A new closeness centrality measure via effective distance in complex networks

In this work a new centrality measure of graphs is presented, based on the principal eigenvector of the distance matrix: spectral closeness. Using spectral graph theory, we show some of its properties and we compare the results of this new centrality with closeness centrality. In particular, we prove that for threshold graphs these two centralities always coincide. In addition we construct an infinity family of graphs for which these centralities never coincide.

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

Algorithms ◽

10.3390/a13090211 ◽

2020 ◽

Vol 13 (9) ◽

pp. 211 ◽

Cited By ~ 1

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.

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Relative Degree Structural Hole Centrality, CRD−SH: A New Centrality Measure in Complex Networks

Journal of Systems Science and Complexity ◽

10.1007/s11424-018-7331-5 ◽

2019 ◽

Vol 32 (5) ◽

pp. 1306-1323

Author(s):

Hamidreza Sotoodeh ◽

Mohammed Falahrad

Keyword(s):

Complex Networks ◽

Relative Degree ◽

Centrality Measure ◽

Structural Hole

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Identifying Core Parts in Complex Mechanical Product for Change Management and Sustainable Design

Sustainability ◽

10.3390/su10124480 ◽

2018 ◽

Vol 10 (12) ◽

pp. 4480 ◽

Cited By ~ 1

Author(s):

Na Zhang ◽

Yu Yang ◽

Jianxin Wang ◽

Baodong Li ◽

Jiafu Su

Keyword(s):

Design Process ◽

Sustainable Design ◽

Closeness Centrality ◽

Node Degree ◽

Global Information ◽

Degree Centrality ◽

Centrality Measure ◽

The Core ◽

Mechanical Products ◽

Negative Impacts

Changes in customer needs are unavoidable during the design process of complex mechanical products, and may bring severely negative impacts on product design, such as extra costs and delays. One of the effective ways to prevent and reduce these negative impacts is to evaluate and manage the core parts of the product. Therefore, in this paper, a modified Dempster-Shafer (D-S) evidential approach is proposed for identifying the core parts. Firstly, an undirected weighted network model is constructed to systematically describe the product structure. Secondly, a modified D-S evidential approach is proposed to systematically and scientifically evaluate the core parts, which takes into account the degree of the nodes, the weights of the nodes, the positions of the nodes, and the global information of the network. Finally, the evaluation of the core parts of a wind turbine is carried out to illustrate the effectiveness of the proposed method in the paper. The results show that the modified D-S evidential approach achieves better performance regarding the evaluation of core parts than the node degree centrality measure, node betweenness centrality measure, and node closeness centrality measure.

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