The diversified route planning finds multiple paths that are sufficiently different from each other while as short as possible. It is of great significance to traffic alleviation through provided alternative routes during navigation. However, it is NP-H to find the optimal result and the existing solutions have either high quality (exact path enumeration-based) or high efficiency (alternative path), which is highly affected by the network properties and query parameters but has never been investigated before. Therefore, this paper proposes a hybrid diversified routing system that can handle any query efficiently with quality as high as possible. Specifically, we first analyze the path enumeration problem from the ground up and unify all the existing algorithms theoretically to identify the factors that affect algorithm performance. After that, we review and select the alternative path methods to identify the suitable ones for our system. Finally, we propose a query classification module to estimate the hardness of a query and determine how it should be processed. Extensive experiments on real-life networks validate the effectiveness and efficiency of our hybrid system compared with state-of-the-art solutions.

Abnormal behavior detection is crucial in many fields, such as social networks, financial transactions, and cyber security. However, it poses significant challenges due to the intricate structural evolution of heterogeneous graphs and the need for explainable models. To address these issues, we propose a novel method called Explainable anomalous behavior (edge) detection for dynamic heterogeneous Graphs (ExpGraph). ExpGraph captures relation-aware structural evolution to model temporal behavioral patterns and introduces a prototype alignment mechanism to improve both performance and interpretability. Specifically, prototype alignment enhances detection by en couraging discriminative representations of normal behaviors, which facilitates more accurate identification of anomalies. It also improves interpretability by enabling intuitive explanations through measuring how anomalous behaviors differ from learned normal prototypes. We conduct extensive experiments to evaluate ExpGraph against advanced competitors. It demonstrates that ExpGraph is 16.2% more effective than other methods on average. Moreover, it offers a deeper insight into abnormal behaviors in dynamic heterogeneous graphs. Our code is available at https://github.com/anonymous-123a/ExpGraph.

Sequential change detection in high-dimensional dynamic networks has attracted growing attention in modern applications. A major challenge is that as network scale increases, limited sensing resources make it difficult to fully observe links at each time point, resulting in partial observability and complicating detection. To tackle this, we propose a Latent Bi-Transit Network (LBTN) that learns unobserved edge formation, latent node states, and the evolution mechanisms of dynamic networks. Based on LBTN, we design a variational Bayesian method to infer sparse node-level changes and employ a likelihood ratio test as the detection statistic. By further formulating the statistic as the reward function of a combinatorial multi-armed bandit (CMAB) problem, we develop a Thompson sampling strategy to adaptively select edges for observation, balancing exploration and exploitation. Comprehensive simulations and real-world experiments show that our method consistently outperforms existing baselines and its variants across diverse scenarios.

In information systems lacking decision-making information, effectively leveraging fuzzy rough sets for outlier detection in complex data is challenging, especially in capturing inherent uncertainty and multi-granularity characteristics to construct discriminative outlier scores. However, existing fuzzy rough sets-based outlier detection methods often suffer from three key limitations: (1) Local data distributions are often ignored when calculating fuzzy relation matrices, resulting in inaccurate fuzzy similarity representations; (2) Use of all objects in fuzzy upper and lower approximations can weaken noise resistance and increase computational complexity; (3) Single-granularity data processing reduces efficiency and may fail to capture the multi-granularity nature of data, thereby limiting the adaptability of these methods in complex data environments. To address these issues, we propose to fuses <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b>atural neighbor fuzzy approximations with <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b>ranular-ball representation for <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</b>utlier <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b>etection (NGOD), which integrates the multi-granularity granular-ball representation and fuzzy rough sets to improve the effectiveness and robustness of unsupervised outlier detection. Specifically, we first define a local distribution-aware fuzzy relation, enabling more discriminative similarity calculations between samples. To improve the effectiveness and robustness of fuzzy upper and lower approximations, we propose a multi-granularity natural neighbor fuzzy approximation model, which effectively utilizes the inherent uncertainty and local abnormal information of data in approximations. Moreover, by introducing natural neighbors, NGOD can adaptively capture local abnormal information in the data without setting neighborhoods manually. Finally, the outlier factors of each sample are calculated in NGOD to measure their outlier degrees. Extensive experiments on diverse datasets demonstrate that NGOD outperforms state-of-the-art methods, validating its superior performance and adaptability. The NGOD code and associated datasets are publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Mxeron/NGOD</uri>.

We propose the DPSM method, a density-based node clustering approach that automatically determines the number of clusters and can be applied in both data space and graph space. Unlike traditional density-based clustering methods, which necessitate calculating the distance between any two nodes, our proposed technique determines density through a propagation process, thereby making it suitable for a graph space. In DPSM, nodes are partitioned into small clusters based on propagated density. The partitioning technique has been proved to be sound and complete. We then extend the concept of spectral clustering from individual nodes to these small clusters, while introducing the CluCut measure to guide cluster merging. This measure is modified in various ways to account for cluster properties, thus provides guidance on when to terminate the merging process. Various experiments have validated the effectiveness of DPSM and the accuracy of these conclusions.

This paper addresses the challenge of identifying super spreaders within large, high-speed data streams. In these streams, data is segmented into flows, with each flow's spread defined as the number of distinct items it contains. A super spreader is characterized as a flow with a notably large spread. Measuring flow spread requires counting any item at its first appearance and ignoring its subsequent duplicate appearances. Current compact solutions, known as sketches, are designed to fit within the constrained memory of online devices. However, existing sketches face accuracy challenges in spread tracking due to the substantial memory needed to remove the impact of duplicate appearances for measuring a single flow's spread—a problem that compounds with increasing flow counts. We propose a novel sketch-based solution to address these limitations. At its core, our approach features an innovative non-duplicate sampler that eliminates duplicate appearances of any item, enabling accurate flow spread calculation using simple counters. Combined with our exponential-weakening decay mechanism that emphasizes large flows, the solution significantly improves super spreader detection accuracy. We provide rigorous theoretical analysis of our method and validate its performance through trace-driven experiments. Results demonstrate that our approach statistically outperforms existing state-of-the-art solutions in super spreader identification. Moreover, it achieves the fastest super spreader restoration time and reduces bandwidth consumption by an order of magnitude during remote offline restoration.

The advent of Single Instruction Multiple Data (SIMD) instructions in modern processors has revolutionized data processing by enabling simultaneous computation across multiple data elements. While database systems have extensively adopted SIMD for traditional operations, its potential for complex event pattern matching remains largely unexplored. This paper presents a novel approach that bridges this gap through bit-parallel processing enhanced with AVX-512 vectorization. Our approach encodes event streams into compact bit sequences, where each bit corresponds to a time slice, and an event's presence is marked by a 1-bit when its timestamp falls within the respective slice. This representation enables the formulation of bit-parallel operations that natively enforce complex event constraints, including temporal window requirements and event ordering relationships. We develop a family of bit-parallel algorithms that leverage this representation for continuous event matching, and further optimize their performance through SIMD vectorization (AVX-512 instructions) to exploit modern hardware parallelism. Experimental evaluations on both real-world and synthetic datasets demonstrate the superiority of our method, achieving at least 35.7x improvement in query efficiency compared to state-of-the-art alternatives.