Triangle counting (TC) plays a vital role in graph mining systems. However, existing methods often rely on single intersection kernel function, limiting their performance across diverse datasets. To address this chall...
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ISBN:
(纸本)9789819755615;9789819755622
Triangle counting (TC) plays a vital role in graph mining systems. However, existing methods often rely on single intersection kernel function, limiting their performance across diverse datasets. To address this challenge, we proposed StepTC (a novel Stepwise Triangle Counting algorithm). StepTC selects appropriate set intersection methods for each counting step to ensure optimal efficiency on a global scale. Additionally, two efficient set intersection methods and a dynamic shared memory assignment strategy are implemented to enhance the adaptability of StepTC for GPUs. A round-robin scheduling strategy for task partitioning is employed to achieve load balancing across multiple GPUs. It is noteworthy that StepTC outperforms state-of-the-art solutions, delivering impressive speedups ranging from 1.4x to 22.4x on various datasets.
Edge detection of objects in images is a longstanding challenge in computer vision. Traditional edge detection methods for grayscale images exhibit strong generalization but often yield unsatisfactory results when app...
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ISBN:
(纸本)9798350349184;9798350349191
Edge detection of objects in images is a longstanding challenge in computer vision. Traditional edge detection methods for grayscale images exhibit strong generalization but often yield unsatisfactory results when applied to binary images. Currently, contour extraction algorithms based on binary images may fail to ensure strict semantic integrity within the sparse 8-neighborhood or may result in contours with low accuracy. Faced with the limitations of currently existing edge detection and contour extraction algorithms, a novel graph-traversal-based algorithm was proposed to extract the 8-neighborhood sparse contour of binary images, while the higher accuracy is held as well. Initially, we eliminate noise regions in binary images that do not correspond to the main objects and solidify foreground objects to create an approximate outer contour while removing outlier areas. Subsequently, we fine-tune the candidate contour pixel sets to obtain the final sparse outer contour. Additionally, we extend the algorithm to achieve inner contour extraction and contour extraction for multiple non-interfering foreground entities. Experimental evaluations of the algorithm are conducted on the LiTS17 dataset and compared with state-of-the-art edge extraction and contour tracing algorithms, as well as the latest binary image 8-neighborhood sparse contour extraction algorithms. The proposed algorithm exhibits a 22.3% improvement in accuracy despite a 32% runtime increase, with higher or equivalent accuracy in all individual images and no degradation observed. (code: https://***/nianwuluo/ct-liver-CSCWD)
We propose an effective hybrid approach jointly leveraging local and global features for shortest-path (SP) distance estimation in domain-agnostic large-scale graphs. Previous works struggle to make estimations either...
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ISBN:
(纸本)9798350359329;9798350359312
We propose an effective hybrid approach jointly leveraging local and global features for shortest-path (SP) distance estimation in domain-agnostic large-scale graphs. Previous works struggle to make estimations either from node-wise local embeddings or by compressing a global SP distance matrix, causing insufficient learning at some distance and loss of accuracy. Unlike them, we find a way to better preserve local distance on node embeddings, and then integrate them with a global process for accurate estimation at every distance. First, we propose a distance-consistent embedding method that better preserves the distance between each node and its local neighbors due to resampling node occurrence on random walks. Second, we train a feed-forward network with boosting techniques (FFN-BT) to estimate SP distance from these embeddings plus existing global features. Experimental results show that our approach averagely yields 10% improved accuracy and 20% reduced time when compared to existing methods on a broad class of graphs.
Automatic knowledge graph (KG) construction is widely used for e.g. data integration, question answering and semantic search. There are many approaches of automatic KG construction. Among which, an important approach ...
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ISBN:
(纸本)9781450392365
Automatic knowledge graph (KG) construction is widely used for e.g. data integration, question answering and semantic search. There are many approaches of automatic KG construction. Among which, an important approach is to map the raw data to a given domain KG schema, e.g., domain ontology or conceptual graph, and construct the entities and properties according to the domain KG schema. However, the existing approaches to construct KGs are not always efficient enough and the resulting KGs are not sufficiently user-friendly. The main challenge arises from the trade-off: the domain KG schema should be knowledge-oriented, to reflect the general domain knowledge;while a KG schema should be data-oriented, to cover all data features. If the former is directly used for KG construction, this can cause issues like a high load of blank nodes, which are technical nodes in the KGs that represent unknown entities. To this end, we propose our ScheRe system in the demo, which relies on a schema reshaping algorithm and other two semantic modules for enhancing KG construction. The demo attendees will use ScheRe to reshape a domain KG schema to data specific KG schema, build KGs with industrial data, and experience more user-friendly querying.
Temporal graphs attach time information to edges and are commonly used for implementing time-critical applications that can not be effectively processed by traditional static and dynamic graph processing engines. Stat...
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ISBN:
(数字)9781665408837
ISBN:
(纸本)9781665408837
Temporal graphs attach time information to edges and are commonly used for implementing time-critical applications that can not be effectively processed by traditional static and dynamic graph processing engines. State-of-the-art solutions that target temporal path problems remain ad-hoc and often suboptimal. A unified and high-performance solution that could efficiently process general temporal path problems via a universal optimization strategy and relieve practitioners from heavy optimization efforts is in urgent demand. In this paper, we make two key observations: (1) temporal path problems can be described as topological-optimum problems and solved by a universal single scan execution model;and (2) data redundancy commonly occurs in the native format of the transformed temporal graphs, which is unnecessary for information propagation and can be eliminated for better memory utilization and execution efficiency. Based on these core insights, we propose TEgraph, the first general-purpose temporal graph computing engine to provide a unified optimization strategy and execution model for general temporal path problems and their applications. TEgraph not only presents temporal information-aware graph representation that naturally fits temporal graphs but also offers general system-level supports such as out-of-core execution. Extensive evaluation reveals that TEgraph can achieve significant speedups over the state-of-the-art designs with up to two orders of magnitude (241x) with the throughput of two hundred million edges per second.
Cloud computing services have found widespread use recently. Offloading computations to public clouds has many benefits albeit harming the privacy of users and data. Homomorphic encryption facilitates cloud computing ...
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ISBN:
(纸本)9798350343557
Cloud computing services have found widespread use recently. Offloading computations to public clouds has many benefits albeit harming the privacy of users and data. Homomorphic encryption facilitates cloud computing services that can do computations over encrypted data without requiring decryption and this enables privacy-preserving applications. In this paper, we propose an approach for confidentially finding islands (connected components) in a graph. We present various performance evaluation results and show that privacy-preservation can be achieved with a cost of computation overhead.
In a graph, a perfect matching cut is an edge cut that is a perfect matching. PERFECT MATCHING CUT (PMC) is the problem of deciding whether a given graph has a perfect matching cut, and is known to be NP-complete. We ...
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In a graph, a perfect matching cut is an edge cut that is a perfect matching. PERFECT MATCHING CUT (PMC) is the problem of deciding whether a given graph has a perfect matching cut, and is known to be NP-complete. We revisit the problem and show that PMC remains NP-complete when restricted to bipartite graphs of maximum degree 3 and arbitrarily large girth. Complementing this hardness result, we give two graph classes in which PMC is polynomial-time solvable. The first one includes claw-free graphs and graphs without an induced path on five vertices, the second one properly contains all chordal graphs. Assuming the Exponential Time Hypothesis, we show there is no O*(2o(n))-time algorithm for PMC even when restricted to n-vertex bipartite graphs, and also show that PMC can be solved in O*(1.2721n) time by means of an exact branching algorithm.(c) 2022 Elsevier B.V. All rights reserved.
In this paper, we address the problem of finding a representation of a subtree distance, which is an extension of a tree metric. We show that a minimal representation is uniquely determined by a given subtree distance...
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In this paper, we address the problem of finding a representation of a subtree distance, which is an extension of a tree metric. We show that a minimal representation is uniquely determined by a given subtree distance, and give an O(n(2)) time algorithm that finds such a representation, where n is the size of the ground set. Since a lower bound of the problem is Omega(n(2)), our algorithm achieves the optimal time complexity.
Data parallel primitives are highly optimized general-purpose algorithms designed only for GPUs and are used as building blocks to develop applications. However, existing data parallel primitives cannot handle data la...
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ISBN:
(纸本)9783031683114;9783031683121
Data parallel primitives are highly optimized general-purpose algorithms designed only for GPUs and are used as building blocks to develop applications. However, existing data parallel primitives cannot handle data larger than the GPU memory size. In this paper, we propose an extension to existing data parallel primitives to efficiently handle data larger than the GPU memory size by cooperatively using both GPUs and CPUs. Moreover, we evaluate the impact of these primitives when applying them to large data processing applications, with respect to both performance and software development cost.
The shortest path problem is the most classical and fundamental problem in the field of graph algorithm. Recently, its reconfiguration variant, namely the Shortest Path Reconfiguration problem, has received a lot of a...
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ISBN:
(数字)9789819705665
ISBN:
(纸本)9789819705658;9789819705665
The shortest path problem is the most classical and fundamental problem in the field of graph algorithm. Recently, its reconfiguration variant, namely the Shortest Path Reconfiguration problem, has received a lot of attention. In this paper, we study the complexity of k-SPR, which generalizes the Shortest Path Reconfiguration problem, with respect to k. In k-SPR, we are allowed to replace at most k consecutive vertices of the current shortest path at a time. We first show that, for any fixed rational numbers c and epsilon such that c > 0 and 0 < epsilon <= 1, k-SPR with k = cn(1-epsilon) is polynomially solvable if epsilon = 1 and c < 1;otherwise, PSPACE-complete. This intractability holds even when given graphs are restricted to bipartite graphs and r-th power graphs, where r is any positive integer. Furthermore, when we restrict 0 < epsilon < 1, the PSPACE-completeness holds for graphs with maximum degree 3. Then, we design an FPT algorithm parameterized by mu = n/2 - k >= 0 that runs in O(m + 6.730(mu) mu(4) n) time. Finally, we show that, for any k, k-SPR can be solved in linear time for K-2,K-3-minor-free graphs.
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