Best Neighbor Heuristic Search for Finding Minimum Paths in Transportation Networks

1998 ◽  
Vol 1651 (1) ◽  
pp. 48-52 ◽  
Author(s):  
Jeffrey L. Adler
Keyword(s):  
Heuristic Search ◽  
Transportation Networks ◽  
Transportation Network ◽  
Search Problems ◽  
A Algorithm ◽  
Running Time ◽  
Search Effort ◽  
Wide Range ◽  
Path Search ◽  
Sparse Networks

For a wide range of transportation network path search problems, the A* heuristic significantly reduces both search effort and running time when compared to basic label-setting algorithms. The motivation for this research was to determine if additional savings could be attained by further experimenting with refinements to the A* approach. We propose a best neighbor heuristic improvement to the A* algorithm that yields additional benefits by significantly reducing the search effort on sparse networks. The level of reduction in running time improves as the average outdegree of the network decreases and the number of paths sought increases.

scholarly journals Swarm shedding in networks of self-propelled agents

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jason Hindes ◽  
Victoria Edwards ◽  
Klimka Szwaykowska Kasraie ◽  
George Stantchev ◽  
Ira B. Schwartz
Keyword(s):  
Network Topology ◽  
Collective Motion ◽  
Mean Field Theory ◽  
Mean Field ◽  
Interaction Strength ◽  
Central Question ◽  
Agent Interaction ◽  
Sparse Networks ◽  
Swarm Pattern ◽  
Limited Sensing

AbstractUnderstanding swarm pattern formation is of great interest because it occurs naturally in many physical and biological systems, and has artificial applications in robotics. In both natural and engineered swarms, agent communication is typically local and sparse. This is because, over a limited sensing or communication range, the number of interactions an agent has is much smaller than the total possible number. A central question for self-organizing swarms interacting through sparse networks is whether or not collective motion states can emerge where all agents have coherent and stable dynamics. In this work we introduce the phenomenon of swarm shedding in which weakly-connected agents are ejected from stable milling patterns in self-propelled swarming networks with finite-range interactions. We show that swarm shedding can be localized around a few agents, or delocalized, and entail a simultaneous ejection of all agents in a network. Despite the complexity of milling motion in complex networks, we successfully build mean-field theory that accurately predicts both milling state dynamics and shedding transitions. The latter are described in terms of saddle-node bifurcations that depend on the range of communication, the inter-agent interaction strength, and the network topology.

scholarly journals Recovering sparse networks: Basis adaptation and stability under extensions

2021 ◽  
pp. 132895
Author(s):  
Marcel Novaes ◽  
Edmilson Roque dos Santos ◽  
Tiago Pereira
Keyword(s):  
Sparse Networks

GRADE approach to rate the certainty from a network meta-analysis: avoiding spurious judgments of imprecision in sparse networks

2019 ◽  
Vol 105 ◽  
pp. 60-67 ◽  
Author(s):  
Romina Brignardello-Petersen ◽  
M. Hassan Murad ◽  
Stephen D. Walter ◽  
Shelley McLeod ◽  
Alonso Carrasco-Labra ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Sparse Networks ◽  
Grade Approach

scholarly journals Mean Hitting Time for Random Walks on a Class of Sparse Networks

Entropy ◽  
2021 ◽  
Vol 24 (1) ◽  
pp. 34
Author(s):  
Jing Su ◽  
Xiaomin Wang ◽  
Bing Yao
Keyword(s):  
Random Walks ◽  
Complete Graph ◽  
Hitting Time ◽  
Electrical Networks ◽  
Kirchhoff Index ◽  
Scale Free ◽  
Closed Form Solutions ◽  
Sparse Networks ◽  
Similar Dynamic ◽  
Recursive Relations

For random walks on a complex network, the configuration of a network that provides optimal or suboptimal navigation efficiency is meaningful research. It has been proven that a complete graph has the exact minimal mean hitting time, which grows linearly with the network order. In this paper, we present a class of sparse networks G(t) in view of a graphic operation, which have a similar dynamic process with the complete graph; however, their topological properties are different. We capture that G(t) has a remarkable scale-free nature that exists in most real networks and give the recursive relations of several related matrices for the studied network. According to the connections between random walks and electrical networks, three types of graph invariants are calculated, including regular Kirchhoff index, M-Kirchhoff index and A-Kirchhoff index. We derive the closed-form solutions for the mean hitting time of G(t), and our results show that the dominant scaling of which exhibits the same behavior as that of a complete graph. The result could be considered when designing networks with high navigation efficiency.

scholarly journals All-pairs min-cut in sparse networks

1995 ◽  
pp. 363-376
Author(s):  
Srinivasa R. Arikati ◽  
Shiva Chaudhuri ◽  
Christos D. Zaroliagis
Keyword(s):  
Sparse Networks ◽  
Min Cut
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