【参考】bike+需求影响

一、分类？

日后吧

1.1 Hail a cab or ride a bike? A travel time comparison of taxi and bicycle-sharing systems in New York City

• 摘要
  在本文中，我们检验了一个假设，即自行车可以在密集的城市地区与汽车在旅行时间上竞争。2014年，我们对纽约市出租车和自行车共享系统（BSS）观察到的出行时间差异进行了详细调查。识别起点和目的地靠近BSS车站的出租车出行，并与来自相同起点和目的地的BSS出行进行比较。旅行时间比较是沿着以下维度进行的：（a）所有旅行，（b）时间维度，包括一天中的不同时间段、工作日与周末、季节变化，以及（c）距离类别。研究发现，在工作日的上午、中午和下午，超过一半的OD对距离小于3km的情况下，BSS要么更快，要么与出租车模式竞争。为了进一步阐明行程时间比较，我们采用面板混合多项式logit模型形式的随机效用框架，进行了多变量分析。识别和理解影响出行时间差异的因素有助于规划者改进BSS服务。向喜欢骑自行车的人提供关于“更快”替代方案的信息，可以作为一种营销工具，在密集的城市核心区吸引更多的BSS使用。BSS和出租车的比较也可以揭示密集城市地区自行车和汽车模式之间的竞争。（C） 2017爱思唯尔有限公司版权所有。

• abstract
  In this paper, we examine the hypothesis that bicycles can compete with cars in terms of travel time in dense urban areas. We conduct a detailed investigation of the differences in observed travel times by taxi and a bicycle-sharing system (BSS) in New York City in 2014. The taxi trips with origins and destinations in proximity to BSS stations are identified and compared to BSS trips from the same origin and destinations. The travel time comparison is conducted along following dimensions: (a) all trips, (b) temporal dimension including different time periods of the day, weekday versus weekend, and seasonal variation, and © distance categories. It is found that during weekdays’ AM, Midday and PM time periods for more than half of OD pairs with distance less than 3 km, BSS is either faster or competitive with taxi mode. To further shed light on the travel time comparison, we develop a multivariate analysis using a random utility framework in the form of a panel mixed multi-nomial logit model. Identifying and understanding the factors that influence the travel time differences can help planners to enhance the BSS service offerings. The provision of information to bicycling-inclined individuals on the “faster” alternative could be used as a marketing tool to attract higher usage for BSS within dense urban cores. The comparison of BSS and taxi can also shed light on the competition between bicycle and car modes in general in dense urban areas. © 2017 Elsevier Ltd. All rights reserved.

• bib

@article{Faghih0Hail,
  title={Hail a cab or ride a bike? A travel time comparison of taxi and bicycle-sharing systems in New York City},
  author={Faghih-Imani and Ahmadreza and Anowar and Sabreena and Miller and  Eric, J.  and Eluru and Naveen},
}

1.2 The Structure of Spatial Networks and Communities in Bicycle Sharing Systems

文章

• 摘要

自行车共享系统存在于世界各地数百个城市，其目的是提供一种公共交通形式，具有自行车带来的相关健康和环境效益，而无需私人拥有和维护的负担。五个城市提供了关于自行车共享系统中发生的旅程（开始和结束时间和地点）的研究数据。在本文中，我们使用可视化、描述性统计、空间和网络分析工具来探索这些城市的系统使用情况，使用技术来调查每个城市独特地理位置的特定特征，并揭示不同系统之间的相似性。行程位移分析表明，抽样城市之间的行程距离相似，每个城市前50个展位的（外）强度等级曲线显示出相似的比例律。衍生网络中的社区检测可以识别局部使用区域，而空间网络校正提供了一个机会，可以洞察简单空间交互模型预测的接近度/受欢迎度之间的相关性。

• Abstract
  Bicycle sharing systems exist in hundreds of cities around the world, with the aim of providing a form of public transport with the associated health and environmental benefits of cycling without the burden of private ownership and maintenance. Five cities have provided research data on the journeys (start and end time and location) taking place in their bicycle sharing system. In this paper, we employ visualization, descriptive statistics and spatial and network analysis tools to explore system usage in these cities, using techniques to investigate features specific to the unique geographies of each, and uncovering similarities between different systems. Journey displacement analysis demonstrates similar journey distances across the cities sampled, and the (out)strength rank curve for the top 50 stands in each city displays a similar scaling law for each. Community detection in the derived network can identify local pockets of use, and spatial network corrections provide the opportunity for insight above and beyond proximity/popularity correlations predicted by simple spatial interaction models.
• bib

@article{2013The,
  title={The Structure of Spatial Networks and Communities in Bicycle Sharing Systems},
  author={ Austwick, M. Z.  and O O’Brien and  Strano, E.  and  Viana, M. },
  journal={PLoS ONE},
  year={2013},
}

1.3 Bicycling renaissance in North America? An update and re-appraisal of cycling trends and policies

• 摘要
  本文回顾了过去20年来加拿大和美国自行车水平、安全和政策的发展趋势。我们分析了这两个国家的总体数据以及城市九个大城市（芝加哥、明尼阿波利斯、蒙特利尔、纽约、波特兰、旧金山、多伦多、温哥华和华盛顿）的具体案例研究数据。美国和加拿大的自行车水平都有所上升，而骑车者的死亡人数有所下降。骑自行车的速度存在很大的空间差异和社会经济不平等。加拿大上班族中自行车的比例是美国的两倍多，在这两个国家的西部地区都更高。自行车主要集中在中心城市，尤其是大学附近和市中心附近的贵族社区。在美国，自行车运动几乎所有的增长都发生在25-64岁的男性中，而女性的自行车运动率保持稳定，儿童的自行车运动率急剧下降。这九个案例研究城市的自行车骑行率增长速度远快于整个国家，自1990年以来，所有城市的自行车骑行率至少翻了一番。他们实施了广泛的基础设施和项目，以促进自行车运动和提高自行车安全性：扩大和改善自行车道和道路、交通平静、停车、自行车交通整合、自行车共享、培训项目和宣传活动。我们描述了九个案例研究城市的具体成就，重点介绍了每个城市的创新和其他试图增加自行车运动的城市的经验教训。波特兰全面的自行车政策已经成功地将自行车水平提高了6倍，并提供了一个其他北美城市可以效仿的例子。
• Abstract
  This paper reviews trends in cycling levels, safety, and policies in Canada and the USA over the past two decades. We analyze aggregate data for the two countries as well as city-specific case study data for nine large cities (Chicago, Minneapolis, Montréal, New York, Portland, San Francisco, Toronto, Vancouver, and Washington). Cycling levels have increased in both the USA and Canada, while cyclist fatalities have fallen. There is much spatial variation and socioeconomic inequality in cycling rates. The bike share of work commuters is more than twice as high in Canada as in the USA, and is higher in the western parts of both countries. Cycling is concentrated in central cities, especially near universities and in gentrified neighborhoods near the city center. Almost all the growth in cycling in the USA has been among men between 25–64 years old, while cycling rates have remained steady among women and fallen sharply for children. Cycling rates have risen much faster in the nine case study cities than in their countries as a whole, at least doubling in all the cities since 1990. They have implemented a wide range of infrastructure and programs to promote cycling and increase cycling safety: expanded and improved bike lanes and paths, traffic calming, parking, bike-transit integration, bike sharing, training programs, and promotional events. We describe the specific accomplishments of the nine case study cities, focusing on each city’s innovations and lessons for other cities trying to increase cycling. Portland’s comprehensive package of cycling policies has succeeded in raising cycling levels 6-fold and provides an example that other North American cities can follow.
• bib

@article{2011Bicycling,
  title={Bicycling renaissance in North America? An update and re-appraisal of cycling trends and policies},
  author={ Pucher, J.  and  Buehler, R.  and  Seinen, M. },
  journal={Transportation Research Part A},
  volume={45},
  number={6},
  pages={451-475},
  year={2011},
}

1.4 Research on the Demand Forecast Model of Public Bike Dynamic Scheduling System

• 摘要
  基于出行理论，将公共自行车需求分为直接需求和间接需求，研究了需求的等待、转移和回归变化规律。根据各出租站的历史出行OD和以往的借还信息，确定公共自行车的调度数量和调度计划，建立出租站的短期多时段需求预测模型。将需求预测模型和调度模型结合起来，形成具有迭代反馈特性的双层模型。通过计算机仿真对模型进行求解，得到最优方案的需求预测结果，并通过以下实例进行验证。结果表明，两种组合模型能有效改善公共自行车动态调度的状况。
• Abstract
  Based on the travelling theory, the demand of public bike is divided into direct and indirect parts and the change rule how the demand waits, transfers and regresses is studied. Depending on all rental stations’ historical trip OD and previous borrow-and-return information, the dispatching number and scheduling plan of public bike are determined and then a short-term multi-period demand forecast model for rental stations is built. The demand forecasting model and scheduling model are combined to form a bi-level model with iterative feedback characteristics. Computer simulation is used to solve the model, aiming to obtain the demand forecast results of the optimal plan, which can be verified by the following example. The results show that the two combined models can effectively improve the situation of public bike dynamic scheduling.
• bib

@article{2013Research,
  title={Research on the Demand Forecast Model of Public Bike Dynamic Scheduling System},
  author={ He, L.  and  Li, X.  and  Chen, D.  and  Lu, J.  and  Wu, Y. },
  journal={Journal of Wuhan University of Technology(Transportation Science & Engineering)},
  year={2013},
}

1.5Station Site Optimization in Bike Sharing Systems

ACM原文

• 摘要
  旨在提供公共交通系统中缺失环节的自行车共享系统在城市中越来越流行。在一个理想的自行车共享网络中，车站位置的选择通常是在车站之间实现上下车平衡。这有助于避免昂贵的重新平衡操作，并保持较高的用户满意度。然而，开发这样一个具有适当车站位置的高效自行车共享系统是一项具有挑战性的任务。事实上，自行车站的需求受到周边环境和复杂公共交通网络的多种因素的影响。通过考虑所有这些因素，开发自行车共享系统的需求和平衡预测模型的努力有限。为此，在本文中，我们提出了一种考虑多个影响因素的自行车共享网络优化方法。目标是通过选择正确的车站位置来提高自行车共享服务的质量和效率。沿着这条路线，我们首先从人类移动数据、兴趣点（POI）以及站点网络结构中提取细粒度鉴别特征。然后，开发了基于人工神经网络（ANN）的预测模型，用于预测站点需求和平衡。此外，基于学习到的站点需求和平衡模式，建立了一个基于遗传算法的优化模型，从大量候选站点中选择一组站点，从而最大限度地提高站点利用率，减少不平衡站点的数量。最后，在纽约市CitiBike共享系统上进行的大量实验结果表明，我们的方法在自行车使用量以及所需的重新平衡工作方面优化了站点分配。
• ABSTRACT
  Bike sharing systems, aiming at providing the missing links in the public transportation systems, are becoming popular in urban cities. In an ideal bike sharing network, the station locations are usually selected in a way that there are balanced pick-ups and drop-offs among stations. This can help avoid expensive re-balancing operations and maintain high user satisfaction. However, it is a challenging task to develop such an efficient bike sharing system with appropriate station locations. Indeed, the bike station demand is influenced by multiple factors of surrounding environment and complex public transportation networks. Limited efforts have been made to develop demand-and-balance prediction models for bike sharing systems by considering all these factors. To this end, in this paper, we propose a bike sharing network optimization approach by considering multiple influential factors. The goal is to enhance the quality and efficiency of the bike sharing service by selecting the right station locations. Along this line, we first extract fine-grained discriminative features from human mobility data, point of interests (POI), as well as station network structures. Then, prediction models based on Artificial Neural Networks (ANN) are developed for predicting station demand and balance. In addition, based on the learned patterns of station demand and balance, a genetic algorithm based optimization model is built to choose a set of stations from a large number of candidates in a way such that the station usage is maximized and the number of unbalanced stations is minimized. Finally, the extensive experimental results on the NYC CitiBike sharing system show the advantages of our approach for optimizing the station site allocation in terms of the bike usage as well as the required re-balancing efforts.

1.6 ⭐️Effects of built environment and weather on bike sharing demand: a station level analysis of commercial bike sharing in Toronto

文章

• 摘要
  自行车共享多伦多是加拿大第二大公共自行车共享系统。它提供了一个独特的案例研究，因为它是位于相对寒冷的北美环境中为数不多的自行车共享项目之一，但全年都在运行。本研究利用全年的历史出行数据，分析了影响多伦多自行车共享率的因素。全面的空间分析为社会人口特征、土地利用和建筑环境以及不同天气措施对自行车共享乘客量的影响提供了有意义的见解。经验模型还揭示了道路网络配置（交叉口密度和车站空间分散）对自行车共享需求的显著影响。自行车基础设施（自行车道、道路等）的影响也被发现对增加自行车共享需求至关重要。使用多层次框架研究了自行车共享出行行为的时间变化。这项研究揭示了温度、土地使用和自行车共享出行活动之间的显著相关性。这篇论文的发现可以转化为指南，目的是增加城市中心的自行车共享活动。
• abstract
  Bike Share Toronto is Canada’s second largest public bike share system. It provides a unique case study as it is one of the few bike share programs located in a relatively cold North American setting, yet operates throughout the entire year. Using year-round historical trip data, this study analyzes the factors affecting Toronto’s bike share ridership. A comprehensive spatial analysis provides meaningful insights on the influences of socio-demographic attributes, land use and built environment, as well as different weather measures on bike share ridership. Empirical models also reveal significant effects of road network configuration (intersection density and spatial dispersion of stations) on bike sharing demands. The effect of bike infrastructure (bike lane, paths etc.) is also found to be crucial in increasing bike sharing demand. Temporal changes in bike share trip making behavior were also investigated using a multilevel framework. The study reveals a significant correlation between temperature, land use and bike share trip activity. The findings of the paper can be translated to guidelines with the aim of increasing bike share activity in urban centers.
• bib

@article{2017Effects,
  title={Effects of built environment and weather on bike sharing demand: a station level analysis of commercial bike sharing in Toronto},
  author={ El-Assi, W.  and  Mahmoud, M. S.  and  Habib, K. N. },
  journal={Transportation},
  volume={44},
  number={3},
  pages={589-613},
  year={2017},
}

1.7 Defining a Primary Market and Estimating Demand for Major Bicycle-Sharing Program in Philadelphia, Pennsylvania

• 文章

• 摘要
  近年来，公共自行车共享（bike share）项目越来越受欢迎，尤其是在欧洲，一些城市最近实施了系统，使用率很高。到目前为止，北美的努力更加有限，最近一些引人注目的例子包括华盛顿特区的一个小型项目和加拿大蒙特利尔的一个重大季节性项目。由于美国没有既定的大型项目，探索潜在系统设计和可行性的规划者面临着一种不同寻常的不确定性，即谁会骑、他们会骑在哪里以及他们会骑多久。宾夕法尼亚州费城正在考虑建立一个大规模的自行车共享系统。本文讨论了一个两阶段项目的方法和结果，该项目（a）使用基于栅格的地理信息系统分析来确定自行车共享计划的主要地理市场区域，以及（b）应用在欧洲同行城市观察到的自行车共享出行分流率来估计主要市场区域的每日自行车共享出行。该分析得出了费城每日使用量的估计值，在两种市场面积和三种需求情景（低、中、高）下，其范围约为6000到23000。随着自行车共享系统在不同环境下的不断普及，新数据可以改进这里使用的方法，以在未来提供更高水平的确定性。

• abstract
  Public bicycle-sharing (bike share) programs have become increasingly popular in recent years, particularly in Europe, with a number of cities recently implementing systems and high levels of usage. North American efforts have been more limited to date, with high-profile recent examples including a small program in Washington, D.C., and a substantial seasonal program in Montreal, Canada. Because there are no established large-scale programs in the United States, planners exploring potential system designs and feasibilities are faced with an unusual degree of uncertainty about who would ride, where they might ride, and how often they might ride. A large-scale bike share system is under consideration in Philadelphia, Pennsylvania. This paper discusses the methods and findings of a two-phase project that (a) used a raster-based geographic information system analysis to identify a primary geographic market area for a bike share program and (b) applied bike share trip diversion rates observed in peer European cities to estimate daily bike share trips in the primary market area. This analysis resulted in estimates for daily usage in Philadelphia that ranged from roughly 6,000 to 23,000 for two scales of market area and three demand scenarios (low, middle, and high). As bike share systems continue to proliferate in different settings, new data can refine the methods used here to provide increasing levels of certainty in the future.

• bib

@article{article,
author = {Krykewycz, Gregory and Puchalsky, Christopher and Rocks, Joshua and Bonnette, Brittany and Jaskiewicz, Frank},
year = {2010},
month = {12},
pages = {},
title = {Defining a Primary Market and Estimating Demand for Major Bicycle-Sharing Program in Philadelphia, Pennsylvania},
volume = {2143},
journal = {Transportation Research Record: Journal of the Transportation Research Board},
doi = {10.3141/2143-15}
}

1.8 Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system

• 摘要
  本文利用巴塞罗那社区自行车项目Bicing站点的可用自行车数量，对城市地区的人类流动数据进行了分析。根据从运营商网站上采集的数据，可以检测城市内的时间和地理流动模式。这些模式用于预测未来几分钟/小时内任何车站的可用自行车数量。这些预测可以用来改进自行车项目，以及通过Bicing网站向用户提供的信息。

• abstract
  This paper provides an analysis of human mobility data in an urban area using the amount of available bikes in the stations of the community bicycle program Bicing in Barcelona. Based on data sampled from the operator’s website, it is possible to detect temporal and geographic mobility patterns within the city. These patterns are applied to predict the number of available bikes for any station some minutes/hours ahead. The predictions could be used to improve the bicycle program and the information given to the users via the Bicing website.

• bib

@article{2010Urban,
  title={Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system},
  author={ Kaltenbrunner, Andreas  and  Meza, Rodrigo  and  Grivolla, Jens  and  Codina, Joan  and  Banchs, Rafael },
  journal={Pervasive and Mobile Computing},
  volume={6},
  number={4},
  pages={455-466},
  year={2010},
}

1.9 Research on Urban Public Bicycle Scheduling Based on Multi Objective Optimization Model

• 摘要
  为了提高公共自行车的使用率，应合理安排净租金制度。在预先考虑借还频度随时间变化的前提下，确定单状态调度需求，划分调度区域，建立了以满足度最大、成本最小为目标的次日高峰需求调度模型。并提出了一种将模拟退火算法与遗传算法相结合的算法来求解该模型。本文以浙江省温州市鹿城区86个监测站为样本进行了实证分析。结果表明，该模型和算法是有效的。
• abstract
  To enhance the usagerate of public bicycle, the Net-Rent system should be reasonable scheduled has. Under the premises of precllcting borrow-repay frequency distribution over time, determine single-state schedluing demand and divide the scheduling area a scheduling model based on the objective of maximized satisfaction and minimized cost is established for the peak demand of the next day. And proposed an algorithm with simulated annealing algorithm integrated into genetic algorithm to solve the model. This paper makes a empirical analysis based on 86 stations in Lucheng district(Wenzhou City, Zhejiang province). The result shows that the model and algorithm proposed are effective.
• bib

@article{2017Research,
  title={Research on Urban Public Bicycle Scheduling Based on Multi Objective Optimization Model},
  author={ Chen, X.  and  Jiang, Y.  and  Li, M.  and  Ke, X. },
  journal={Modern Transportation Technology},
  year={2017},
}

1.10 Operational Redistribution Model for a Large-Scale Bicycle-Sharing System: Case Study in China

• 摘要
  近年来，自行车共享系统（BSS）在世界各地的许多城市，尤其是发展中国家越来越流行。然而，一个重大的运营问题是自行车分布的不平衡，尤其是在高峰时段的大规模BSS中。这个问题可能会显著降低服务水平和潜在用户数量。为了提高BSSs的服务水平，分析了再分配的必要性，并开发了一个能够处理大规模再分配的可操作再分配模型（ORM）。ORM的目标是最小化BSS的广义运营成本，即未服务用户需求的惩罚成本和自行车重新分配的成本。在几种情况下分析了系统的总体性能。结果表明，ORM可以有效地提高BSS的服务水平，并为每个再分配卡车提供详细的工作计划。对于大规模BSS中的再分配，分区对于以相对较低的总体成本实现高水平的服务非常重要。此外，通过基于场景的优化过程，可以找到最佳数量的子区域。
• abstract
  In recent years, bicycle-sharing systems (BSSs) have been getting more and more popular in many cities all over the world, particularly in developing countries. However, a significant operating problem was the imbalance that occurred in the distribution of bicycles, especially in large-scale BSSs during peak hours. This problem could significantly reduce the level of service and number of potential users. To improve the level of service of BSSs, the necessity of redistribution was analyzed, and an operational redistribution model (ORM) that could deal with large-scale redistribution was developed. The objective of the ORM was to minimize the generalized operation costs of BSSs, which were the penalty cost of unserved user demand and the cost of redistribution of bicycles. The overall system performance was analyzed under several scenarios. The results demonstrated that an ORM could effectively improve the level of service of a BSS and could provide a detailed work plan for each redistribution truck to implement. For redistribution in a large-scale BSS, the partition of subzones was important to achieve a high level of service with relatively low generalized costs. In addition, an optimal number of subzones could be found through the scenario-based optimization process.
• bib

@inproceedings{2015Operational,
  title={Operational Redistribution Model for a Large-Scale Bicycle-Sharing System: Case Study in China},
  author={ Song, M.  and  Li, M.  and  Zou, M. },
  booktitle={Transportation Research Board Meeting},
  year={2015},
}

1.11 Models for Effective Deployment and Redistribution of Bicycles Within Public Bicycle-Sharing Systems

• 摘要
  我们开发了实用的运筹学模型，以支持公共自行车共享系统的设计和管理决策。我们开发了一个具有比例约束的网络流量模型，以估计网络内的自行车流量和支持的出行次数，前提是每个站点的自行车初始分配。我们还研究了在网络中定期重新分配自行车以支持更大流量的有效性，以及对所需码头数量的影响。我们使用来自新加坡火车运营商的交通数据进行数值分析。考虑到火车系统中有相当大比例的乘客在短途通勤——超过16%的乘客在距离起点两站以内下车——这就形成了对自行车共享计划的潜在需求。我们认为，为了让自行车共享系统对这一客户群体最有效，系统必须在正确的地点部署正确数量的自行车，因为这会影响自行车的利用率以及自行车在系统内的流通方式。我们还确定了适当的操作环境，在这些环境中，自行车的定期重新分配对于提高系统性能最为有效。
• abstract
  We develop practical operations research models to support decision making in the design and management of public bicycle-sharing systems. We develop a network flow model with proportionality constraints to estimate the flow of bicycles within the network and the number of trips supported, given an initial allocation of bicycles at each station. We also examine the effectiveness of periodic redistribution of bicycles in the network to support greater flow, and the impact on the number of docks needed.We conduct our numerical analysis using transit data from train operators in Singapore. Given that a substantial proportion of passengers in the train system commute a short distance—more than 16% of passengers alight within two stops from the origin—this forms a latent segment of demand for a bicycle-sharing program. We argue that for a bicycle-sharing system to be most effective for this customer segment, the system must deploy the right number of bicycles at the right places, because this affects the utilization rate of the bicycles and how bicycles circulate within the system. We also identify the appropriate operational environments in which periodic redistribution of bicycles will be most effective for improving system performance.
• bib

@article{10.1287/opre.2013.1215,
title = {Models for Effective Deployment and Redistribution of Bicycles Within Public Bicycle-Sharing Systems},
year = {2013},
issue_date = {December 2013},
publisher = {INFORMS},
address = {Linthicum, MD, USA},
volume = {61},
number = {6},
issn = {0030-364X},
journal = {Oper. Res.},
month = {dec},
pages = {1346–1359},
numpages = {14},
}

1.12Effect of node attributes on the temporal dynamics of network structure

摘要

许多自然和社会网络随着时间的推移而演变，它们的结构是动态的。在大多数网络中，节点是异构的，它们在结构演化中的角色不同。本文主要研究个体属性在网络结构时间动态中的作用。我们关注的是一个包含节点属性（我们称之为“质量”）的增长网络的基本模型，我们关注的是预测任意初始网络在任意时间的网络结构属性的问题。也就是说，我们解决了以下问题：如果给定一个具有给定任意结构和已知节点属性的初始网络，那么当具有给定属性分布的新节点加入网络时，该结构如何随时间变化？我们对模型进行了解析求解，得到了质量度的联合分布和度相关性。我们描述了各个属性在连接层次中各个节点的位置中的作用。我们用蒙特卡罗模拟证实了理论结果。

• Abstract
  Many natural and social networks evolve in time and their structures are dynamic. In most networks, nodes are heterogeneous, and their roles in the evolution of structure differ. This paper focuses on the role of individual attributes on the temporal dynamics of network structure. We focus on a basic model for growing networks that incorporates node attributes (which we call “quality”), and we focus on the problem of forecasting the structural properties of the network in arbitrary times for an arbitrary initial network. That is, we address the following question: If we are given a certain initial network with given arbitrary structure and known node attributes, then how does the structure change in time as new nodes with given distribution of attributes join the network? We solve the model analytically and obtain the quality-degree joint distribution and degree correlations. We characterize the role of individual attributes in the position of individual nodes in the hierarchy of connections. We confirm the theoretical findings with Monte Carlo simulations.
• bib

@article{PhysRevE.95.032304,
  title = {Effect of node attributes on the temporal dynamics of network structure},
  author = {Momeni, Naghmeh and Fotouhi, Babak},
  journal = {Phys. Rev. E},
  volume = {95},
  issue = {3},
  pages = {032304},
  numpages = {20},
  year = {2017},
  month = {Mar},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevE.95.032304},
  url = {https://link.aps.org/doi/10.1103/PhysRevE.95.032304}
}