ML-SOR: Message routing using multi-layer social networks in opportunistic communications

Opportunistic networks are a generalization of DTNs in which disconnections are frequent and encounter patterns between mobile devices are unpredictable. In such scenarios, message routing is a fundamental issue. Social-based routing protocols usually exploit the social information extracted from the history of encounters between mobile devices to find an appropriate message relay. Protocols based on encounter history, however, take time to build up a knowledge database from which to take routing decisions. While contact information changes constantly and it takes time to identify strong social ties, other types of ties remain rather stable and could be exploited to augment available partial contact information. In this paper, we start defining a multi-layer social network model combining the social network detected through encounters with other social networks and investigate the relationship between these social network layers in terms of node centrality, community structure, tie strength and link prediction. The purpose of this analysis is to better understand user behavior in a multi-layered complex network combining online and offline social relationships. Then, we propose a novel opportunistic routing approach ML-SOR (Multi-layer Social Network based Routing) which extracts social network information from such a model to perform routing decisions. To select an effective forwarding node, ML-SOR measures the forwarding capability of a node when compared to an encountered node in terms of node centrality, tie strength and link prediction. Trace driven simulations show that a routing metric combining social information extracted from multiple social network layers allows users to achieve good routing performance with low overhead cost.     

Long-run urban dynamics: understanding local housing market change in London

Abstract

Recently, a literature has emerged using empirical techniques to study the evolution of international cities over many centuries; however, few studies examine long-run change within cities. Conventional models and concepts are not always appropriate and data issues make long-run neighbourhood analysis particularly problematic. This paper addresses some of these points. First, it discusses why the analysis of long-run urban change is important for modern urban policy and considers the most important concepts. Second, it constructs a novel data set at the micro level, which allows consistent comparisons of London neighbourhoods in 1881 and 2001. Third, the paper models some of the key factors that affected long-run change, including the role of housing. There is evidence that the relative social positions of local urban areas persist over time but, nevertheless, at fine spatial scales, local areas still exhibit change, arising from aggregate population dynamics, from advances in technology, and also from the effects of shocks, such as wars. In general, where small areas are considered, long-run changes are likely to be greater, because individuals are more mobile over short than long distances. Finally, the paper considers the implications for policy.     

A novel approach for efficient stance detection in online social networks with metaheuristic optimization

In the 19th and 20th centuries, social networks have been an important topic in a wide range of fields from sociology to education. However, with the advances in computer technology in the 21st century, significant changes have been observed in social networks, and conventional networks have evolved into online social networks. The size of these networks, along with the large amount of data they generate, has introduced new social networking problems and solutions. Social network analysis methods are used to understand social network data. Today, several methods are implemented to solve various social network analysis problems, albeit with limited success in certain problems. Thus, the researchers develop new methods or recommend solutions to improve the performance of the existing methods. In the present paper, a novel optimization method that aimed to classify social network analysis problems was proposed. The problem of stance detection, an online social network analysis problem, was first tackled as an optimization problem. Furthermore, a new hybrid metaheuristic optimization algorithm was proposed for the first time in the current study, and the algorithm was compared with various methods. The analysis of the findings obtained with accuracy, precision, recall, and F-measure classification metrics demonstrated that our method performed better than other methods.     

社区居家养老服务设施的规划配置研究

社区和居家养老是我国当前和未来的主要养老模式，然而，与机构养老设施相比较而言，我国社区居家养老服务设施的发展明显滞后，具体表现为设施的建成基础薄弱、空间资源的配置要素模糊、规范和管控手段缺位等。在概述现状与问题的基础上，系统地探讨了我国社区层面养老服务资源的配置主体、配置对象和配置思路。针对养老服务需求和供给主体日益多元化的挑战，提出结合社区实际情况灵活配置服务模块的建议，采用总量控制和复合设置的规划手段落实配建，从而实现服务设施综合效益最大化的建设目标。     

Digital inclusion of people with disabilities: a qualitative study of intra-disability diversity in the digital realm

ABSTRACT

This paper presents an in-depth study of intra-disability diversity in the digital realm and the related role of individuality and selectiveness in the digital choices and experiences of people with disabilities (PwD). The study adopts the interactionist model of disability and problematises conceptual uniformity in research that focuses on the medical and socially-constructed features of disability as those determining digital constraints and affordances for PwD. Through primary qualitative evidence, it argues that individuality and selectiveness shape a complex terrain of intra-disability diversity, which forms the nuances and experiences of digital inclusion for PwD. Thus, it invites researchers to move beyond disability-fixed categorisations and offers a 3-tier recommendation for future research to explain the range of PwD’ perceptions and experiences in the digital realm.     

基于LBSN动态异构网络的时间感知兴趣点推荐

随着基于位置社交网络(Location-Based Social Network,LBSN)的快速发展,兴趣点(Point-Of-Interest,POI)推荐可以帮助人们发现有趣的并吸引人的位置。针对签到数据的稀疏性和用户兴趣的动态性等挑战性问题,提出了基于LBSN动态异构网络的时间感知兴趣点推荐算法。在LBSN异构网络模式中增加会话节点类型。通过动态元路径,在用户和兴趣点语义关系之间有效地融入时间信息、位置信息和社交信息等。设置了用户-兴趣点之间的动态元路径集,并提出了动态路径实例的偏好度计算方法。采用矩阵分解模型对不同动态偏好矩阵进行矩阵分解。根据不同动态元路径的用户特征矩阵和兴趣点特征矩阵,获取用户在目标时间访问兴趣点的推荐列表。实验结果表明,与其他兴趣点推荐方法相比,所提方法在兴趣点推荐精确度上取得了较好的推荐结果,具有良好的应用前景。     

Next POI Recommendation Based on Location Interest Mining with Recurrent Neural Networks

Journal of Computer Science and Technology - In mobile social networks, next point-of-interest (POI) recommendation is a very important function that can provide personalized location-based...     

‘Breaking’ news: uncovering sense-breaking patterns in social media crisis communication during the 2017 Manchester bombing

ABSTRACT

Individuals, (media-) organisations, and crisis responders who are involved in ad hoc crisis communication steadily deploy social media to contribute to collective sense-making as an endeavour to create meaning in highly uncertain situations. Exerting sense-giving in order to shape others’ conceptions is causally preceded by an initial breakup of existing understanding. This study aims to explore patterns of sense-breaking in social media crisis communication and its impact on collective sense-making and sense-giving. To this end, we conducted a case study of the Manchester bombing in 2017, including a social network analysis of 708,147 Twitter postings and a content analysis of 2006 original tweets. We found individual role types to be initiators of sense-breaking in early crisis stages when uncertainty is at its height. Exerting successive sense-giving becomes more challenging if the collective sense-making has progressed along with the sequence of events. This understanding aims to encourage emergency management organisations to move their sense-giving actions closer to the point in time when sense-breaking occurs.     

基于农产品品牌社会责任的消费者智能消费推荐研究

为了在竞争日益激烈的市场中提高竞争力,达到个性化销售目的,创造更大品牌价值,提出一种基于农产品品牌社会责任的消费者智能消费推荐研究。实验利用离散函数及品牌认可度描述推荐结果,分析推荐结果指数,证明基于社会责任的智能推荐研究令消费者认可度更高,与文献方法比较推荐结果有更高的精确度,设计的推荐模型可实现农产品个性销售目的,令农产品有更大的发展空间。     

创建和发展自然灾害摩擦学

回顾了自然灾害摩擦学孕育的历史背景，阐明其定义、研究对象、社会功能和主要任务。介绍这一新领域研究的主要进展，并提出今后的研究方向。