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432 results on '"Jalili, Mahdi"'

28. Enhanced methods for the weight constrained shortest path problem.

Author

Ahmadi, Saman, Tack, Guido, Harabor, Daniel, Kilby, Philip, and Jalili, Mahdi

Subjects
COMMONS, ALGORITHMS
Abstract

The classic problem of constrained pathfinding is a well‐studied, yet challenging, network optimization problem with a broad range of applications in various areas such as communication and transportation. The weight constrained shortest path problem (WCSPP), the base form of constrained pathfinding with only one side constraint, aims to plan a cost‐optimum path with limited weight/resource usage. Given the bi‐criteria nature of the problem (i.e., dealing with the cost and weight of paths), methods addressing the WCSPP have some common properties with bi‐objective search. This article leverages the recent state‐of‐the‐art techniques in both constrained pathfinding and bi‐objective search and presents two new solution approaches to the WCSPP on the basis of A* search, both capable of solving hard WCSPP instances on very large graphs. We empirically evaluate the performance of our algorithms on a set of large and realistic problem instances and show their advantages over the state‐of‐the‐art algorithms in both time and space metrics. This article also investigates the importance of priority queues in constrained search with A*. We show with extensive experiments on both realistic and randomized graphs how bucket‐based queues without tie‐breaking can effectively improve the algorithmic performance of exhaustive A*‐based bi‐criteria searches. [ABSTRACT FROM AUTHOR]

Published
2024
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35. A new approach to finding overlapping community structure in signed networks based on Neutrosophic theory.

Author

Gholami, Maryam, Sheikhahmadi, Amir, Khamforoosh, Keyhan, Jalili, Mahdi, and Veisi, Farshid

Subjects
CONVOLUTIONAL neural networks
Abstract

One of the longstanding challenges in network science is the identification of overlapping community structures. Real-world networks often exhibit a complex interplay of positive and negative relationships, making the recognition of overlapping communities a crucial area of research. Current community detection methods in signed networks primarily focus on discovering disjoint communities, where each node belongs exclusively to a single community. However, these algorithms often fail to detect overlapping communities, where nodes can belong to multiple communities simultaneously. To address this limitation, we propose a novel approach called Neutrosophic c-means Overlapping Community Detection (NOCD) based on neutrosophic set (NS) theory. By incorporating NS theory, our approach effectively handles the uncertainty associated with ambiguous community boundaries and appropriately handles nodes on the community boundaries and isolated nodes. The NOCD method comprises two phases: firstly, a signed graph convolutional neural network is employed to learn the structural features of the signed network in a lower-dimensional representation; secondly, overlapping communities are detected using the neutrosophic c-means algorithm applied to the embedded network. To evaluate the effectiveness of our proposed NOCD method, we conducted comprehensive experiments on both real and artificial networks. The experimental results demonstrate the effectiveness and robustness of NOCD in identifying overlapping communities, outperforming existing methods. [Received on 3 August 2023; editorial decision on 13 December 2023; accepted on 19 December 2023] [ABSTRACT FROM AUTHOR]

Published
2024
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