scholarly journals A Scalable Distributed Parallel Breadth-First Search Algorithm on BlueGene/L

2005 ◽  
Author(s):  
A. Yoo ◽  
E. Chow ◽  
K. Henderson ◽  
W. McLendon ◽  
B. Hendrickson ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Breadth First Search

scholarly journals An Innovative Tour Recommendation System using Graph Algorithms

2019 ◽  
Vol 5 (1) ◽  
pp. 32 ◽  
Author(s):  
Md. Sabir Hossain ◽  
Ahsan Sadee Tanim ◽  
Nabila Nawal ◽  
Sharmin Akter
Keyword(s):  
Recommendation System ◽  
Search Algorithm ◽  
Limited Time ◽  
Dijkstra Algorithm ◽  
Breadth First Search ◽  
User Interest ◽  
Trip Planning ◽  
Physical Effort ◽  
Points Of Interest ◽  
Point Of Departure

Background: Tour recommendation and path planning are the most challenging jobs for tourists as they decide Points of Interest (POI).Objective: To reduce the physical effort of the tourists and recommend them a personalized tour is the main objective of this paper. Most of the time people had to find the places he wants to visit in a difficult way. It kills a lot of time.Methods: To cope with this situation we have used different methodology. First, a greedy algorithm is used for filtering the POIs and BFS (Breadth First Search) algorithm will find POI in terms of user interest. The maximum number of visited POI within a limited time will be considered. Then, the Dijkstra algorithm finds the shortest path from the point of departure to the end of tours.Results:  This work shows its users list of places according to the user's interest in a particular city. It also suggests them places to visit in a range from the location of the user where a user can dynamically change this range and it also suggests nearby places they may want to visit.Conclusion: This tour recommendation system provides its users with a better trip planning and thus makes their holidays enjoyable.

A new distributed breadth-first-search algorithm

1987 ◽  
Vol 25 (5) ◽  
pp. 329-333 ◽  
Author(s):  
Yunzhou Zhu ◽  
To-Yat Cheung
Keyword(s):  
Search Algorithm ◽  
Breadth First Search

Research on the Operation Mode of Planned Island and Partitioning Method

2012 ◽  
Vol 433-440 ◽  
pp. 475-479
Author(s):  
Ke Dang ◽  
Hui Ming Zhang ◽  
Xin He
Keyword(s):  
Heuristic Search ◽  
Search Algorithm ◽  
Operation Mode ◽  
Distribution Network ◽  
Operating Mode ◽  
Breadth First Search ◽  
Distributed Power ◽  
Network Failure ◽  
Different Types ◽  
Supply Capacity

The island operation is a new operating mode of the distribution network after the distributed generation is introduced. And it is a beneficial supplement for the operation of the distribution network. The distributed power supply capacity is made full use to ensure the safe operation of the maximum number of significant load when the fault occurs. A combinational algorithm of the planned island partition is presented according to the principle of power balance in the distribution network failure. Heuristic search strategy and the breadth-first search algorithm are adopted by taking into account the characteristics of different types of distributed power. The validity of the method is proved by the example.

scholarly journals Finding Top-k Nodes for Temporal Closeness in Large Temporal Graphs

Algorithms ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 211 ◽  
Author(s):  
Pierluigi Crescenzi ◽  
Clémence Magnien ◽  
Andrea Marino
Keyword(s):  
Time Complexity ◽  
Search Algorithm ◽  
Sampling Technique ◽  
Closeness Centrality ◽  
Experimental Results ◽  
The Other ◽  
Breadth First Search ◽  
Centrality Measure ◽  
Temporal Graphs

The harmonic closeness centrality measure associates, to each node of a graph, the average of the inverse of its distances from all the other nodes (by assuming that unreachable nodes are at infinite distance). This notion has been adapted to temporal graphs (that is, graphs in which edges can appear and disappear during time) and in this paper we address the question of finding the top-k nodes for this metric. Computing the temporal closeness for one node can be done in O(m) time, where m is the number of temporal edges. Therefore computing exactly the closeness for all nodes, in order to find the ones with top closeness, would require O(nm) time, where n is the number of nodes. This time complexity is intractable for large temporal graphs. Instead, we show how this measure can be efficiently approximated by using a “backward” temporal breadth-first search algorithm and a classical sampling technique. Our experimental results show that the approximation is excellent for nodes with high closeness, allowing us to detect them in practice in a fraction of the time needed for computing the exact closeness of all nodes. We validate our approach with an extensive set of experiments.

scholarly journals Puzzle game solving with breadth first search algorithm

2019 ◽  
Vol 1402 ◽  
pp. 066040
Author(s):  
R Rahim ◽  
R Dijaya ◽  
M T Multazam ◽  
A Daengs GS ◽  
D Sudrajat
Keyword(s):  
Search Algorithm ◽  
Breadth First Search

Strategy Study on Maximum Degree and Bisection Degree Synchronization Search in Scale-Free Networks

2013 ◽  
Vol 303-306 ◽  
pp. 2157-2160
Author(s):  
Xin Yi Chen ◽  
Jian Hua Xia
Keyword(s):  
Maximum Degree ◽  
Search Strategy ◽  
Search Algorithm ◽  
Algorithm Design ◽  
Search Efficiency ◽  
Network Congestion ◽  
Breadth First Search ◽  
Strategy Study ◽  
Scale Free ◽  
Search Speed

With the expansion of network and the increasing number of communities’ network, It’s a big problem for the search algorithm to enhance the search efficiency. The number of search steps and the amount of query information generated by maximum degree search strategy, which will grow exponentially, consequently, and lead to low the efficiency of search. Without considering the network congestion, breadth-first search strategy is undoubtedly the best search efficiency. From the point of the breadth-first search strategy, this paper designed and proposed the synchronous search strategy of Maximum degree and Bisection degree, and described the algorithm idea and algorithm design for MBDS. The simulation results showed that MBDS not only decreased the amount of query information, but also can efficiently decrease the search steps and improve the search speed.

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