requires that a graph, corresponding to a similarity matrix, should contain exactly c connected components. Then, we present an optimization algorithm to solve the proposed model. Next, we discuss the method of determining the regularization parameter γ in our proposed method and analyze the computational complexity of the optimization algorithm. Finally, experiments are implemented on both synthetic and real-world datasets to demonstrate the effectiveness of the proposed method.

Disambiguation-based partial label feature selection via feature dependency and label consistency

Partial label learning refers to the issue that each training sample corresponds to a candidate label set containing only one valid label. Feature selection can be viewed as an effective pre-processing technology to improve the generalization performance of learning models, while the partial label feature selection task is challenging due to ambiguous labeling information. To this end, this paper utilizes granular ball computing and neighborhood rough sets to put forward a disambiguation-based partial label feature selection algorithm via feature dependency and label consistency. Firstly, the proposed algorithm performs an adaptive neighbor aggregation based on granular balls to disambiguate candidate labels. Adaptive neighbors are flexible to make more efficient learning performance. Then, considering the labeling confidence induced by disambiguation, the significance of each feature is evaluated by fusing the feature dependency from the neighborhood granularity and the label consistency among the nearest neighbors. Neighborhood rough sets directly handle continuous features that can reduce the adverse impact of data discretization on feature selection. The label consistency among adjacent samples is important for measuring feature discrimination. The fusion of feature dependency and label consistency can facilitate performance improvement. Finally, experiments conducted on eight controlled UCI and five real-world partial label datasets demonstrate that the proposed algorithm can improve the generalization performance of partial label learning and achieve superior performance compared to the partial label feature selection methods.

Entropy Regularized Unsupervised Clustering Based on Maximum Correntropy Criterion and Adaptive Neighbors

Constructing accurate similarity graph is an important process in graph-based clustering. However, traditional methods have three drawbacks, such as the inaccuracy of the similarity graph, the vulnerability to noise and outliers, and the need for additional discretization process. In order to eliminate these limitations, an entropy regularized unsupervised clustering based on maximum correntropy criterion and adaptive neighbors (ERMCC) is proposed. 1) Combining information entropy and adaptive neighbors to solve the trivial similarity distributions. And we introduce ℓ0-norm and spectral embedding to construct similarity graph with sparsity and strong segmentation ability. 2) Reducing the negative impact of non-Gaussian noise by reconstructing the error using correntropy. 3) The prediction label vector is directly obtained by calculating the sparse strongly connected components of the similarity graph Z, which avoids additional discretization process. Experiments are conducted on six typical datasets and the results showed the effectiveness of the method.

Context-Patch Representation Learning With Adaptive Neighbor Embedding for Robust Face Image Super-Resolution

Representation learning steered robust face image super-resolution (FSR) methods have attracted extensive attention in the past few decades. Most previous methods were devoted to exploiting the local position patches in the training set for FSR. However, they usually overlooked the sufficient usage of the contextual information around the testing patches, which are useful for stable representation learning. In this paper, we attempt to utilize the context-patch around the testing patch and propose a method named context-patch representation learning with adaptive neighbor embedding (CRL-ANE) for FSR. On one hand, we simultaneously use the testing position patch and its adjacent ones for stable representation weight learning. This contextual information can compensate for recovering missing details in the target patch. On the other hand, for each input patch set, due to its inherent facial structural properties, we design an adaptive neighbor embedding strategy to elaborately and adaptively choose primary candidates for more accurate reconstruction. These two improvements enable the proposed method to achieve better SR performance than some of the other methods. Qualitative and quantitative experiments on some benchmarks have validated the superiority of the proposed method over some state-of-the-art methods.

Local Linear Embedding with Adaptive Neighbors

Dimensionality reduction is one of the most important techniques in the field of data mining. It embeds high-dimensional data into a low-dimensional vector space while keeping the main information as much as possible. Locally Linear Embedding (LLE) as a typical manifold learning algorithm computes neighborhood preserving embeddings of high-dimensional inputs. Based on the thought of LLE, we propose a novel unsupervised dimensionality reduction model called Local Linear Embedding with Adaptive Neighbors (LLEAN). To achieve a desirable dimensionality reduction result, we impose adaptive neighbor strategy and adopt a projection matrix to project data into an optimal subspace. The relationship between every pair-wise data is investigated to help reveal the data structure. Augmented Lagrangian Multiplier (ALM) is devised in optimization procedure to effectively solve the proposed objective function. Comprehensive experiments on toy data and benchmark datasets have been done and the results show that LLEAN outperforms other state-of-the-art dimensionality reduction methods.