Gene regulatory network (GRN) inference is essential for understanding gene interactions that control biological functions and disease progression. Its goal is to uncover regulatory relationships among genes using gene expression data, such as single-cell RNA sequencing (scRNA-seq). With the rise of deep learning, numerous approaches-particularly those using Graph Neural Networks (GNNs)-have shown promising progress in GRN inference. However, scRNA-seq datasets exhibit high sparsity, noise, dropout events, and cellular heterogeneity. These characteristics make it difficult for fixed GNN architectures to generalize across scRNA-seq datasets, often leading to performance degradation. To address this challenge, we propose AutoGRN, an automated graph neural network framework for gene regulatory network inference tasks. AutoGRN incorporates a range of components that potentially influence GRN inference performance into its search space. Through a genetic search algorithm constrained by information entropy, AutoGRN identifies optimal GNN architectures by adapting to the characteristics of individual datasets. Experimental results show that AutoGRN outperforms existing methods in prediction accuracy across multiple public datasets.

Drug repositioning is critical in accelerating drug discovery, which identifies new indications for existing drugs by modeling drug-disease associations. Compared to traditional methods, graph neural networks (GNNs) have recently gained widespread attention due to their ability to effectively aggregate information from neighboring nodes in drug-disease heterogeneous graphs. The GNN-based methods need effective node embeddings for information aggregation. However, they generate the node embeddings by random initialization, rather than incorporating the high-quality expert knowledge involving biological mechanisms in the databases. This limits their capacity to generate interpretable node embeddings aligned with expert knowledge. To bridge this gap, we develop a novel framework dubbed DReKGNN (Drug Repositioning based on expert Knowledge augmented Graph Neural Network). To be specific, DReKGNN will first adopt large language models (LLMs) as a semantic bridge between expert knowledge and GNNs. To ensure the accuracy of expert knowledge, DReKGNN does not rely on prompt templates in LLMs to generate knowledge descriptions for drugs and diseases. Instead, it extracts expert knowledge directly from the DrugBank and OMIM databases. The effective node embeddings with interpretable semantic information will be generated from expert knowledge descriptions involving biological mechanisms by LLMs. Then, we demonstrate that there is a need to mitigate noise when LLM node embeddings serving drug repositioning prediction tasks. Considering this design need, we integrate GNNs with LLM node embeddings by a mean aggregation strategy. The experiment results of performance comparison and case study show the effectiveness of DReKGNN in predicting drug-disease associations. The code is available at https://github.com/csubigdata-Organization/DReKGNN.

Graph neural networks (GNNs) have shown promising performance in the gene regulatory network (G RN) inference task. As mainstream GNNs are developed based on link prediction paradigm, they are susceptible to the impact of noise and imbalanced samples. Recent advances challenge this paradigm by representing gene pairs as nodes in a new graph, thereby recasting GRN inference as graph anomaly detection paradigm and demonstrating superior performance. However, we reveal that diverse GRN datasets require varied model architectures to achieve accurate prediction, and using only a fixed model architecture limits the full potential of graph anomaly detection paradigm, thus leading to suboptimal performance. In this study, we propose AutoGRAD, an automated gene regulatory network inference framework, to systematically explore the benefits of graph anomaly detection paradigm under different model architectures. Through deconstructing graph anomaly detection paradigm for GRN inference, AutoGRAD presents a novel search space consisting of various components, such as similarity computation, graph sparsification, and aggregation function. To this end, AutoGRAD adopts a reinforcement learning search strategy with a proxy training mechanism, which significantly reduces search costs while dynamically identifying the optimal model architecture specific to the dataset. Experimental results on multiple benchmark GRN datasets confirm that AutoGRAD consistently outperforms state-of-the-art baseline models, demonstrating the effectiveness of automatic model architecture adaptation for G RN inference. The code is available at https://github.com/csubigdata-Oraanization/AutoGRAD.

Graph neural networks (GNNs) for fraud detection has received extensive attention, where malicious behaviors often exhibit complex relational patterns. Despite their success, the GNN architecture design of existing graph-based fraud detection methods requires significant manual work and expert knowledge. The application of manually designed architectures to diverse real-world scenarios remains a huge time cost, as it requires numerous parameter tuning for varying conditions. Moreover, the GNN-based methods suffer from the over-smoothing problem during multi-layer message passing, which limits the performance in the fraud detection task. To address these problems, we propose the Automatic lightning decoupled Graph neural architecture search for Fraud Detection (AutoGFD). Specifically, AutoGFD designs the decoupled search algorithm to automatically construct the optimal architecture from the specialized architecture search space for the fraud detection task, which can effectively solve the over-smoothing problem. In addition, AutoGFD designs the lightning search tuning mechanism to improve the efficiency of architecture estimation. As far as we know, AutoGFD is the first attempt to design decoupled architecture search for fraud detection, which can automatically search for optimal architectures in different fraud detection scenarios without manual design and expert knowledge. The experimental results based on multiple benchmark datasets show that AutoGFD can achieve significant performance advantages over state-of-the-art baseline methods.

Cyclicity and trends are fundamental components of time series data and are critical for long-term time series forecasting (LTSF). However, most existing models focus on a single fixed cycle and struggle to disentangle multiple overlapping cyclical patterns, such as daily fluctuations nested within weekly patterns. This limitation prevents them from fully leveraging the complex cyclical patterns inherent in real-world time series. Moreover, many of these models rely on intricate designs to capture such patterns, highlighting the need for a more direct modeling approach. In this paper, we analyze the time series through a multi-cycle perspective, explicitly modeling multiple cyclical patterns and decoupling trend variations from time series data by utilizing these patterns. We propose MctLF, a Multiple cyclicity-trend Learning Framework that leverages learnable cyclicity-trend decomposition to adaptively capture multi-scale cyclical patterns and trend variations. Specifically, the Learnable Multi-cyclicity Extraction Block identifies significant cycles and explicitly models these cyclical patterns. After decoupling the time series into significant cycles and trend variations, the 2D Trend Extraction Block reshapes trend components into 2D tensors to further capture trend variations. Final predictions are generated by the Cyclicity-Trend Aggregation Block. Experiments on seven real-world datasets demonstrate that MctLF achieves state-of-the-art performance and excels in computational efficiency. Code availability: https://github.com/Ian-Shi11/MctLF.

Graph fraud detection has garnered significant attention as Graph Neural Networks (GNNs) have proven effective in modeling complex relationships within multimodal data. However, existing graph fraud detection methods typically use preprocessed node embeddings and predefined graph structures to reveal fraudsters, which ignore the rich semantic cues contained in raw textual information. Although Large Language Models (LLMs) exhibit powerful capabilities in processing textual information, it remains a significant challenge to perform multimodal fusion of processed textual embeddings with graph structures. In this paper, we propose a Multi-level LLM Enhanced Graph Fraud Detection framework called MLED. In MLED, we utilize LLMs to extract external knowledge from textual information to enhance graph fraud detection methods. To integrate LLMs with graph structure information and enhance the ability to distinguish fraudsters, we design a multi-level LLM enhanced framework including type-level enhancer and relation-level enhancer. One is to enhance the difference between the fraudsters and the benign entities, the other is to enhance the importance of the fraudsters in different relations. The experiments on four real-world datasets show that MLED achieves state-of-the-art performance in graph fraud detection as a generalized framework that can be applied to existing methods.

Heterogeneous temporal graphs (HTGs) are ubiquitous data structures in the real world. Recently, to enhance representation learning on HTGs, numerous attention-based neural networks have been proposed. Despite these successes, existing methods rely on a decoupled temporal and spatial learning paradigm, which weakens interactions of spatio-temporal information and leads to a high model complexity. To bridge this gap, we propose a novel learning paradigm for HTGs called Simple and Efficient Heterogeneous Temporal Graph Neural Network (SE-HTGNN). Specifically, we innovatively integrate temporal modeling into spatial learning via a novel dynamic attention mechanism, which substantially reduces model complexity while enhancing discriminative representation learning on HTGs. Additionally, to comprehensively and adaptively understand HTGs, we leverage large language models to prompt SE-HTGNN, enabling the model to capture the implicit properties of node types as prior knowledge. Extensive experiments demonstrate that SE-HTGNN achieves up to 10× speed-up over the state-of-the-art and latest baseline while maintaining the best forecasting accuracy.

Multi-modal multi-view graph learning models have achieved significant success in medical outcome prediction, combining various modalities to enhance the performance of various medical tasks. However, current architectures for multi-modal multi-view graph learning (M3GL) models heavily depend on manual design, demanding significant effort and expert experience. Meanwhile, significant advancements have been achieved in the field of graph neural architecture search (GNAS), contributing to the automated design of learning architectures based on graphs. However, GNAS faces challenges in automating multimodal multi-view graph learning (M3GL) models, as existing frameworks cannot handle M3GL architecture topology, and current search spaces do not consider M3GL models. To address the above challenges, we propose, for the first time, a multi-modal multi-view graph neural architecture search (M3GNAS) framework that automates the construction of the optimal M3GL models, enabling the integration of multi-modal features from different views. We also design an effective multi-modal multi-view learning (M3L) search space to develop inner-view and outer-view graph representation learning in the context of graph learning, obtaining a latent graph representation tailored to the specific requirements of downstream tasks. To examine the effectiveness of M3GNAS, it is evaluated on medical outcome prediction tasks. The experimental findings demonstrate our proposed framework’s superior performance compared to state-of-the-art models.

Drug repositioning has become attractive because it can significantly accelerate drug discovery and reduce development costs by identifying new indications for existing drugs. Based on graph neural networks (GNNs), the recent work utilizes inter-domain information (drug-disease association network) and intra-domain information (drug-drug similarity network and disease-disease similarity network) to learn the effective representation of drugs and diseases. However, they overlook the significant impact of the adaptive inter-domain and intra-domain information fusion operation on model performance under different data-splitting strategies. Moreover, manually designing GNN architectures for specific drug repositioning datasets is time-consuming and expert-dependent. To address the above problem, we propose an adaptive drug repositioning prediction method called AdaDR, which can adaptively fuse inter-domain and intra-domain information under different data-splitting strategies and automatically design the optimal GNN architecture for each drug repositioning dataset. Specifically, we first design a unified drug repositioning search space with different information fusion operations and various handcrafted GNN architectures. Then, a drug repositioning model search will be adopted to enable an efficient search. Empirical studies on three benchmark datasets demonstrate that the optimal drug repositioning model identified by our proposed AdaDR achieves the best performance among competitive baselines. Through the analysis of the case study, the applicability of AdaDR in practical scenarios is further validated. The code is available at: https://github.com/csubigdata-Organization/AdaDR.

Heterogeneous dynamic graph neural networks (HDGNNs) are effective methods for processing heterogeneous temporal graphs (HTGs), which serve as ubiquitous data structures in real-world scenarios. The previous HDGNN paradigm obtains representations of future target nodes by mining the spatial heterogeneity and temporal dependence of node attributes, ignoring the learning of relation temporal dependence. However, through experience, we find that the learning of relation temporal dependence, which describes the evolving trends in the importance of neighbors under a certain relation, is beneficial for representation learning of HTGs. To bridge this gap, we propose a novel end-to-end heterogeneous temporal graph learning paradigm called Relation Time-aware Heterogeneous Dynamic Graph Neural Networks (ReTag). Compared to previous HDGNNs, ReTag extracts the temporal dependence of relations from historical relation information and the evolving node attributes to drive subsequent spatio-temporal representation learning. As far as we know, ReTag is the first attempt to perform learning of the temporal dependence of relation, which can generate a more effective representation for different downstream tasks of HTGs. The experimental results of different downstream tasks of HTGs based on multiple benchmark datasets show that ReTag can obtain obvious performance advantages compared with the sota baseline method.

Drug-drug interaction (DDI) has attracted widespread attention because when incompatible drugs are taken together, DDI will lead to adverse effects on the body, such as drug poisoning or reduced drug efficacy. The adverse effects of DDI are closely determined by the molecular structures of the drugs involved. To represent drug data effectively, researchers usually treat the molecular structure of drugs as a molecule graph. Then, previous studies can use the handcrafted graph neural network (GNN) model to learn the molecular graph representations of drugs for DDI prediction. However, in the field of bioinformatics, manually designing GNN architectures for specific molecular structure datasets is time-consuming and depends on expert experience. To address this problem, we propose an automatic drug-drug interaction prediction method named AutoDDI that can efficiently and automatically design the GNN architecture for drug-drug interaction prediction without manual intervention. To this end, we first design an effective search space for drug-drug interaction prediction by revisiting various handcrafted GNN architectures. Then, to efficiently and automatically design the optimal GNN architecture for each drug dataset from the search space, a reinforcement learning search algorithm is adopted. The experiment results show that AutoDDI can achieve the best performance on two real-world datasets. Moreover, the visual interpretation results of the case study show that AutoDDI can effectively capture drug substructure for drug-drug interaction prediction.

The multivariate fusion transformation is ubiquitous in multivariate time series prediction (MTSP) problems. The previous multivariate fusion transformation fuses the feature of different variates at a time step, then projects them to a new feature space for effective feature representation. However, temporal dependency is the most fundamental property of time series. The previous manner fails to capture the temporal dependency of the feature, which is destroyed in the transformed feature matrix. Multivariate feature extraction based on the feature matrix with missing temporal dependency leads to the loss of predictive performance of MTSP. To address this problem, we propose the Temporal Dependency Priority for Multivariate Time Series Prediction (TemDep) method. Specifically, TemDep extracts feature temporal dependency of multivariate time series first and then considers multivariate feature fusion. Moreover, the low-dimensional and high-dimensional feature fusion manners are designed with the temporal dependency priority to fit different dimensional multivariate time series. The extensive experimental results of different datasets show that our proposed method can outperform all state-of-the-art baseline methods. It proves the significance of temporal dependency priority for MTSP.

To alleviate the over-smoothing problem caused by deep graph neural networks, decoupled graph neural networks (DGNNs) are proposed. DGNNs decouple the graph neural network into two atomic operations, the propagation (P) operation and the transformation (T) operation. Since manually designing the architecture of DGNNs is a time-consuming and expert-dependent process, the DF-GNAS method is designed, which can automatically construct the architecture of DGNNs with fixed propagation operation and deep layers. The propagation operation is a key process for DGNNs to aggregate graph structure information. However, DF-GNAS automatically designs DGNN architecture using fixed propagation operation for different graph structures will cause performance loss. Meanwhile, DF-GNAS designs deep DGNNs for graphs with simple distributions, which may lead to overfitting problems. To solve the above challenges, we propose the Decoupled Graph Neural Architecture Search with Variable Propagation Operation and Appropriate Depth (DGNAS-PD) method. In DGNAS-PD, we design a DGNN operation space with variable efficient propagation operations in order to better aggregate information on different graph structures. We build an effective genetic search strategy to adaptively design appropriate DGNN depths instead of deep DGNNs for the graph with simple distributions in DGNAS-PD. The experiments on five real-world graphs show that DGNAS-PD outperforms state-of-art baseline methods.

In the field of financial prediction, most studies focus on individual stocks or stock indices. Stock sectors are collections of stocks with similar characteristics and the indices of sectors have more stable trends and predictability compared to individual stocks. Additionally, stock sectors are subsets of stock indices, which implies that investment portfolios based on stock sectors have a greater potential to achieve excess returns. In this paper, we propose a new method, Time-aware Graph Structure Learning Network (TGSLN), to address the problem of stock sector ranking recommendation. In this model, we use an indicator called Relative Price Strength (RPS) to describe the ranking change trend of the sectors. To construct the inherent connection between sectors, we construct a multi-variable time series that consists of multi-scale RPS sequences and effective indicators filtered through the factor selector. We also build a stock sector relation graph based on authoritative stock sector classifications. Specially, we design a time-aware graph structure learner, which can mine the sector relations from time series, and enhance the initial graph through graph fusion. Our model outperforms state-of-the-art baselines in both A-share and NASDAQ markets.

In recent years, heterogeneous graph neural networks (HGNNs) have achieved excellent performance. The efficient HGNNs consist of meta-graphs and aggregation operations. Since manually designing meta-graph is an expert-dependent and time-consuming process, the performance of HGNNs is limited. To address this challenge, the differentiable meta-graph search has been proposed to obtain promising meta-graph automatically. However, the previous differentiable meta-graph search constructs the supernet without learnable aggregation operations, which limits the semantics extracting ability of HGNNs with automatically designed meta-graph for downstream tasks. To solve this problem, we propose the Meta-graph Search with Learnable Supernet for Heterogeneous Graph Neural Networks (MSLS). Specifically, to obtain better performance HGNNs, MSLS constructs a supernet with learnable aggregation operations based on the meta-graphs. MSLS adopts decoupling training to train the learnable supernet and obtains the optimal meta-graph with learnable aggregation operations using a constrained evolution strategy. Extensive experiments show that our method (MSLS) achieves the best performance in different tasks.

MOTIVATION: Understanding drug-response differences in cancer treatments is one of the most challenging aspects of personalized medicine. Recently, graph neural networks (GNNs) have become state-of-the-art methods in many graph representation learning scenarios in bioinformatics. However, building an optimal handcrafted GNN model for a particular drug sensitivity dataset requires manual design and fine-tuning of the hyperparameters for the GNN model, which is time-consuming and requires expert knowledge. RESULTS: In this work, we propose AutoCDRP, a novel framework for automated cancer drug-response predictor using GNNs. Our approach leverages surrogate modeling to efficiently search for the most effective GNN architecture. AutoCDRP uses a surrogate model to predict the performance of GNN architectures sampled from a search space, allowing it to select the optimal architecture based on evaluation performance. Hence, AutoCDRP can efficiently identify the optimal GNN architecture by exploring the performance of all GNN architectures in the search space. Through comprehensive experiments on two benchmark datasets, we demonstrate that the GNN architecture generated by AutoCDRP surpasses state-of-the-art designs. Notably, the optimal GNN architecture identified by AutoCDRP consistently outperforms the best baseline architecture from the first epoch, providing further evidence of its effectiveness. AVAILABILITY AND IMPLEMENTATION: https://github.com/BeObm/AutoCDRP.

Recently, graph neural architecture search (GNAS) frameworks have been successfully used to automatically design the optimal neural architectures for many problems such as node classification and graph classification. In the existing GNAS frameworks, the designed graph neural network (GNN) architectures learn the representation of homogenous graphs with one type of relationship connecting two nodes. However, multi-view graphs, where each view represents a type of relationship among nodes, are ubiquitous in the real world. The traditional GNAS frameworks learn the graph representation without considering the interactions between nodes and multiple relationships, so they fail to solve multi-view graph-based problems, such as multi-view graphs modelling the biomedical entity and relation extraction tasks. In this paper, we propose MVGNAS, a multi-view graph neural network automatic modelling framework for biomedical entity and relation extraction, to resolve this challenge. In MVGNAS, we propose an automatic multi-view representation learning to learn low-dimensional representations of nodes that capture multiple relationships in a multi-view graph, representing the first research work in literature to solve the problem of multi-view graph representation learning architecture search for biomedical entity and relation extraction tasks. The experimental results demonstrate that MVGNAS can achieve the best performance in biomedical entity and relation extraction tasks against the state-of-the-art baseline methods.

It is significant to comprehend the relationship between metabolic pathway and molecular pathway for synthesizing new molecules, for instance optimizing drug metabolization. In bioinformatics fields, multi-label prediction of metabolic pathways is a typical manner to understand this relationship. Graph neural networks (GNNs) have become an effective method to extract molecular structure's features for multi-label prediction of metabolic pathways. Though GNNs can effectively capture structural features from molecular structure graphs, building a well-performed GNN model for a given molecular structure data set requires the manual design of the GNN architecture and fine-tuning of the hyperparameters, which are time-consuming and rely on expert experience. To address the above challenge, we design an end-to-end automatic molecular structure representation learning framework named AutoMSR that can design the optimal GNN model based on a given molecular structure data set without manual intervention. We propose a multi-seed age evolution (MSAE) search algorithm to identify the optimal GNN architecture from the GNN architecture subspace. For a given molecular structure data set, AutoMSR first uses MSAE to search the GNN architecture, and then it adopts a tree-structured parzen estimator to obtain the best hyperparameters in the hyperparameters subspace. Finally, AutoMSR automatically constructs the optimal GNN model based on the best GNN architecture and hyperparameters to extract the molecular structure features for multi-label metabolic pathway prediction. We test the performance of AutoMSR on the real data set KEGG. The experiment results show that AutoMSR outperforms baseline methods on different multi-label classification evaluation metrics.

Graph classification has been widely used for knowledge discovery in numerous practical application scenarios, such as social networks and protein-protein interaction networks. Recently, Graph Neural Networks (GNNs), which generalize deep neural networks to graph-structured data, have drawn considerable attention and achieved state-of-the-art performance in graph classification. However, existing GNN models mainly focus on capturing the information of immediate or first-order neighboring nodes within a single layer. The graph substructure and substructure interaction, that play an important role in learning graph representations, are usually overlooked. In this paper, we propose a Substructure Assembling Graph Attention Network (SA-GAT) to extract graph features and improve the performance of graph classification. SA-GAT is able to fully explore higher-order substructure information hidden in graphs by a core module called Substructure Interaction Attention (SIA). Theoretically, we have also proved that SA-GAT satisfies the graph isomorphism theory of graph neural network design, which is that the network should map isomorphic graphs to the same representation and output the same prediction. Extensive experimental results on multiple real-world graph classification datasets demonstrate that the proposed SA-GAT outperforms the state-of-the-art methods including graph kernels and graph neural networks.

Glycans play an indispensable role in various bio-logical processes, such as cancer and autoimmune diseases. The function of glycan is closely determined by its structure. Due to the branch and nonlinear properties of glycans, previous research treats the glycans graph structure as a topological graph to represent glycans data effectively. Graph neural networks (GNNs) are an efficient graph mining method and have many applications in bioinformatics. Therefore, researchers have successfully used handcrafted GNNs to predict glycan immunogenicity. However, a GNN architecture contains many different components, and designing GNN architectures for specific graphs in the bioinformatics field is time-consuming and expert-dependent. To address this challenge, we propose an efficient automatic graph neural network method called EAGNN that can efficiently and automatically construct GNN architecture for glycan immunogenicity prediction. We design an effective graph attention pooling (GAP) search space. We use differential architecture search to efficiently create the optimal GNN architecture in the search space to build the GNN model for glycan immunogenicity prediction. We test EAGNN on the data set SugarBase based on the glycan immunogenicity prediction task. The experiment results show that EAGNN can work more superiorly than the baseline model and achieve the best performance.

Multivariate time series (MTS) prediction has been widely applied in a diverse range of fields including electricity, economics, finance, and traffic. Many studies have successfully constructed spatial and temporal convolution modules called spatio-temporal block (ST-block) for multivariate time series prediction. However, existing methods need to manually design the architecture topology based on ST-blocks, which is time-consuming and requires extensive expert experience. In this paper, we propose a Spatio-Temporal based Architecture Topology Search (STATS) method for multivariate time series prediction, which can automatically design the ST-block for multivariate time series prediction. In the STATS, we construct static and dynamic graphs topologically to integrate both static and dynamic information to obtain more expressive ST-graphs for the prediction task. Then, STATS explores the architecture topology with the differentiable search algorithm based on ST-blocks automatically. Extensive experiments on four commonly used multivariate time series prediction benchmark datasets demonstrate that our proposed method STATS can outperform the state-of-the-art baseline models.

Stock prediction is a challenging task due to the uncertainty of stock markets. Despite the success of previous works, most of them rely on the assumption that stock data follow the identically identical distribution while the existence of multiple trading patterns in stock market violates it, ignoring multiple patterns in stock market will inevitably lead to the performance decline, and the lack of pattern prior knowledge further hinders the learning of patterns. In this paper, we propose a novel training process Pattern Adaptive Training based on Optimal Transport (OT) to train a set of predictors specializing in diverse patterns while without any prior pattern knowledge and inconsistent assumption. Based on this process, we further mine the potential fitness rank among specialists and design the Pattern Adaptive Specialist Network (PASN) with proposed ranking based selector to choose appropriate specialist predictor for samples. Extensive experimental results show that our method achieves best IC and other metrics on real-world stock datasets.

Graph Neural Networks (GNNs) have drawn attention due to their excellent performance in fraud detection tasks, which reveal fraudsters by aggregating the features of their neighbors. However, some fraudsters typically tend to alleviate their suspiciousness by connecting with many benign ones. Besides, label-imbalanced neighborhood also deteriorates fraud detection accuracy. Such behaviors violate the homophily assumption and worsen the performance of GNN-based fraud detectors. In this paper, we propose a Dual-Augment Graph Neural Network (DAGNN) for fraud detection tasks. In DAGNN, we design a two-pathway framework including disparity augment (DA) pathway and similarity augment (SA) pathway. Accordingly, we devise two novel information aggregation strategies. One is to augment the disparity between target node and its heterogenous neighbors in original topology. The other is to augment its similarity to homogenous neighbors in a relatively label-balanced neighborhood. The experimental results compared with the state-of-the-art models on two real-world datasets demonstrate the superiority of the proposed DAGNN.

Multivariate time series (MTS) forecasting is a challenging task. In MTS forecasting, We need to consider both intra-series temporal correlations and inter-series spatial correlations simultaneously. However, existing methods capture spatial correlations from the local data of the time series, without taking the global historical information of time series into account. In addition, most methods base on graph neural network mining for the temporal correlations tend to the redundancy of information at adjacent time points in the time-series data, which introduces noise. In this paper, we propose a memory augmented graph learning network (MAGL), which captures the spatial correlations in terms of the global historical features of MTS. Specifically, we use a memory unit to learn from the local data of MTS. The memory unit records the global historical features of the time series, which is used to mine the spatial correlations. We also design a temporal feature distiller to reduce the noise in extracting temporal features. We extensively evaluate our model on four real-world datasets, comparing with several state-of-the-art methods. The experimental results show MAGL outperforms the state-of-the-art baseline methods on several datasets.

Stock prediction is a hot topic of research in the field of Fintech. Stocks are not independent of each other. But, existing studies ignore the relations between stocks or simply utilize stock spatial dependencies based on predefined graphs. The predefined graphs may miss some potential relations and are not suitable for depicting the dynamic relations between stocks. To address both problems for stock recommendation, we propose the static-dynamic graph neural network (SDGNN). In SDGNN, a graph learning module is designed to learn the static and dynamic graphs. This module employs a data-driven approach which makes the model uncover potential relations between stocks. Furthermore, to enable each stock node to obtain more information from more important neighbor nodes, we develop a graph interaction module. It implements interactions between the static graph and the dynamic graph. Experiments demonstrate that our proposed model significantly outperforms the current state-of-the-art methods on two real-world stock datasets.

In recent years, cancer patients survival prediction holds important significance for worldwide health problems, and has gained many researchers attention in medical information communities. Cancer patients survival prediction can be seen the classification work which is a meaningful and challenging task. Nevertheless, research in this field is still limited. In this work, we design a novel Multimodal Graph Neural Network (MGNN)framework for predicting cancer survival, which explores the features of real-world multimodal data such as gene expression, copy number alteration and clinical data in a unified framework. Specifically, we first construct the bipartite graphs between patients and multimodal data to explore the inherent relation. Subsequently, the embedding of each patient on different bipartite graphs is obtained with graph neural network. Finally, a multimodal fusion neural layer is proposed to fuse the medical features from different modality data. Comprehensive experiments have been conducted on real-world datasets, which demonstrate the superiority of our modal with significant improvements against state-of-the-arts. Furthermore, the proposed MGNN is validated to be more robust on other four cancer datasets.

The stock market investors aim at maximizing their investment returns. Stock recommendation task is to recommend stocks with higher return ratios for the investors. Most stock prediction methods study the historical sequence patterns to predict stock trend or price in the near future. In fact, the future price of a stock is correlated not only with its historical price, but also with other stocks. In this article, we take into account the relationships between stocks (corporations) by stock relation graph. Furthermore, we propose a Time-aware Relational Attention Network (TRAN) for graph-based stock recommendation according to return ratio ranking. In TRAN, the time-aware relational attention mechanism is designed to capture time-varying correlation strengths between stocks by the interaction of historical sequences and stock description documents. With the dynamic strengths, the nodes of the stock relation graph aggregate the features of neighbor stock nodes by graph convolution operation. For a given group of stocks, the proposed TRAN model can output the ranking results of stocks according to their return ratios. The experimental results on several real-world datasets demonstrate the effectiveness of our TRAN for stock recommendation.

With the development of e-commerce, fraud behaviors have been becoming one of the biggest threats to the e-commerce business. Fraud behaviors seriously damage the ranking system of e-commerce platforms and adversely influence the shopping experience of users. It is of great practical value to detect fraud behaviors on e-commerce platforms. However, the task is non-trivial, since the adversarial action taken by fraudsters. Existing fraud detection systems used in the e-commerce industry easily suffer from performance decay and can not adapt to the upgrade of fraud patterns, as they take already known fraud behaviors as supervision information to detect other suspicious behaviors. In this article, we propose a competitive graph neural networks (CGNN)-based fraud detection system (eFraudCom) to detect fraud behaviors at one of the largest e-commerce platforms, “Taobao” 1 . In the eFraudCom system, (1) the competitive graph neural networks (CGNN) as the core part of eFraudCom can classify behaviors of users directly by modeling the distributions of normal and fraud behaviors separately; (2) some normal behaviors will be utilized as weak supervision information to guide the CGNN to build the profile for normal behaviors that are more stable than fraud behaviors. The algorithm dependency on fraud behaviors will be eliminated, which enables eFraudCom to detect fraud behaviors in presence of the new fraud patterns; (3) the mutual information regularization term can maximize the separability between normal and fraud behaviors to further improve CGNN. eFraudCom is implemented into a prototype system and the performance of the system is evaluated by extensive experiments. The experiments on two Taobao and two public datasets demonstrate that the proposed deep framework CGNN is superior to other baselines in detecting fraud behaviors. A case study on Taobao datasets verifies that CGNN is still robust when the fraud patterns have been upgraded.

Graph neural networks (GNNs) have received much attention as GNNs have recently been successfully applied on non-euclidean data. However, artificially designed graph neural networks often fail to get satisfactory model performance for a given graph data. Graph neural architecture search effectively constructs the GNNs that achieve the expected model performance with the rise of automatic machine learning. The challenge is efficiently and automatically getting the optimal GNN architecture in a vast search space. Existing search methods serially evaluate the GNN architectures, severely limiting system efficiency. To solve these problems, we develop an Auto matic G raph N eural A rchitecture S earch framework (Auto-GNAS) with parallel estimation to implement an automatic graph neural search process that requires almost no manual intervention. In Auto-GNAS, we design the search algorithm with multiple genetic searchers. Each searcher can simultaneously use evaluation feedback information, information entropy, and search results from other searchers based on sharing mechanism to improve the search efficiency. As far as we know, this is the first work using parallel computing to improve the system efficiency of graph neural architecture search. According to the experiment on the real datasets, Auto-GNAS obtain competitive model performance and better search efficiency than other search algorithms. Since the parallel estimation ability of Auto-GNAS is independent of search algorithms, we expand different search algorithms based on Auto-GNAS for scalability experiments. The results show that Auto-GNAS with varying search algorithms can achieve nearly linear acceleration with the increase of computing resources.

Understanding the relationship between molecular structure and metabolic pathway classes is significant for optimizing drug metabolization. In bioinformatics, graph neural networks (GNNs) can effectively capture structural and semantic features for molecular representation. Graph neural networks have become an essential method to encode molecular structures for multi-label prediction of metabolic pathways. However, building a GNN model for a given molecular structure dataset requires the manual design of GNN structure and fine-tuning of the hyperparameters for the GNN model, which is time-consuming and relies on expert experience. In this paper, we design an automatic end-to-end molecular structure representation learning framework named Auto-MSR that can design a GNN model for molecular structure encoding with little manual intervention. For a given compound molecule structure dataset, Auto-MSR first uses an efficient pa rallel GNN structure search algorithm to identify the optimal GNN structure from the GNN structure subspace. Then, it adopts a tree-structured parzen estimator approach to obtain the best hyperparameters of the GNN model in the hyperparameters subspace. We test AutoMSR on the dataset KEGG based on the multi-label metabolic pathway prediction task. The comparing results show that AutoMSR outperforms state-of-art manual graph neural networks on performance.

The interaction of multiple drugs could lead to serious events, which causes injuries and huge medical costs. Accurate prediction of drug-drug interaction (DDI) events can help clinicians make effective decisions and establish appropriate therapy programs. Recently, many AI-based techniques have been proposed for predicting DDI associated events. However, most existing methods pay less attention to the potential correlations between DDI events and other multimodal data such as targets and enzymes. To address this problem, we propose a Multimodal Deep Neural Network (MDNN) for DDI events prediction. In MDNN, we design a two-pathway framework including drug knowledge graph (DKG) based pathway and heterogeneous feature (HF) based pathway to obtain drug multimodal representations. Finally, a multimodal fusion neural layer is designed to explore the complementary among the drug multimodal representations. We conduct extensive experiments on real-world dataset. The results show that MDNN can accurately predict DDI events and outperform the state-of-the-art models.

Graph neural architecture search has received a lot of attention as Graph Neural Networks (GNNs) has been successfully applied on the non-Euclidean data recently. However, exploring all possible GNNs architectures in the huge search space is too time-consuming or impossible for big graph data. In this paper, we propose a parallel graph architecture search (GraphPAS) framework for graph neural networks. In GraphPAS, we explore the search space in parallel by designing a sharing-based evolution learning, which can improve the search efficiency without losing the accuracy. Additionally, architecture information entropy is adopted dynamically for mutation selection probability, which can reduce space exploration. The experimental result shows that GraphPAS outperforms state-of-art models with efficiency and accuracy simultaneously.

Knowledge Graphs (KGs) are widely used in various applications of information retrieval. Despite the large scale of KGs, they are still facing incomplete problems. Conventional approaches on Knowledge Graph Completion (KGC) require a large number of training instances for each relation. However, long-tail relations which only have a few related triples are ubiquitous in KGs. Therefore, it is very difficult to complete the long-tail relations. In this paper, we propose a meta pattern learning framework (MetaP) to predict new facts of relations under a challenging setting where there is only one reference for each relation. Patterns in data are representative regularities to classify data. Triples in KGs also conform to relation-specific patterns which can be used to measure the validity of triples. Our model extracts the patterns effectively through a convolutional pattern learner and measures the validity of triples accurately by matching query patterns with reference patterns. Extensive experiments demonstrate the effectiveness of our method. Besides, we build a few-shot KGC dataset of COVID-19 to assist the research process of the new coronavirus.

Research of Protein-Protein Interaction (PPI) Network Alignment is playing an important role in understanding the crucial underlying biological knowledge such as functionally homologous proteins and conserved evolutionary pathways across different species. Existing methods of PPI network alignment often try to improve the coverage ratio of the alignment result by aligning all proteins from different species. However, there is a fundamental biological premise that needs to be considered carefully: not every protein in a species can, nor should, find its homologous proteins in other species. In this work, we propose a novel alignment method to map only those proteins with the most similarity throughout the PPI networks of multiple species. For the similarity features of the protein in the networks, we integrate both topological features with biological characteristics to provide enhanced supports for the alignment procedures. For topological features, we apply a representation learning method on the networks and generate a low dimensional vector embedding with its surrounding structural features for each protein. The topological similarity of proteins from different PPI networks can thus be transferred as the similarity of their corresponding vector representations, which provides a new way to comprehensively quantify the topological similarities between proteins. We also propose a new measure for the topological evaluation of the alignment results which better uncover the structural quality of the alignment across multiple networks. Both biological and topological evaluations on the alignment results of real datasets demonstrate our approach is promising and preferable against previous multiple alignment methods.

To identify and explore the similarities between diseases is of great significance for u nderstanding t he pathogenic mechanisms of emerging complex diseases. Some methods try to measure the similarity of diseases through deep learning models. However, the insufficient number of labelled similar disease pairs cannot support the optimal training of the models. In this paper, we propose a Multi-Task Graph Neural Network (MTGNN) framework to retrieve similar diseases by few-shot learning. To deal with the problem of insufficient number o f labelled similar disease pairs, we design double tasks to optimize the graph neural network for disease similarity task (lack of labelled training data) by introducing link prediction task (sufficient labelled training data). The similarity between diseases can then be obtained by measuring the distance between disease embeddings in high-dimensional space learning from the double tasks. The experiment results illustrate the overall effectiveness by comparing with prior methods on few labeled training dataset.

In distributed storage, dynamic loads usually cause a huge burden such as hot-spot and unblance region problems. In this paper, we focus on the problems of dynamic unbalance loads in distributed database HBase. Firstly, we propose a new intelligent load balance framework to support the collection and analysis of dynamic load information. Then, two efficient load balance strategies are proposed to solve hot-spot problem and unbalance region problem. Finally, we evaluate the strategies on HBase using real-world big data on distributed clusters. Compared to the existing balance strategies, the proposed load balance strategies can make more effective use of the resources and reduce the response time for large dynamic loads.

Recommending stock with the highest return ratio is always a challenging problem in the field of financial technology. In this paper, we propose a time-aware graph relational attention network (TRAN) for stock recommendation based on return ratio ranking. In TRAN, time-aware relational attention mechanism is the key unit to capture time-varying correlation strength between stocks by the interaction of historical sequences and stock description documents. With the dynamic strength, the nodes of the stock relation graph aggregate the features of neighbor stock nodes by graph convolution operation. For a given group of stocks, our model can output the ranking results of stocks according to their return ratios. The experimental results on several real-world datasets demonstrate the effectiveness of our TRAN for stock recommendation.

Predicting the survival of cancer patients holds significant meaning for public health, and has attracted increasing attention in medical information communities. In this study, we propose a novel framework for cancer survival prediction named Multimodal Graph Neural Network (MGNN), which explores the features of real-world multimodual data such as gene expression, copy number alteration and clinical data in a unified framework. In order to explore the inherent relation, we first construct the bipartite graphs between patients and multimodal data. Subsequently, graph neural network is adopted to obtain the embedding of each patient on different bipartite graphs. Finally, a multimodal fusion neural layer is designed to fuse the features from different modal data. The output of our method is the classification of short term survival or long term survival for each patient. Experimental results on one breast cancer dataset demonstrate that MGNN outperforms all baselines. Furthermore, we test the trained model on lung cancer dataset, and the experimental results verify the strong robust by comparing with state-of-the-art methods.

Quantifying the similarities between diseases is now playing an important role in biology and medicine, which provides reliable reference information in finding similar diseases. Most of the previous methods for similarity calculation between diseases either use a single-source data or do not fully utilize multi-sources data. In this study, we propose an approach to measure disease similarity by utilizing multiple heterogeneous disease information networks. Firstly, multiple disease-related data sources are formulated as heterogeneous disease information networks which include various types of objects such as disease, pathway, and chemicals. Then, the corresponding subgraphs of these heterogeneous disease information networks are obtained by filtering vertices. Topological scores and semantics scores are calculated in these heterogenous subgraphs using Dynamic Time Warping (DTW) algorithm and meta path method respectively. In this way, we transform multiple heterogeneous disease networks to a homogeneous disease network with different weights on the edges. Finally, the disease nodes can be embedded according to the weights and the similarity between diseases can then be calculated using these n-dimensional vectors. Experiments based on benchmark set fully demonstrate the effectiveness of our method in measuring the similarity of diseases through multi-sources data.

Disconnected graphs are very common in the real world. However, most existing methods for graph similarity focus on connected graph. In this paper, we propose an effective approach for measuring the similarity of disconnected graphs. By embedding connected subgraphs with graph kernel, we obtain the feature vectors in low dimensional space. Then, we match the subgraphs and weigh the similarity of matched subgraphs. Finally, an intuitive example shows the feasibility of the method.

Graph convolutional network (GCN) is a promising approach that has recently been used to resolve knowledge graph alignment. In this paper, we propose a new method to entity alignment for cross-lingual knowledge graph. In the method, we design a scheme of attribute embedding for GCN training. Furthermore, GCN model utilizes the attribute embedding and structure embedding to abstract graph features simultaneously. Our preliminary experiments show that the proposed method outperforms the state-of-the-art GCN-based method.

Subgraph matching query is to find out the sub-graphs of data graph G which match a given query graph Q. Traditional methods can not deal with big data graphs due to their high computational complex. In this paper, we propose a distributed top-k subgraph search method over big graphs. The proposed method is designed at the level of single vertex and all vertices obtain their matching state separately without requiring global graph information. Therefore, it can be easily deployed in distributed platform like Hadoop. The evaluations of running time, number of messages and supersteps show the efficiency and scalability of the proposed method.

Aspect-level opinion mining aims to find and aggregate opinions on opinion targets. Previous work has demonstrated that precise modeling of opinion targets within the surrounding context can improve performances. However, how to effectively and efficiently learn hidden word semantics and better represent targets and the context still needs to be further studied. In this paper, we propose and compare two interactive attention neural networks for aspect-level opinion mining, one employs two bi-directional Long-Short-Term-Memory (BLSTM) and the other employs two Convolutional Neural Networks (CNN). Both frameworks learn opinion targets and the context respectively, followed by an attention mechanism that integrates hidden states learned from both the targets and context. We compare our model with state-of-the-art baselines on two SemEval 2014 datasets1. Experiment results show that our models obtain competitive performances against the baselines on both datasets. Our work contributes to the improvement of state-of-the-art aspect-level opinion mining methods and offers a new approach to support human decision-making process based on opinion mining results. The quantitative and qualitative comparisons in our work aim to give basic guidance for neural network selection in similar tasks.

BACKGROUND: In bioinformatics, network alignment algorithms have been applied to protein-protein interaction (PPI) networks to discover evolutionary conserved substructures at the system level. However, most previous methods aim to maximize the similarity of aligned proteins in pairwise networks, while concerning little about the feature of connectivity in these substructures, such as the protein complexes. RESULTS: In this paper, we identify the problem of finding conserved protein complexes, which requires the aligned proteins in a PPI network to form a connected subnetwork. By taking the feature of connectivity into consideration, we propose ConnectedAlign, an efficient method to find conserved protein complexes from multiple PPI networks. The proposed method improves the coverage significantly without compromising of the consistency in the aligned results. In this way, the knowledge of protein complexes in well-studied species can be extended to that of poor-studied species. CONCLUSIONS: We conducted extensive experiments on real PPI networks of four species, including human, yeast, fruit fly and worm. The experimental results demonstrate dominant benefits of the proposed method in finding protein complexes across multiple species.

One of the important sources for big data is the datasets collected by wireless sensor networks. However, errors in sensor data could result in serious damages in industrial applications. Therefore, error detection plays a crucial role in industrial wireless sensor networks (IWSNs). Existing approaches of error detection are generally threshold-based, which rely on a predetermined threshold to judge whether a reading is erroneous. The threshold-based approaches, however, often fail to balance between detection accuracy and false alarm rate. It is thus difficult, if not impossible, to obtain a proper threshold for various errors in real-world applications. Motivated by this consideration, we propose a novel threshold-free error detection approach for IWSNs. By taking the advantage of the spatiotemporal correlations between sensor readings, we present the model to characterize the relationship between sensor pairs and, thus, construct a correlation graph for IWSN. In the correlation graph, the states of nodes, i.e., the states of sensor readings, are accurately exploited without requiring any threshold. Through the experiments on both real sensors and simulations, we demonstrate that the proposed approach is able to significantly improve the detection accuracy while largely reducing the false alarm rate.

Botnet, a group of coordinated bots, is becoming the main platform of malicious Internet activities like DDOS, click fraud, web scraping, spam/rumor distribution, etc. This paper focuses on design and experiment of a new approach for botnet detection from streaming web server logs, motivated by its wide applicability, real-time protection capability, ease of use and better security of sensitive data. Our algorithm is inspired by a Principal Component Analysis (PCA) to capture correlation in data, and we are first to recognize and adapt Lanczos method to improve the time complexity of PCA-based botnet detection from cubic to sub-cubic, which enables us to more accurately and sensitively detect botnets with sliding time windows rather than fixed time windows. We contribute a generalized online correlation matrix update formula, and a new termination condition for Lanczos iteration for our purpose based on error bound and non-decreasing eigenvalues of symmetric matrices. On our dataset of an ecommerce website logs, experiments show the time cost of Lanczos method with different time windows are consistently only 20% to 25% of PCA.

Network alignment is becoming an active topic in network data analysis. Despite extensive research, we realize that efficient use of topological and attribute information for large attributed network alignment has not been sufficiently addressed in previous studies. In this paper, based on Stochastic Block Model (SBM) and Dirichlet-multinomial, we propose "divide-and-conquer" models CAlign that jointly consider network alignment, community discovery and community alignment in one framework for large networks with node attributes, in an effort to reduce both the computation time and memory usage while achieving better or competitive performance. It is provable that the algorithms derived from our model have sub-quadratic time complexity and linear space complexity on a network with small densification power, which is true for most real-world networks. Experiments show CAlign is superior to two recent state-of-art models in terms of accuracy, time and memory on large networks, and CAlign is capable of handling millions of nodes on a modern desktop machine.

In this paper, we propose a novel k-anonymization scheme to counter deanonymization queries on social networks. With this scheme, all entities are protected by k-anonymization, which means the attackers cannot re-identify a target with confidence higher than 1/k. The proposed scheme minimizes the modification on original networks, and accordingly maximizes the utility preservation of published data while achieving k-anonymization privacy protection. Extensive experiments on real data sets demonstrate the effectiveness of the proposed scheme, where the efficacy of the k-anonymized networks is verified with the distributions of pagerank, betweenness, and their Kolmogorov-Smirnov (K-S) test.

Alignment among protein-protein interaction (PPI) networks largely benefits our understanding in biological researches as it contributes greatly to the uncovering of important information, such as evolutionary conserved pathways, protein complexes and functional orthologs. Global alignment of multiple PPI networks aims at clustering functionally conserved proteins throughout different species, where most traditional methods attempt to achieve results with high overall coverage and consistency. However little attention was paid on the deeper level criteria of k-coverage and especially the k-consistency that we additionally conducted for evaluations, where k indicates the number of species that the proteins in a cluster belong to. In this paper, we propose a novel approach for global alignment of multiple PPI networks which achieved high k-coverage and k-consistency simultaneously in addition to conventional criteria. The evaluations demonstrate the preferable comprehensive strength of our approach against other three state-of-the-art methods on six datasets composed of various numbers of PPI networks from five species.

In the real world, many problems on massive graphs can be mapped to an underlying critical problem of discovering top-k subgraphs. For massive graphs, subgraph queries may have enormous number of matches, and so it is inefficient to compute all matches when only top-k matches are desired. Meanwhile, parallel algorithm is urgent for the scalability of massive graph computing. In this paper, we address the challenges of top-k subgraph query in massive graph. Firstly, we present a new graph matching notion: “approximate graph simulation”. With approximate graph simulation, top-k subgraph query can be customized by appointing a weighted query graph, which provides good flexibility for different application scenarios. Secondly, we propose a parallel top-k subgraph query algorithm at the level of vertex. With such algorithm, each vertex in massive graph obtains its matching state separately without requiring global graph information. In the algorithm, we also design a filter mechanism to speed up the the computation and a aggregation mechanism to obtain top-k vertices for query focus. Using real-life datasets, we experimentally verify that our approach of parallel top-k subgraph query are efficient.

Graph pattern matching is becoming important for a variety of emerging applications such as social network analysis. Graph pattern matching is traditionally defined in terms of subgraph isomorphism or graph simulation. These notions, however, often impose too strong constraint to identify meaningful matches. In this paper, we propose a new graph pattern matching based on a notion of relaxation simulation, which extends graph simulation by allowing partially absent vertices. We show that relaxation simulation is able to find significant matches which traditional approaches of graph pattern matching fail to catch. We propose two parallel algorithms to apply relaxation simulation to massive graph since the graph in practice is considerably large. The algorithms are based on Bulk Synchronous Parallel model and can be easily deployed on cloud computing platforms. Finally, we experimentally verify the effectiveness and efficiency of these algorithms, using real-life data and synthesis data. The results suggest that relaxation simulation is a promising framework for real-life massive graph analysis.

SUMMARY Supported by the IEEE 802.15.4 standard, embedded sensor networks have become popular and been widely deployed in recent years. The IEEE 802.15.4 medium access control (MAC) protocol is uniquely designed to meet the desirable requirements of the low end‐to‐end delay, low packet loss, and low power consumption in the low rate wireless personal areas networks (LR‐WPANs). This paper develops an analytical model to quantify the key performance metrics of the MAC protocol in LR‐WPANs with bursty ON–OFF traffic. This study fills the gap in the literature by removing the assumptions of saturated traffic or nonbursty unsaturated traffic conditions, which are unable to capture the characteristics of bursty multimedia traffic in sensor networks. This analytical model can be used to derive the QoS performance metrics in terms of throughput and total delay. The accuracy of the model is verified through NS‐2 ( http://www.isi.edu/nsnam/ns/ ) simulation experiments. This model is adopted to investigate the performance of the MAC protocol in LR‐WPANs under various traffic patterns, different loads, and various numbers of stations. Numerical results show that the traffic patterns and traffic burstiness have a significant impact on the delay performance of LR‐WPANs. Copyright © 2012 John Wiley & Sons, Ltd.

Concurrent trace is an emerging challenge when debugging multicore systems. In concurrent trace, trace buffer becomes a bottleneck since all trace sources try to access it simultaneously. In addition, the on-chip interconnection fabric is extremely high hardware cost for the distributed trace signals. In this paper, we propose a clustering-based scheme which implements concurrent trace for debugging Network-on-Chip (NoC) based multicore systems. In the proposed scheme, a unified communication framework eliminates the requirement for interconnection fabric which is only used during debugging. With clustering scheme, multiple concurrent trace sources can access distributed trace buffer via NoC under bandwidth constraint. We evaluate the proposed scheme using Booksim and the results show the effectiveness of the proposed scheme.

In the practical applications of wireless sensor networks, it is almost inevitable that some sensors become faulty during running. The faulty measurement values will cause a burden to the limited energy of sensor networks. Furthermore, wrong judgement might be deduced because of the faulty data when they reach base station. Therefore, proper fault detection especially for long-term large-scale systems is crucial and challenging. Motivated by the requirement of practical applications, we propose a distributed fault detection approach for wireless senor networks. Firstly, Hidden Markov Random Field (HMRF) model is introduced to characterize the correlations between measurement values and real values of sensor nodes. Then, an errors-in-variables estimation method is presented to obtain the parameters in the HMRF model. Finally, a distributed fault detection algorithm is proposed based on the HMRF model. Both theoretical analysis and simulation results show that the proposed HMRF-based fault detection achieves considerable high detection accuracy and low false alarm rate simultaneously.

A critical concern for post-silicon debug is the need to control the chip at clock cycle level. In a single clock chip, run-stop control can be implemented by gating the clock signal using a stop signal. However, data invalidation might occur when it comes to multiple-clock chips. In this paper, we analyze the possible data invalidation, including data repetition and data loss, when stopping and resuming a multiple-clock chip. Furthermore, we propose an efficient solution to eliminate data repetition and data loss. Theoretical analysis and simulation experiments are both conducted for the proposed solution. We implement the proposed Design-for-Debug (DfD) circuit with SMIC 0.18μm technology and simulate the data transfer across clock domains using SPICE tool. The results show that both data repetition and data loss can be avoided with the proposed solution, even if metastability occurs.

The IEEE 802.15.4 standard defines physical layer and Medium Access Control (MAC) layer protocols for the Low Rate Wireless Personal Areas Network (LR-WPAN). The analytical models of 802.15.4 MAC have been primarily developed under the assumptions of the ideal channels or uniform error channels which fail to capture the characteristics of bursty and correlated channel errors in the practical wireless network environment. In this paper, we propose an analytical model for 802.15.4 MAC in LR-WPAN in the presence of bursty error channels. This model can be adopted to obtain the Quality-of-Service (QoS) performance metrics in terms of throughput, service time, and total delay. Utilizing the analytical model, we investigate the QoS performance of 802.15.4 MAC under various traffic loads, backoff parameters, numbers of stations, and channel conditions.

Bugs are tending to be unavoidable in the design of complex integrated circuits. It is imperative to identify the bugs as soon as possible by post-silicon debug. The main challenge for post-silicon debug is the observability of the internal signals. This paper exploits the fact that it is not necessary to observe the error free states. Then we introduce "suspect window" and present a method for determining its boundary. Based on suspect window, we propose a debug approach to achieve high observability by reusing scan chain. Since scan dumps take place only in suspect window, debug time is greatly reduced. Experiment results demonstrate the effectiveness of the proposed approach.

This paper focuses on how to use class-modular single-hidden-layer perceptrons (MLPs) with sigmoid activation functions (SAFs) to solve the multi-class learning problems, and pays special attention to the unbalanced data sets. Our solutions are as follows. (A) An n-class learning problem first decomposes into n two-class problems (B) A single-output MLP is responsible for solving a two-class problem, separating its represented class with all the other classes, and trained only by the samples from the represented class and some neighboring ones. (C) The samples from the minority classes or in the thin regions are virtually reinforced (D)The generalization region of an MLP is localized. The proposed method is verified effective by the experimental result of letter recognition.