The growing demands of modern applications, such as AI, graph computing, and big data processing, are driving the increase in algorithmic scale and computational workload.As a result, Multi-PEA CGRA has been a popular choice because of its high computing power.However, such kinds of Architecture are confronted with control problem due to the large amount of PEA required management on Architecture.To address this challenge, this paper propose a built-in control design(Control Element, CE) for multi-PEA CGRA to improve computing efficiency.In this paper, this paper has compared the execution time, and power consumption with and without CE.Experiments demonstrate that our CE can reduce 97.3% execution time, in which the proportion of PEA work time is 76.2% and at least 79.7% power consumption.

Graph Neural Networks (GNNs) conventionally operate under the assumption that node attributes are entirely observable. Their performance notably deteriorates when confronted with incomplete graphs due to the inherent message-passing mechanisms. Current solutions either employ classic imputation techniques or adapt GNNs to tolerate missed attributes. However, their ability to generalize is impeded especially when dealing with high rates of missing attributes. To address this, we harness the representations of the essential views on graphs, attributes and structures, into a common shared latent space, ensuring robust tolerance even at high missing rates. Our proposed neural model, named ASD-VAE, parameterizes such space via a coupled-and-decoupled learning procedure, reminiscent of brain cognitive processes and multimodal fusion. Initially, ASD-VAE separately encodes attributes and structures, generating representations for each view. A shared latent space is then learned by maximizing the likelihood of the joint distribution of different view representations through coupling. Then, the shared latent space is decoupled into separate views, and the reconstruction loss of each view is calculated. Finally, the missing values of attributes are imputed from this learned latent space. In this way, the model offers enhanced resilience against skewed and biased distributions typified by missing information and subsequently brings benefits to downstream graph machine-learning tasks. Extensive experiments conducted on four typical real-world incomplete graph datasets demonstrate the superior performance of ASD-VAE against the state-of-the-art

Though graph neural networks (GNNs)-based fraud detectors have received remarkable success in identifying fraudulent activities, few of them pay equal attention to models' performance and explainability. In this paper, we attempt to achieve high performance for graph-based fraud detection while considering model explainability. We propose NGS (Neural meta-Graph Search), in which the message passing process of a GNN is formalized as a meta-graph, and a differentiable neural architecture search is devised to determine the optimized message passing graph structure. We further enhance the model by aggregating multiple searched meta-graphs to make the final prediction. Experimental results on two real-world datasets demonstrate that NGS outperforms state-of-the-art baselines. In addition, the searched meta-graphs concisely describe the information used for prediction and produce reasonable explanations.

Graph-based fraud detection approaches have escalated lots of attention recently due to the abundant relational information of graph-structured data, which may be beneficial for the detection of fraudsters. However, the GNN-based algorithms could fare poorly when the label distribution of nodes is heavily skewed, and it is common in sensitive areas such as financial fraud, etc. To remedy the class imbalance problem of graph-based fraud detection, we propose a Pick and Choose Graph Neural Network (PC-GNN for short) for imbalanced supervised learning on graphs. First, nodes and edges are picked with a devised label-balanced sampler to construct sub-graphs for mini-batch training. Next, for each node in the sub-graph, the neighbor candidates are chosen by a proposed neighborhood sampler. Finally, information from the selected neighbors and different relations are aggregated to obtain the final representation of a target node. Experiments on both benchmark and real-world graph-based fraud detection tasks demonstrate that PC-GNN apparently outperforms state-of-the-art baselines.