Social-physical Network Research Articles

Collaborative Diffusion Based on Value Measurement in Social–Physical Networks

In the study of information diffusion in social-physical networks, existing works are usually based on information entropy. These works measure and represent the information attribute characteristics independently for social networks and physical networks, resulting in ineffective interactions and waste of resources. Therefore, to solve the key problem of the mismatch between interaction demands and communication resources, the framework of collaborative diffusion based on value measurement is proposed in social-physical networks, including social-physical interaction, cognitive difference and mutual trust degree of nodes. Based on parameterizing the relative strength of these influences by confidence and collaborative conservation factors, the collaborative diffusion model based on value measurement is established. Extensive simulations verify the influence of value measurement on the collaboration diffusion process, presented by the evolutions of value entropy, sentiment fragmentation and diffusion range. In addition, the influence of collaboration on information dissemination is confirmed by the comparison of the change of value entropy and diffusion range. These results can help decision-makers better balance the matching problem between interaction demands and available resources, which is beneficial to realize customized information diffusion.

Social awareness of crisis events: A new perspective from social-physical network

The emergence of social media provides a rich source of posts to facilitate situational awareness and management during crisis events. Since interactions within social media users are frequently used to establish social networks, previous studies have explored how social media contributes to the information diffusion about crisis events. However, most studies have concentrated on the dynamics of the online social network, whereas the information diffused within social space and physical world during different event evolution stages tends to be overlooked. To address this problem, this study introduces a methodology for modeling and analyzing the information diffusion of crisis events based on the retweet patterns of social media users at city level. The proposed methodology consists of two parts: 1) Fisher ordinal clustering method is applied to divide the evolution stages of crisis events based on social media user activities. 2) To explore the information diffusion of crisis events in the physical world, the social-physical network (SPnet) is constructed by extending the retweet network to the geographical space for each evolution stage. The Weibo data related to the Kunming terrorist attack in 2014 are analyzed to demonstrate the methodology. By identifying the geo-location of users (90.8% of the total active users), two types of SPnet are constructed based on the retweet patterns of ordinary users and media users, respectively. The results show that the information diffusion in the SPnet of ordinary users are mainly from Kunming, while in the SPnet of media users, major developed cities are the key nodes for information diffusion. This study provides useful insights for understanding how human activities react to crisis events through the social-physical network at city level.

The outbreak threshold of information diffusion over social–physical networks

Cyber–Physical–Social System (CPSS) will be the foundation of intelligent enterprise and intelligent management in the future industrial system. As an important part of CPSS, social–physical networks, which are emerging through coupling social networks with physical networks, could facilitate information diffusion. To better characterize the process of information diffusion over social–physical networks, a coupled model, which incorporates social behavior spread and multimedia data transmission, is proposed and analyzed. In particular, the effective outbreak threshold of multimedia transmission is analytically derived. By theoretical analysis and numerical simulations, it is demonstrated that (a) social behavior spread cannot alter the effective outbreak threshold of multimedia data transmission, and (b) there is a mutual influence between social behavior spread and multimedia data transmission. These results would ultimately help to design more advanced, more robust and more effective control strategies and applications of information dissemination over multi-layer coupled networks.

Social-Aware Relay Selection for Cooperative Multicast Device-to-Device Communications

The increasing use of social networks such as Facebook, Twitter, and Instagram to share photos, video streaming, and music among friends has generated a huge increase in the amount of data traffic over wireless networks. This social behavior has triggered new communication paradigms such as device-to-device (D2D) and relaying communication schemes, which are both considered as strong drivers for the next fifth-generation (5G) cellular systems. Recently, the social-aware layer and its relationship to and influence on the physical communications layer have gained great attention as emerging focus points. We focus here on the case of relaying communications to pursue the multicast data dissemination to a group of users forming a social community through a relay node, according to the extension of the D2D mode to the case of device-to-many devices. Moreover, in our case, the source selects the device to act as the relay among different users of the multicast group by taking into account both the propagation link conditions and the relay social-trust level with the constraint of minimizing the end-to-end content delivery delay. An optimization procedure is also proposed in order to achieve the best performance. Finally, numerical results are provided to highlight the advantages of considering the impact of social level on the end-to-end delivery delay in the integrated social–physical network in comparison with the classical relay-assisted multicast communications for which the relay social-trust level is not considered.

Alternative Resilience Indices for City Ecosystems Subjected to Natural Hazards

AbstractPrompt and efficient responses against natural hazards are needed to build cities capable of withstanding disasters, namely resilient cities. This study aims at presenting and testing synthetic resilience indices over a real urban center threatened by multiple hazards, for which a global overview of city performance is requested. An integrated framework is proposed for quantitative resilience assessment by way of time‐independent synthetic indices. The approach proposed is in accordance to the complex network theory and uses a global indicator of the system connectivity to assess the city functioning also in case of network disruption. Resilience is evaluated as a proxy for systemic urban damage by modeling a city ecosystem as a hybrid social–physical network. Seismic and landslide scenario analyses are performed for the city of Sarno, Italy. A probability‐based approach is used to compute urban vulnerability. Subsequently, to highlight changes in results according to the type of disaster, a recovery strategy is simulated to assess efficiency and damage states in each recovery stage, and urban resilience.

A methodological framework assessing disaster resilience of city ecosystems to enhance resource use efficiency

Resilience represents a key issue for modern societies. Resilience is related to sustainability as safeguarding environmental assets can reduce risk and provide resources to facilitate recovery. The methodology quantifies the disaster resilience of city ecosystems by measuring urban efficiency in the event of shocks, according to an anthropocentric perspective. Cities are modelled as hybrid social–physical networks (HSPNs), accounting for interrelations between the physical and social components. Seismic scenarios are run on diverse urban shapes and on the real case study of the city of Sarno. Results on synthetic HSPNs show consistency with the real case study, therefore the validity of the model is highlighted. Urban shapes or forms prove to have a significant impact on urban efficiency in terms of service delivery, and on urban disaster resilience. The methodology proposed in this paper could be a useful tool for urban planners, particularly in cities with high exposure to natural disasters.

Developing an integrated framework to quantify resilience of urban systems against disasters

Urban resilience against disasters represents a key issue for contemporary society. The increasing complexity of cities along with more severe threats induced by climate change is pressing modern societies to search for new paths to prevention, preparedness and rapid recovery. As a result, resilience is triggering an increasing interest within many scientific contexts to explore the capabilities of communities to withstand extreme events. The present study proposes a framework aimed at quantifying disaster resilience of urban systems while ensuring an adequate level of sustainability, all according to a social and human-centric perspective. Urban networks are modelled as hybrid social–physical networks (HSPNs) by merging both physical and social components, and engineering measures are performed on HSPNs, as a measure of urban efficiency, within a multi-scale approach. Thence, social indicators are identified in order to characterise quality of life in the aftermath of a catastrophic event. Both efficiency and quality of life indicators are evaluated using a time–discrete approach before and after an extreme event occurs and during the recovery phase in order to measure inhabitant happiness and environmental sustainability. This approach allows handling different kinds of information simultaneously, being potentially implemented both in peacetime and during the recovery process. The former can be effective for urban coping capacity assessment in order to reduce risks as a mitigation instrument. The latter can be used in the post-event to identify the best recovery paths needing to be followed for adaptation.

Diffusion of real-time information in overlaying social-physical networks: Network coupling and clique structure

We study the diffusion behavior of real-time information in an overlaying social-physical network. Typically, real-time information is valuable only for a limited time duration and needs to be delivered before its deadline, indicating that real-time information is more likely to spread among friends within a “social proximity.” With this insight, we consider a physical information network which consists of many cliques and assume that real-time information can spread quickly within a clique. Conjoint to this physical information network, there are online social networks where the information can propagate via websites such as Facebook, Twitter, Youtube, etc. Capitalizing on the theory of inhomogeneous random graph, we analytically characterize the size of information epidemic. One interesting finding is that a larger size online social network, with the same degree distribution, may not necessarily yield a larger size of information epidemic in this overlaying social-physical network. In fact, under certain conditions, the size of information epidemic could even decrease with the growing size of the online social network. This is in stark contrast to that in a single network.

Conjoining Speeds up Information Diffusion in Overlaying Social-Physical Networks

We study the diffusion of information in an overlaying social-physical network. Specifically, we consider the following set-up: There is a physical information network where information spreads amongst people through conventional communication media (e.g., face-to-face communication, phone calls), and conjoint to this physical network, there are online social networks where information spreads via web sites such as Facebook, Twitter, FriendFeed, YouTube, etc. We quantify the size and the critical threshold of information epidemics in this conjoint social-physical network by assuming that information diffuses according to the SIR epidemic model. One interesting finding is that even if there is no percolation in the individual networks, percolation (i.e., information epidemics) can take place in the conjoint social-physical network. We also show, both analytically and experimentally, that the fraction of individuals who receive an item of information (started from an arbitrary node) is significantly larger in the conjoint social-physical network case, as compared to the case where the networks are disjoint. These findings reveal that conjoining the physical network with online social networks can have a dramatic impact on the speed and scale of information diffusion.