Accurately detecting super host that establishes connections to a large number of distinct peers is significant for mitigating web attacks and ensuring high quality of web service. Existing sketch-based approaches estimate the number of distinct connections called flow cardinality according to full IP addresses, while ignoring the fact that a malicious or victim super host often communicates with hosts within the same subnet, resulting in high false positive rates and low accuracy. Though hierarchical-structure based approaches could capture flow cardinality in subnet, they inherently suffer from high memory usage. To address these limitations, we propose SegSketch, a segmented cardinality estimation approach that employs a lightweight halved-segment hashing strategy to infer common prefix lengths of IP addresses, and estimates cardinality within subnet to enhance detection accuracy under constrained memory size. Experiments driven by real-world traces demonstrate that, SegSketch improves F1-Score by up to 8.04× compared to state-of-the-art solutions, particularly under small memory budgets.

Distributed deep learning has been widely deployed in data centers to provide various services such as image classification and speech recognition. To reduce the training time, Top-k compression has become one of the most popular solutions used to shrink the data volume of gradients. Nevertheless, we observe that existing Top-k compression solutions are inefficient when used for large-scale distributed training due to gradient build-up, missing of Top-k gradients, and high compression overhead at the end hosts. To address these problems, we propose SwitchTop-k, which improves the accuracy of selecting Top-k values while ensuring a high compression rate and zero compression overhead. Specifically, SwitchTop-k offloads the Top-k compression from the end hosts to the programmable switches, thus alleviating the gradient build-up and compression overhead. Meanwhile, we propose a sketch-based solution to achieve high accuracy in selecting global Top-k gradients. We also co-design switch logic and end host logic to improve communication efficiency of uncompressed traffic. Finally, we implement SwitchTop-k on Intel Tofino switches and integrate it with Pytorch. The test results show that SwitchTop-k reduces iteration time by up to 91% compared with existing compression algorithms.

Self-supervised learning on graph neural networks is receiving increasing attention due to the difficulty of obtaining graph labels in many real applications. Graph contrastive learning (GCL), a recently popular method for self-supervised learning on graphs, has achieved great success in many tasks. The key to the effectiveness of GCL is the construction of suitable contrasting pairs to capture important attributes of the data through the data augmentation modules. However, most of the existing approaches fail to fully consider both data diversity and the semantic consistency when conducting data augmentation. To fill this gap, we propose an augmentation diversity and semantic consistency balanced graph contrastive learning model (ADSCB for short), which enhances the representation ability of the CL model through richer contrasting objectives. In particular, we first introduce a semantic consistency module to extract the subgraph from the original graph through optimizing a carefully designed semantic consistency loss. Then, we introduce an augmentation diversity module and perform data augmentation and cross-scale mix-up operations on the original graph and the extracted semantic preserved subgraph to generate more diverse contrasting pairs. With the above two modules, our model ultimately achieves two contrasting objectives: diversity contrasting and semantic contrasting. The tradeoff between these two contrasting objectives allows our model to benefit from both the augmentation diversity and the semantic consistency. We evaluate ADSCB for graph classification in unsupervised, semi-supervised, and transfer learning settings using standard graph contrastive learning benchmarks. The results demonstrate the superiority of our method against several state-of-the-art baselines.

With the proliferation of mobile sensing techniques, huge amounts of time series data are generated and accumulated in various domains, fueling plenty of real-world applications. In this setting, time series anomaly detection is practically important. It endeavors to identify deviant samples from the normal sample distribution in time series. Existing approaches generally assume that all the time series is available at a central location. However, we are witnessing the decentralized collection of time series due to the deployment of various edge devices. To bridge the gap between the decentralized time series data and the centralized anomaly detection algorithms, we propose a Parameter-efficient Federated Anomaly Detection framework named PeFAD with the increasing privacy concerns. PeFAD for the first time employs the pre-trained language model (PLM) as the body of the client's local model, which can benefit from its cross-modality knowledge transfer capability. To reduce the communication overhead and local model adaptation cost, we propose a parameter-efficient federated training module such that clients only need to fine-tune small-scale parameters and transmit them to the server for update. PeFAD utilizes a novel anomaly-driven mask selection strategy to mitigate the impact of neglected anomalies during training. A knowledge distillation operation on a synthetic privacy-preserving dataset that is shared by all the clients is also proposed to address the data heterogeneity issue across clients. We conduct extensive evaluations on four real datasets, where PeFAD outperforms existing state-of-the-art baselines by up to 28.74%.

Inferring the fine-grained urban flows based on the coarse-grained flow observations is practically important to many smart city-related applications. However, the collected human/vehicle trajectory flows are usually rather unreliable, may contain various noise and sometimes are incomplete, thus posing great challenges to existing approaches. In this paper, we present a pioneering study on robust fine-grained urban flow inference with noisy and incomplete urban flow observations, and propose a denoising diffusion model named DiffUFlow to effectively address it. Specifically, we propose an improved reverse diffusion strategy. A spatial-temporal feature extraction network called STFormer and a semantic features extraction network called ELFetcher are also proposed. Then, we overlay the spatial-temporal feature map extracted by STFormer onto the coarse-grained flow map, serving as a conditional guidance for the reverse diffusion process. We further integrate the semantic features extracted by ELFetcher to cross-attention layers, enabling the comprehensive consideration of semantic information encompassing the entirety of urban data in fine-grained inference. Extensive experiments on two large real-world datasets validate the effectiveness of our method compared with the state-of-the-art baselines.

Graph contrastive learning (GCL) has attracted rising research attention recently due to its effectiveness in self- supervised graph learning. A key step of GCL is to conduct data augmentation, based on which self-supervised learning is performed through the contrast between two augmented data views. Existing approaches generally generate the two data views from the original graph, which has been revealed to be less effective due to the lack of data diversity. Meanwhile, although the data augmentation methods and the contrastive modes have been extensively studied, the effect of hard negative samples (i.e.samples that are difficult to distinguish from an anchor node) on GCL is not fully explored. In this paper, we propose a novel complementary graph contrastive learning method boosted by adversarial hard negative sample generation. Specifically, we first construct a κNN graph as the complementary counterpart of the original graph in the semantic space. Then graph augmentation is conducted in both the semantic and topology spaces for the two complementary graphs to obtain two contrastive views with a larger data diversity. To facilitate the contrastive learning, an adversarial network named ADNet is also proposed to generate hard negative samples. The generated samples are more informative and challenging, and thus can further boost the learning performance. Extensive evaluations over the node classification task demonstrate that our proposal outperforms existing state-of-the-art GCL methods, and even exceeds supervised approaches. The code of this work is publicly available at https://github.com/sktsherlock/HNGCL-V1.

The autonomous exploration task we consider requires Unmanned Aerial Vehicles (UAVs) to actively navigate through unknown environments with the goal of fully perceiving and mapping the environments. Some existing exploration strategies suffer from rough cost budgets, ambiguous Information Gain (IG), and unnecessary backtracking exploration caused by Fragmented Regions (FRs). In our work, a hierarchical spatio-temporal-aware exploration framework is proposed to alleviate these problems. At the local exploration level, the Asymmetrical Traveling Salesman Problem (ATSP) is solved by comprehensively considering exploration time, IG, and heading consistency to avoid blindly exploring. Specifically, the exploration time is reasonably budgeted by fast marching in an artificial potential field. Meanwhile, a transformer-based map occupancy predictor is designed to assist in IG calculation by imagining spatial clues out of the Field of View (FoV), facilitating the prescient exploration. We verify that our local exploration is effective in alleviating the unnecessary back-and-forth movements caused by FRs and the interference of potential obstacle occlusion on the IG calculation. At the global exploration level, the classical Next Best View Points (NBVP) are generalized to Next Best Sub-Regions (NBSR) to choose informative sub-regions for further forward-looking exploration based on a well-designed utility function. Safe flight paths and dynamically feasible trajectories are reasonably generated throughout the exploration process by fast marching and B-spline curve optimization. Comparative simulations and benchmark tests demonstrate that our proposed exploration strategy is quite competitive in terms of exploration path length, total exploration time, and exploration ratio.

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.

Urban flow imputation, which aims to infer the missing flows of some locations based on the available flows of surrounding areas, is critically important to various smart city related applications such as urban planning and public safety. Although many methods are proposed to impute time series data, they may not be feasible to be directly applied on urban flow data due to the following reasons. First, urban flows have the complex spatial and temporal correlations which are much harder to be captured compared with time series data. Second, the urban flow data can be random missing (i.e., missing randomly in terms of times and locations) or block missing (i.e., missing for all locations in a particular time slot). Thus it is difficult for existing methods to work well on both scenarios. In this paper, we for the first time study the urban flow imputation problem and propose a generative-free Attention-based Spatial-Temporal Combine and Mix Completion Network model (AST-CMCN for short) to effectively address it. Specifically, AST-CMCN consists of a Spatial and Temporal Completion Network (SATCNet for short) and a Spatial-Temporal Mix Completion Network (STMCNet for short). SATCNet is composed of stacked GRUAtt modules to capture the geographical and temporal correlations of the urban flows, separately. STMCNet is designed to capture the complex spatial-temporal associations jointly between historical urban flows and current data. A Message Passing module is also proposed to capture new spatial-temporal patterns that never appear in the historical data. Extensive experiments on two large real-world datasets validate the effectiveness and efficiency of our method compared with the state-of-the-art baselines.

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.

Matrix completion, whose goal is to recover a matrix from a few entries observed, is a fundamental model behind many applications. Our study shows that, in many applications, the to-be-complete matrix can be represented as the sum of a low-rank matrix and a sparse matrix associating with side information matrices. The low-rank matrix depicts the global patterns while the sparse matrix characterizes the local patterns, which are often described by the side information. Accordingly, to achieve high-quality matrix completion, we propose a Feature and Nuclear Norm Minimization (FNNM) model. The rationale of FNNM is to employ transductive completion to generalize the global pattern and inductive completion to recover the local pattern. Alternative minimization algorithm based on fixed-point iteration is developed to numerically solve the FNNM model. FNNM has demonstrated promising results on a variety of applications, including movie recommendation, drug-target interaction prediction, and multi-label learning, consistently outperforming the state-of-the-art matrix completion algorithms.

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.

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.