GatherTweet: A Python Package for Collecting Social Media Data on Online Events

Social media plays a crucial role in the organization of massive social movements. However, the sheer quantity of data generated by the events as well as the data collection restrictions that researchers encounter, leads to a series of challenges for researchers who want to analyze dynamic public discourse and opinion in response to and in the creation of world events. In this paper we present gatherTweet, a Python package that helps researchers efficiently collect social media data for events that are composed of many decentralized actions (across both space and time). The package is useful for studies that require analysis of the organizational or baseline messaging before an action, the action itself, and the effects of the action on subsequent public discourse. By capturing these aspects of world events gatherTweet enables the study of events and actions like protests, natural disasters, and elections.

Tackling Key Technological Problems and Advancing 5G Technology Application in Railway

The paper summarizes the development of mobile communication of domestic and foreign railways,and proposes the priorities for tackling key technological problems of railway 5G private network according to the technical routes of railway next-generation mobile communication determined by China State Railway Group Co.,Ltd.From the aspects of work objectives,principles,technical routes and innovative working methods,the paper elaborates the ideas of railway 5G scientific and technological research,puts forward the contents and plans of scientific and technological research on railway 5G private network,systematically organizes the achievements in the scientific and technological research stage of railway 5G private network,and sets forth the key contents of next-step scientific and technological research.

Network science approach for identifying disruptive elements of an airline

Currently,flight delays are common and they propagate from an originating flight to connecting flights,leading to large disruptions in the overall schedule.These disruptions cause massive economic losses,affect airlines’reputations,waste passengers’time and money,and directly impact the environment.This study adopts a network science approach for solving the delay propagation problem by modeling and analyzing the flight schedules and historical operational data of an airline.We aim to determine the most disruptive airports,flights,flightconnections,and connection types in an airline network.Disruptive elements are influential or critical entities in an airline network.They are the elements that can either cause(airline schedules)or have caused(historical data)the largest disturbances in the network.An airline can improve its operations by avoiding delays caused by the most disruptive elements.The proposed network science approach for disruptive element analysis was validated using a case study of an operating airline.The analysis indicates that potential disruptive elements in a schedule of an airline are also actual disruptive elements in the historical data and they should be considered to improve operations.The airline network exhibits small-world effects and delays can propagate to any part of the network with a minimum of four delayed flights.Finally,we observed that passenger connections between flights are the most disruptive connection type.Therefore,the proposed methodology provides a tool for airlines to build robust flight schedules that reduce delays and propagation.

Infrastructure of Scientometrics: The Big and Network Picture

A network is a set of nodes connected via edges,with possibly directions and weights on the edges.Sometimes,in a multi-layer network,the nodes can also be heterogeneous.In this perspective,based on previous studies,we argue that networks can be regarded as the infrastructure of scientometrics in the sense that networks can be used to represent scientometric data.Then the task of answering various scientometric questions related to this data becomes an algorithmic problem in the corresponding network.

Small World Effects in a Harmonious Unifying Hybrid Preferential Model Networks

Small worm effects in the harmonious unifying hybrid preferential model （HUHPM） networks are studied both numerically and analytically. The idea and method of the HUHPM is applied to three typical examples of unweighted BA model, weighted BBV model, and the TDE rnodel, so-called HUHPM-BA, HUHPM-BBV and HUHPM- TDE networks. Comparing the HUHPM with current typical models above, it is found that the HUHPM networks has the smallest average path length and the biggest average clustering coefficient. The results demonstrate that the HUHPM is more suitable not only for the un-iveighted models but also for the weighted models.

Network Approaches to Substance Use and HIV/Hepatitis C Risk among Homeless Youth and Adult Women in the United States: A Review

During the United States economic recession of 2008-2011, the number of homeless and unstably housed people in the United States increased considerably. Homeless adult women and unaccompanied homeless youth make up the most marginal segments of this population. Because homeless individuals are a hard to reach population, research into these marginal groups has traditionally been a challenge for researchers interested in substance abuse and mental health. Network analysis techniques and research strategies offer means for dealing with traditional challenges such as missing sampling frames, variation in definitions of homelessness and study inclusion criteria, and enumeration/population estimation procedures. This review focuses on the need for, and recent steps toward, solutions to these problems that involve network science strategies for data collection and analysis. Research from a range of fields is reviewed and organized according to a new stress process framework aimed at understanding how homeless status interacts with issues related to substance abuse and mental health. Three types of network innovation are discussed: network scale-up methods, a network ecology approach to social resources, and the integration of network variables into the proposed stress process model of homeless substance abuse and mental health. By employing network methods and integrating these methods into existing models, research on homeless and unstably housed women and unaccompanied young people can address existing research challenges and promote more effective intervention and care programs.

Empirical topological investigation of practical supply chains based on complex networks

The industrial supply chain networks basically capture the circulation of social resource, dominating the stability and efficiency of the industrial system. In this paper, we provide an empirical study of the topology of smartphone supply chain network. The supply chain network is constructed using open online data. Our experimental results show that the smartphone supply chain network has small-world feature with scale-free degree distribution, in which a few high degree nodes play a key role in the function and can effectively reduce the communication cost. We also detect the community structure to find the basic functional unit. It shows that information communication between nodes is crucial to improve the resource utilization. We should pay attention to the global resource configuration for such electronic production management.

Special Section of Tsinghua Science and Technology on Wireless Computing and Networking

The publication of Tsinghua Science and Technology was started in 1996. Since then, it has been an international academic journal sponsored by Tsinghua University and published bimonthly. This journal aims at presenting the state-of-art scientific achievements in computer science, and other IT fields, and is currently indexed by Ei and other abstracting indices. From year 2013, the journal will be available for open access through IEEE Xplore Digital Library. This year＇s special section on Wireless Computing and Networking of Tsinghua Science and Technology is devoted to gather and present new research that address the challenges in the broad areas of Wireless Networks, Sensor Networks, Wireless Computing and Communication. While Wireless Networks have great potential to provide heterogeneous access and services for ubiquitous users, the demanding communication environment of wireless networks imposes challenges to many interesting research topics, such as channel estimation, communication protocol design, resource management, system design and so on. In Wireless Network research, it is unavoidable to wrestle unique problems such as non-uniform spectrum allocation, various radio resource management policies, economic concerns, the scarcity of radio resources, and user mobility. This Special Section therefore aims to publish high quality, original, unpublished research papers in the broad area of Wireless Computing and Networking, and thus presents a platform for scientists and scholars to share their observations and research results in the field. Specific topics for this special section include but are not limited to：

Evolution of airports from a network perspective--An analytical concept

Analyzing airports＇ role in global air transportation and monitoring their development over time provides an additional perspective on the dynamics of network evolution.In order to understand the different roles airports can play in the network an integrated and multidimensional approach is needed.Therefore,an approach to airport classification through hierarchical clustering considering several parameters from network theory is presented in this paper.By applying a 29 year record of global flight data and calculating the conditional transition probabilities the results are displayed as an evolution graph similar to a discrete-time Markov chain.With this analytical concept the meaning of airports is analyzed from a network perspective and a new airport taxonomy is established.The presented methodology allows tracking the development of airports from certain categories into others over time.Results show that airports of equal classes run through similar stages of development with a limited number of alternatives,indicating clear evolutionary patterns.Apart from giving an overview of the results the paper illustrates the exact data-driven approach and suggests an evaluation scheme.The methodology can help the public and industry sector to make informed strategy decisions when it comes to air transportation infrastructure.

Identifying spreading influence nodes for social networks

The identification of spreading influence nodes in social networks,which studies how to detect important individuals in human society,has attracted increasing attention from physical and computer science,social science and economics communities.The identification algorithms of spreading influence nodes can be used to evaluate the spreading influence,describe the node’s position,and identify interaction centralities.This review summarizes the recent progress about the identification algorithms of spreading influence nodes from the viewpoint of social networks,emphasizing the contributions from physical perspectives and approaches,including the microstructure-based algorithms,community structure-based algorithms,macrostructure-based algorithms,and machine learning-based algorithms.We introduce diffusion models and performance evaluation metrics,and outline future challenges of the identification of spreading influence nodes.

HITS hits art

The blockchain art market is partitioned around the roles of artists and collectors and highly concentrated among a few prominent figures.We hence propose to adapt Kleinberg's authority/hub HITS(Hyperlink-Induced Topic Search)method to rate artists and collectors in the art context.This seems a reasonable choice since the original method deftly defines its scores in terms of a mutual recursive relationship between authorities/artists—the miners of information/art,and hubs/collectors—the assemblers of such information/art.We evaluated the proposed method on the collector-artist network of SuperRare gallery,the major crypto art marketplace.We found that the proposed artist and collector metrics are weakly correlated with other networks science metrics like degree and strength.This hints at the possibility of coupling different measures in order to profile active users of the gallery and suggests investment strategies with different risk/reward ratios for collectors as well as marketing strategies with different targets for artists.

Exploring link generation in a temporal evolution network for human mobility

The prevalence of mobile devices has spurred human mobility to be applied in mobile networking and communications by using network science, in which the temporal evolution of a network topology is of great importance for protocol design and performance analysis. This paper focuses on link generation in a temporal evolution network. Based on observations revealing the strong correlation between the connection patterns of different time periods, a link generation potential based on historical connections is proposed in this paper, aiming to provide a method for making topological predictions with less randomness. Using MIT Reality dataset, an evaluation of the accuracy of the proposed method was conducted. The experimental results demonstrate the proposal's adequacy in terms of its accuracy.