اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدA weighted network evolution model based on passenger behavior
166
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
This paper presents an evolution model of weighted networks in which the structural growth and weight dynamics are driven by human behavior, i.e. passenger route choice behavior. Transportation networks grow due to peoples increasing travel demand and the pattern of growth is determined by their route choice behavior. In airline networks passengers often transfer from a third airport instead of flying directly to the destination, which contributes to the hubs formation and finally the scale-free statistical property. In this model we assume at each time step there emerges a new node with m travel destinations. Then the new node either connects destination directly with the probability p or transfers from a third node with the probability 1-p. The analytical result shows degree and strength both obey power-law distribution with the exponent between 2.33 and 3 depending on p. The weights also obey power-law distribution. The clustering coefficient, degree assortatively coefficient and degree-strength correlation are all dependent on the probability p. This model can also be used in social networks.
قيم البحث
اقرأ أيضاً
Inspired by studies on airline networks we propose a general model for weighted networks in which topological growth and weight dynamics are both determined by cost adversarial mechanism. Since transportation networks are designed and operated with o
bjectives to reduce cost, the theory of cost in micro-economics plays a critical role in the evolution. We assume vertices and edges are given cost functions according to economics of scale and diseconomics of scale (congestion effect). With different cost functions the model produces broad distribution of networks. The model reproduces key properties of real airline networks: truncated degree distributions, nonlinear strength degree correlations, hierarchy structures, and particulary the disassortative and assortative behavior observed in different airline networks. The result suggests that the interplay between economics of scale and diseconomics of scale is a key ingredient in order to understand the underlying driving factor of the real-world weighted networks.
From the macroscopic viewpoint for describing the acceleration behavior of drivers, this letter presents a weighted probabilistic cellular automaton model (the WP model, for short) by introducing a kind of random acceleration probabilistic distributi
on function. The fundamental diagrams, the spatio-temporal pattern are analyzed in detail. It is shown that the presented model leads to the results consistent with the empirical data rather well, nonlinear velocity-density relationship exists in lower density region, and a new kind of traffic phenomenon called neo-synchronized flow is resulted. Furthermore, we give the criterion for distinguishing the high-speed and low-speed neo-synchronized flows and clarify the mechanism of this kind of traffic phenomena. In addition, the result that the time evolution of distribution of headways is displayed as a normal distribution further validates the reasonability of the neo-synchronized flow. These findings suggest that the diversity and randomicity of drivers and vehicles has indeed remarkable effect on traffic dynamics.
Networks representing complex systems in nature and society usually involve multiple interaction types. These types suggest essential information on the interactions between components, but not all of the existing types are usually discovered. Theref
ore, detecting the undiscovered edge types is crucial for deepening our understanding of the network structure. Although previous studies have discussed the edge label detection problem, we still lack effective methods for uncovering previously-undetected edge types. Here, we develop an effective technique to detect undiscovered new edge types in networks by leveraging a novel temporal network model. Both analytical and numerical results show that the prediction accuracy of our method is perfect when the model networks time parameter approaches infinity. Furthermore, we find that when time is finite, our method is still significantly more accurate than the baseline.
Social impacts and degrees of organization inherent to opinion formation for interacting agents on networks present interesting questions of general interest from physics to sociology. We present a quantitative analysis of a case implying an evolving
small size network, i.e. that inherent to the ongoing debate between modern creationists (most are Intelligent Design (ID) proponents (IDP)) and Darwins theory of Evolution Defenders (DED)). This study is carried out by analyzing the structural properties of the citation network unfolded in the recent decades by publishing works belonging to members of the two communities. With the aim of capturing the dynamical aspects of the interaction between the IDP and DED groups, we focus on $two$ key quantities, namely, the {it degree of activity} of each group and the corresponding {it degree of impact} on the intellectual community at large. A representative measure of the former is provided by the {it rate of production of publications} (RPP), whilst the latter can be assimilated to the{it rate of increase in citations} (RIC). These quantities are determined, respectively, by the slope of the time series obtained for the number of publications accumulated per year and by the slope of a similar time series obtained for the corresponding citations. The results indicate that in this case, the dynamics can be seen as geared by triggered or damped competition. The network is a specific example of marked heterogeneity in exchange of information activity in and between the communities, particularly demonstrated through the nodes having a high connectivity degree, i.e. opinion leaders.
The one-mode projecting is extensively used to compress the bipartite networks. Since the one-mode projection is always less informative than the bipartite representation, a proper weighting method is required to better retain the original informatio
n. In this article, inspired by the network-based resource-allocation dynamics, we raise a weighting method, which can be directly applied in extracting the hidden information of networks, with remarkably better performance than the widely used global ranking method as well as collaborative filtering. This work not only provides a creditable method in compressing bipartite networks, but also highlights a possible way for the better solution of a long-standing challenge in modern information science: How to do personal recommendation?
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
