scholarly journals Unit Disk Graph-Based Node Similarity Index for Complex Network Analysis

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-22
Author(s):  
Natarajan Meghanathan
Keyword(s):  
Coordinate System ◽  
Unit Disk ◽  
Complex Network ◽  
Search Algorithm ◽  
Similarity Index ◽  
Unit Disk Graph ◽  
Threshold Distance ◽  
Node Level ◽  
Centrality Metrics ◽  
Node Similarity

We seek to quantify the extent of similarity among nodes in a complex network with respect to two or more node-level metrics (like centrality metrics). In this pursuit, we propose the following unit disk graph-based approach: we first normalize the values for the node-level metrics (using the sum of the squares approach) and construct a unit disk graph of the network in a coordinate system based on the normalized values of the node-level metrics. There exists an edge between two vertices in the unit disk graph if the Euclidean distance between the two vertices in the normalized coordinate system is within a threshold value (ranging from 0 tok, where k is the number of node-level metrics considered). We run a binary search algorithm to determine the minimum value for the threshold distance that would yield a connected unit disk graph of the vertices. We refer to “1 − (minimum threshold distance/k)” as the node similarity index (NSI; ranging from 0 to 1) for the complex network with respect to the k node-level metrics considered. We evaluate the NSI values for a suite of 60 real-world networks with respect to both neighborhood-based centrality metrics (degree centrality and eigenvector centrality) and shortest path-based centrality metrics (betweenness centrality and closeness centrality).

scholarly journals An Efficient Search Algorithm for Multi-hop Network Localization in a Sparse Unit Disk Graph Model

2017 ◽  
Vol 11 (1) ◽  
pp. 1-9
Author(s):  
Phisan Kaewprapha ◽  
Thaewa Tansarn ◽  
Nattakan Puttarak
Keyword(s):  
Unit Disk ◽  
Complexity Analysis ◽  
Search Algorithm ◽  
Graph Model ◽  
Search Space ◽  
Evaluation Function ◽  
Unit Disk Graph ◽  
Network Localization ◽  
Graph Properties ◽  
Anchor Nodes

We consider a network localization problem by modeling this as a unit disk graph where nodes are randomly placed with uniform distribution in an area.The connectivity between nodes is defined when the distances fall within a unit range. Under a condition that certain nodes know their locations (anchor nodes), this paper proposes a heuristic approach to find a realization for the rest of the network by applying a tree search algorithm in a depth- first search manner. Our contribution is to put together a priori information and constraints such as graph properties in order to speed up the search. An  evaluation function is formed and used to prune down the search space. This evaluation function is used to select the order of the unknown nodes to iterate. This paper also extends the idea further by accommodating a variety of other properties of graphs into the evaluation function. The results show that node degrees, node distances and shortest paths to anchor nodes drastically reduce the number of iterations required for realizing a feasible localization instance both in noise-free and noisy environments. Finally, some preliminary complexity analysis is also given.

scholarly journals A New Constant Factor Approximation to Construct Highly Fault-Tolerant Connected Dominating Set in Unit Disk Graph

2017 ◽  
Vol 25 (1) ◽  
pp. 18-28 ◽  
Author(s):  
Wei Wang ◽  
Bei Liu ◽  
Donghyun Kim ◽  
Deying Li ◽  
Jingyi Wang ◽  
...  
Keyword(s):  
Unit Disk ◽  
Fault Tolerant ◽  
Dominating Set ◽  
Constant Factor ◽  
Connected Dominating Set ◽  
Unit Disk Graph

Link prediction via layer relevance of multiplex networks

2017 ◽  
Vol 28 (08) ◽  
pp. 1750101 ◽  
Author(s):  
Yabing Yao ◽  
Ruisheng Zhang ◽  
Fan Yang ◽  
Yongna Yuan ◽  
Qingshuang Sun ◽  
...  
Keyword(s):  
Structural Properties ◽  
Link Prediction ◽  
Structural Information ◽  
Similarity Index ◽  
Single Layer ◽  
Structural Features ◽  
Prediction Performance ◽  
Multiplex Networks ◽  
Multiplex Network ◽  
Node Similarity

In complex networks, the existing link prediction methods primarily focus on the internal structural information derived from single-layer networks. However, the role of interlayer information is hardly recognized in multiplex networks, which provide more diverse structural features than single-layer networks. Actually, the structural properties and functions of one layer can affect that of other layers in multiplex networks. In this paper, the effect of interlayer structural properties on the link prediction performance is investigated in multiplex networks. By utilizing the intralayer and interlayer information, we propose a novel “Node Similarity Index” based on “Layer Relevance” (NSILR) of multiplex network for link prediction. The performance of NSILR index is validated on each layer of seven multiplex networks in real-world systems. Experimental results show that the NSILR index can significantly improve the prediction performance compared with the traditional methods, which only consider the intralayer information. Furthermore, the more relevant the layers are, the higher the performance is enhanced.

Link prediction based on contribution of neighbors

2020 ◽  
Vol 31 (11) ◽  
pp. 2050158
Author(s):  
Xiang-Chun Liu ◽  
Dian-Qing Meng ◽  
Xu-Zhen Zhu ◽  
Yang Tian
Keyword(s):  
Complex Network ◽  
Link Prediction ◽  
Node Degree ◽  
Prediction Methods ◽  
Obvious Advantage ◽  
Node Similarity ◽  
The Difference

Link prediction based on node similarity has become one of the most effective prediction methods for complex network. When calculating the similarity between two unconnected endpoints in link prediction, most scholars evaluate the influence of endpoint based on the node degree. However, this method ignores the difference in contribution of neighbor (NC) nodes for endpoint. Through abundant investigations and analyses, the paper quantifies the NC nodes to endpoint, and conceives NC Index to evaluate the endpoint influence accurately. Extensive experiments on 12 real datasets indicate that our proposed algorithm can increase the accuracy of link prediction significantly and show an obvious advantage over traditional algorithms.

scholarly journals A Method for Obtaining Optimal Path in Angle and Avoiding Collision for Robotic Belt Grinding

2019 ◽  
Vol 2019 ◽  
pp. 1-19
Author(s):  
Tie Zhang ◽  
Xiaohong Liang ◽  
Ye Yu ◽  
Bin Zhang
Keyword(s):  
Coordinate System ◽  
Search Algorithm ◽  
Optimal Path ◽  
Recursive Method ◽  
Coordinate Plane ◽  
Angular Variation ◽  
Belt Grinding ◽  
Robotic Grinding ◽  
Path Search ◽  
Robotic Belt Grinding

The angular variation of the joints may be large, and collision between workpieces and tools may occur in robotic grinding. Therefore, this paper proposes an optimal robotic grinding path search algorithm based on the recursive method. The algorithm is optimized by changing the position of the tool coordinate system on the belt wheel; thus, the pose of the robot during grinding is adjusted. First, the position adjustment formula of the tool coordinate system is proposed, and a coordinate plane is established to describe the grinding path of the robot based on the position adjustment formula. Second, the ordinate value of this coordinate plane is dispersed to obtain the search field of the optimal robotic grinding path search algorithm. Third, an optimal robotic grinding path search algorithm is proposed based on the recursive method and single-step search process. Finally, the algorithm is implemented on the V-REP platform. Robotic grinding paths for V-shaped workpieces and S-shaped workpieces are generated using this algorithm, and a grinding experiment is performed. The experimental results show that the robotic grinding paths generated by this algorithm can smoothly complete grinding operations and feature a smaller angular variation of the joint than other methods and no collision.

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