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Sublinear Algorithms in T-Interval Dynamic Networks
We consider standard T - interval dynamic networks , under the synchronous timing model and the broadcast CONGEST model. In a T - interval dynamic network ,...
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Extract Implicit Semantic Friends and Their Influences from Bipartite Network for Social Recommendation
Social recommendation often incorporates trusted social links with user-item interactions to enhance rating prediction. Although methods that...
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Parallel continuous skyline query over high-dimensional data stream windows
Real-time multi-criteria decision-making applications in fields like high-speed algorithmic trading, emergency response, and disaster management have...
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The NP-hard problem of computing the maximal sample variance over interval data is solvable in almost linear time with a high probability
We consider the algorithm by Ferson et al. (Reliab Comput 11(3):207--233, 2005) designed for solving the NP-hard problem of computing the maximal...
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Stagnation Detection in Highly Multimodal Fitness Landscapes
Stagnation detection has been proposed as a mechanism for randomized search heuristics to escape from local optima by automatically increasing the...
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Multi-view Heterogeneous Graph Neural Networks for Node Classification
Recently, with graph neural networks (GNNs) becoming a powerful technique for graph representation, many excellent GNN-based models have been...
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Parameterized Complexity of Streaming Diameter and Connectivity Problems
We initiate the investigation of the parameterized complexity of Diameter and Connectivity in the streaming paradigm. On the positive end, we show...
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Approximation Algorithms for the Two-Watchman Route in a Simple Polygon
The two-watchman route problem is that of computing a pair of closed tours in an environment so that the two tours together see the whole environment...
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Graph-Enhanced Prompt Learning for Personalized Review Generation
Personalized review generation is significant for e-commerce applications, such as providing explainable recommendation and assisting the composition...
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Algorithms for Matrix Multiplication via Sampling and Opportunistic Matrix Multiplication
As proposed by Karppa and Kaski (in: Proceedings 30th ACM-SIAM Symposium on Discrete Algorithms (SODA), 2019) a novel “broken" or "opportunistic"...
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Channel-Enhanced Contrastive Cross-Domain Sequential Recommendation
Sequential recommendation help users find interesting items by modeling the dynamic user-item interaction sequences. Due to the data sparseness...
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Online Unit Profit Knapsack with Predictions
A variant of the online knapsack problem is considered in the setting of predictions. In Unit Profit Knapsack, the items have unit profit, i.e., the...
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Approximate and Randomized Algorithms for Computing a Second Hamiltonian Cycle
In this paper we consider the following problem: Given a Hamiltonian graph G , and a Hamiltonian cycle C of G , can we compute a second Hamiltonian...
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PECC: parallel expansion based on clustering coefficient for efficient graph partitioning
In the pursuit of graph processing performance, graph partitioning, as a crucial preprocessing step, has been widely concerned. Based on an in-depth...
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Erdos: A Novel Blockchain Consensus Algorithm with Equitable Node Selection and Deterministic Block Finalization
The introduction of blockchain technology has brought about significant transformation in the realm of digital transactions, providing a secure and...
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Online Geometric Covering and Piercing
We consider the online version of the piercing set problem, where geometric objects arrive one by one, and the online algorithm must maintain a valid...
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Slim Tree-Cut Width
Tree-cut width is a parameter that has been introduced as an attempt to obtain an analogue of treewidth for edge cuts. Unfortunately, in spite of its...
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Approximating Long Cycle Above Dirac’s Guarantee
Parameterization above (or below) a guarantee is a successful concept in parameterized algorithms. The idea is that many computational problems admit...
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New Algorithms for Steiner Tree Reoptimization
Reoptimization is a setting in which we are given a good approximate solution of an optimization problem instance and a local modification that...