scholarly journals Distance closures on complex networks

2015 ◽  
Vol 3 (2) ◽  
pp. 227-268 ◽  
Author(s):  
TIAGO SIMAS ◽  
LUIS M. ROCHA
Keyword(s):  
Complex Networks ◽  
Diffusion Processes ◽  
Shortest Paths ◽  
Approximate Reasoning ◽  
Weighted Graphs ◽  
Dijkstra Algorithm ◽  
Simple Method ◽  
Distance Graphs ◽  
Network Analyses ◽  
Metric Distances

AbstractTo expand the toolbox available to network science, we study the isomorphism between distance and Fuzzy (proximity or strength) graphs. Distinct transitive closures in Fuzzy graphs lead to closures of their isomorphic distance graphs with widely different structural properties. For instance, the All Pairs Shortest Paths (APSP) problem, based on the Dijkstra algorithm, is equivalent to a metric closure, which is only one of the possible ways to calculate shortest paths in weighted graphs. We show that different closures lead to different distortions of the original topology of weighted graphs. Therefore, complex network analyses that depend on the calculation of shortest paths on weighted graphs should take into account the closure choice and associated topological distortion. We characterize the isomorphism using the max-min and Dombi disjunction/conjunction pairs. This allows us to: (1) study alternative distance closures, such as those based on diffusion, metric, and ultra-metric distances; (2) identify the operators closest to the metric closure of distance graphs (the APSP), but which are logically consistent; and (3) propose a simple method to compute alternative path length measures and corresponding distance closures using existing algorithms for the APSP. In particular, we show that a specific diffusion distance is promising for community detection in complex networks, and is based on desirable axioms for logical inference or approximate reasoning on networks; it also provides a simple algebraic means to compute diffusion processes on networks. Based on these results, we argue that choosing different distance closures can lead to different conclusions about indirect associations on network data, as well as the structure of complex networks, and are thus important to consider.

scholarly journals Identifying Node Importance in a Complex Network Based on Node Bridging Feature

2018 ◽  
Vol 8 (10) ◽  
pp. 1914 ◽  
Author(s):  
Lincheng Jiang ◽  
Yumei Jing ◽  
Shengze Hu ◽  
Bin Ge ◽  
Weidong Xiao
Keyword(s):  
Complex Networks ◽  
Large Scale ◽  
Shortest Paths ◽  
Evaluation Criteria ◽  
Local Network ◽  
Network Efficiency ◽  
Network Attack ◽  
Node Importance ◽  
Pass Through ◽  
Large Scale Networks

Identifying node importance in complex networks is of great significance to improve the network damage resistance and robustness. In the era of big data, the size of the network is huge and the network structure tends to change dynamically over time. Due to the high complexity, the algorithm based on the global information of the network is not suitable for the analysis of large-scale networks. Taking into account the bridging feature of nodes in the local network, this paper proposes a simple and efficient ranking algorithm to identify node importance in complex networks. In the algorithm, if there are more numbers of node pairs whose shortest paths pass through the target node and there are less numbers of shortest paths in its neighborhood, the bridging function of the node between its neighborhood nodes is more obvious, and its ranking score is also higher. The algorithm takes only local information of the target nodes, thereby greatly improving the efficiency of the algorithm. Experiments performed on real and synthetic networks show that the proposed algorithm is more effective than benchmark algorithms on the evaluation criteria of the maximum connectivity coefficient and the decline rate of network efficiency, no matter in the static or dynamic attack manner. Especially in the initial stage of attack, the advantage is more obvious, which makes the proposed algorithm applicable in the background of limited network attack cost.

scholarly journals MODELING OF EMERGENCY EVACUATION IN BUILDING FIRE

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 321-327 ◽  
Author(s):  
H. Bayat ◽  
M. R. Delavar ◽  
W. Barghi ◽  
S. A. EslamiNezhad ◽  
P. Hanachi ◽  
...  
Keyword(s):  
Indoor Environment ◽  
Shortest Paths ◽  
Fire Risk ◽  
Emergency Evacuation ◽  
Information Modeling ◽  
Sufficient Information ◽  
Dijkstra Algorithm ◽  
Rescue Workers ◽  
Building Information ◽  
Emergency Exit

Abstract. One of the main problems of rescue workers in confrontation of fired complex buildings is the lack of sufficient information about the building indoor environment and their emergency exit ways. Building information modeling (BIM) is a database for building a 3D model of building information to create a 3D building geometry network model. This paper has implemented some GIS and BIM integration analyses to determine the shortest and safest paths to people under fire risk and simulate their movement in the building. Plasco building was a multi-story shop in Tehran which has been fired in 2017 and destroyed. This paper attempts to simulate the firefighting and rescue operations in Plasco Building using an integration of BIM and GIS. There is no detailed information about the building and the fire incident, therefore the developed BIM and corresponding geometric network might differ slightly. The shortest and safest paths to the exit door or windows where the fire ladders are located are computed and analyzed. As a result of 15 scenarios developed in this paper, it was found that at 87% of the cases, the safest paths for the emergency exit of the people at risk were longer than the shortest paths. This study has evaluated different scenarios for the shortest and safest paths using Dijkstra algorithm considering different origins and destination points in the 3D indoor environment to assist the rescue operations.

scholarly journals Algorithms for Finding Shortest Paths in Networks with Vertex Transfer Penalties

Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 269 ◽  
Author(s):  
Rhyd Lewis
Keyword(s):  
Waiting Times ◽  
Shortest Paths ◽  
Dijkstra’S Algorithm ◽  
Weighted Graphs ◽  
Worst Case ◽  
Sparse Graphs ◽  
Graph Expansion ◽  
Advantages And Disadvantages ◽  
Dijkstra's Algorithm ◽  
Dense Graphs

In this paper we review many of the well-known algorithms for solving the shortest path problem in edge-weighted graphs. We then focus on a variant of this problem in which additional penalties are incurred at the vertices. These penalties can be used to model things like waiting times at road junctions and delays due to transfers in public transport. The usual way of handling such penalties is through graph expansion. As an alternative, we propose two variants of Dijkstra’s algorithm that operate on the original, unexpanded graph. Analyses are then presented to gauge the relative advantages and disadvantages of these methods. Asymptotically, compared to using Dijkstra’s algorithm on expanded graphs, our first variant is faster for very sparse graphs but slower with dense graphs. In contrast, the second variant features identical worst-case run times.

scholarly journals Faster shortest paths in dense distance graphs, with applications

2018 ◽  
Vol 711 ◽  
pp. 11-35
Author(s):  
Shay Mozes ◽  
Yahav Nussbaum ◽  
Oren Weimann
Keyword(s):  
Shortest Paths ◽  
Distance Graphs

An Efficient K-Shortest Paths Based Routing Algorithm

2012 ◽  
Vol 532-533 ◽  
pp. 1775-1779
Author(s):  
Jian Lian ◽  
Yan Zhang ◽  
Cheng Jiang Li
Keyword(s):  
Computer Networks ◽  
Shortest Path ◽  
Traffic Engineering ◽  
Routing Algorithm ◽  
Shortest Paths ◽  
Routing Algorithms ◽  
Dijkstra Algorithm ◽  
Link State

We present an efficient K-shortest paths routing algorithm for computer networks. This Algorithm is based on enhancements to currently used link-state routing algorithms such as OSPF and IS-IS, which are only focusing on finding the shortest path route by adopting Dijkstra algorithm. Its desire effect to achieve is through the use of K-shortest paths algorighm, which has been implemented successfully in some fileds like traffic engineering. The correctness of this Algorithm is discussed at the same time as long as the comparison with OSPF.

scholarly journals Inferring propagation paths for sparsely observed perturbations on complex networks

2016 ◽  
Vol 2 (10) ◽  
pp. e1501638 ◽  
Author(s):  
Francesco Alessandro Massucci ◽  
Jonathan Wheeler ◽  
Raúl Beltrán-Debón ◽  
Jorge Joven ◽  
Marta Sales-Pardo ◽  
...  
Keyword(s):  
Social Sciences ◽  
Complex System ◽  
Complex Networks ◽  
Probabilistic Model ◽  
Belief Propagation ◽  
Shortest Paths ◽  
Single Observation ◽  
Perturbed System ◽  
The Social ◽  
Propagation Paths

In a complex system, perturbations propagate by following paths on the network of interactions among the system’s units. In contrast to what happens with the spreading of epidemics, observations of general perturbations are often very sparse in time (there is a single observation of the perturbed system) and in “space” (only a few perturbed and unperturbed units are observed). A major challenge in many areas, from biology to the social sciences, is to infer the propagation paths from observations of the effects of perturbation under these sparsity conditions. We address this problem and show that it is possible to go beyond the usual approach of using the shortest paths connecting the known perturbed nodes. Specifically, we show that a simple and general probabilistic model, which we solved using belief propagation, provides fast and accurate estimates of the probabilities of nodes being perturbed.

scholarly journals Positive interactions support complex networks

2017 ◽  
Author(s):  
Gianalberto Losapio ◽  
Marcelino de la Cruz ◽  
Adrián Escudero ◽  
Bernhard Schmid ◽  
Christian Schöb
Keyword(s):  
Complex Networks ◽  
Network Architecture ◽  
Biotic Interactions ◽  
Positive Interactions ◽  
Plant Interactions ◽  
Network Analyses ◽  
Environmental Perturbations ◽  
Alpine Plant Communities ◽  
The Face ◽  
Living Organisms

Ecologists have recognised the effects of biotic interactions on the spatial distribution of living organisms. Yet, the spatial structure of plant interaction networks in real-world ecosystems has remained elusive so far. Using spatial pattern and network analyses, we found that alpine plant communities are organised in spatially variable and complex networks. Specifically, the cohesiveness of complex networks is promoted by short-distance positive plant interactions. At fine spatial scale, where positive mutual interactions prevailed, networks were characterised by a large connected component. With increasing scale, when negative interactions took over, network architecture became more hierarchical with many detached components that show a network collapse. This study highlights the crucial role of positive interactions for maintaining species diversity and the resistance of communities in the face of environmental perturbations.

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