Journal of Network and Computer Applications 86 (2017) 1–2
Contents lists available at ScienceDirect
Journal of Network and Computer Applications journal homepage: www.elsevier.com/locate/jnca
Editorial
Special issue on pervasive social networking
With the rapid growth of mobile computing and social networking, social network has extended its popularity from the Internet to mobile domain. Pervasive Social Networking (PSN) ensures social communications at any time and in any place with a universal manner. It supports on-line and instant (i.e., pervasive) social activities based on heterogeneous networks, e.g., the Internet, mobile cellular networks, self-organized networks or other networking technologies. PSN holds such specific characteristics as intelligence for service provision, adaptability with network access and ubiquity on social communications. It is thought as one of killer applications in the next generation mobile networks and wireless systems (i.e., 5G). There are various applications over PSN. Typical examples include social chatting, gaming, rescuing, recommending, information sharing, crowdsourcing and crowdsensing. Because group mobility is very common in modern life, PSN has become significantly valuable for mobile users, especially when they are familiar strangers and often appear in vicinity. PSN greatly extends our experiences of social communications. There are quite a number of vivid research activities related to social networking and computing. Recent efforts have started to study social communications in mobile and wireless domains. In both academia and industry, many research groups and companies have conducted research in the area of PSN. However, we are still facing many technical challenges to make PSN finally successful. First, trust, security and privacy have not been extensively considered in existing projects. Current literature has not comprehensively investigated how to manage trust in PSN in a holistic manner although trust plays an important role in PSN for reciprocal activities among strangers. Second, social behavior mining and social data analytics in PSN are interesting and significant research topics, which greatly benefit the diversity of applications in the cyber-physical and social world. However, how to extract valuable information from big social data collected in various social contexts and based on different network access agencies is still a challenge. Third, how to make use of 5G mobile networks, particularly Device-to-Device (D2D) communications, to realize PSN with core network support on networking reliability and availability is still an open issue. Finally, PSN can for sure support many promising applications (e.g., IoT sensing and sourcing) and further develop itself in parallel with other emerging technologies. Innovating killer applications based on PSN and overcoming challenges caused by PSN become essentially important and interesting. This special issue aims to bring together researchers and practitioners to discuss various aspects of pervasive social networking, explore key theories, investigate technology enablers, http://dx.doi.org/10.1016/j.jnca.2017.03.023 1084-8045/& 2017 Published by Elsevier Ltd.
develop significant applications and innovate new solutions for overcoming major challenges in this exciting research area. The special issue collects 9 articles that cover original unpublished research illustrative of pervasive social networking from 38 submissions after a very rigorous review process. We classify them into three categories and briefly introduce them as below.
1. Studies about the essence of PSN Influence maximization is a fundamental problem that aims at finding a small subset of seed nodes to maximize the spread of influence in social networks. Lu et al. (2017) studied the problem of influence maximization. They proposed a more efficient method to improve the performance of the greedy algorithm to replace its time-consuming part and overcome its bottleneck. They further designed a CascadeDiscount algorithm to solve the influence maximization problem and demonstrated its advanced performance with real world datasets. Yang et al. (2017) investigated the scalability issue of scale-free social network partition with hypergraph. In order to achieve scalable and high partitioning quality for hypergraph modeled social networks, they proposed a partitioning method, EQHyperpart by applying information-Entropy-based modularity Q value (EQ) to direct the hypergraph partitioning process. Microblog is a significant PSN form that provides an efficient service for diffusing information. Zhou et al. (2017) studied information diffusion in microblog. They proposed a multi-level structure to analyze the diffusion process of hot topics. Consequently, the interesting features of the merging effect between two retweeting groups, the existence of super groups and the centralized topology of information cascades are discovered and analyzed. Some interesting findings were disclosed about the trend of future diffusion. In addition, the authors also proposed a diffusion model based on cascade model framework for generating a retweeting network and demonstrated its effectiveness with real data. Zhang et al. (2017a), made efforts to explore user behaviors in PSN by treating it as a monopolistically competitive market where each agent specializes in a particular area and data are priced and tradable as goods. They proposed decentralized deep reinforcement learning and improved conventional reinforcement learning algorithms in order to effectively study user patterns in PSN.
2
Editorial / Journal of Network and Computer Applications 86 (2017) 1–2
2. PSN trust and privacy
Acknowledgment
By making use of the social trust evaluated in mobile social networking, CloudFile was proposed to automatically manage personal data sharing at the cloud with sound performance and effectiveness (Yan and Shi, 2017). It uses a Key Policy–Attribute Based Encryption (KP-ABE) scheme to control personal data access via mobile devices based on social trust in order to guarantee the safety of mobile cloud data storage. The investigation on system user acceptance achieved a satisfactory result. For enhancing privacy in mobile online social networks, Sun et al. (2017) proposed a new architecture and a new scheme called User-Defined Privacy Location-Sharing System (UDPLS) to guarantee user location privacy and social network privacy. It can prevent user location privacy from a social network server and user social network privacy from a location server.
This work is sponsored by the National Key Research and Development Program of China (grant 2016YFB0800704), the NSFC (grants 61672410 and U1536202), the Project Supported by Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2016ZDJC-06), the PhD grant of the Ministry of Education, China (grant JY0300130104), the 111 project (grants B08038 and B16037), and Aalto University.
3. Studies about PSN enablers and other issues D2D communications can play as a feasible platform for PSN. Peer discovery is the key technique affecting the D2D functioning. Traditional service-attribute-based peer discovery methods cannot guarantee effective establishment of D2D communication links. For solving this deficiency, Zhang etal. (2017b) proposed a peer discovery scheme on the basis of a two-dimensional abstract model with the support of both social attribute and service attribute. In this model, trust degree and communication willingness are utilized to form a neighbor cluster recommendation in order to improve the efficiency of peer discovery and guarantee safe establishment of D2D links. Motivated by the practical phenomenon in PSN in term of multiple data sources, Liu et al. (2017) proposed a Multi-DataSource Dynamic Searchable Symmetric Encryption (MDSDSSE) scheme. This scheme allows each data source to build a local index individually and enables a storage provider to merge all local indexes into a global index afterwards. Any information such as how data files or search results are distributed over data sources is kept secret in the proposed scheme, thus ensure its security. Quick and Choo (2017) investigated a new research field about Digital Forensic Intelligence Analysis in the context of PSN. They proposed a Digital Forensic Intelligence Analysis Cycle (DFIAC) and demonstrated its potential to locate information across forensically extracted data from mobile devices. It was a great pleasure to edit this special issue. We would like to thank all authors and reviewers for their tremendous contributions to it. We indeed appreciate the kind support from professor Mohammed Atiquzzaman, the Editor-in-Chief of Journal of Network and Computer Applications, for ensuring the quality of the whole special issue. Without any doubt, PSN is a field full of promising research challenges. Many significant research issues worth our efforts to explore, but unfortunately have not been covered in this special issue. But we believe this special issue can stimulate future research and investigation in the field of pervasive social networking.
References Liu, C., Zhu, L., Chen, J., 2017. Efficient searchable symmetric encryption for storing multiple source dynamic social data on cloud. J. Netw. Comput. Appl. Lu, F., Zhang, W., Shao, L., Jiang, X., Xu, P., Jin, H., 2017. Scalable influence maximization under independent cascade model. J. Netw. Comput. Appl. Quick, D., Choo, K.K.R., 2017. Pervasive social networking forensic intelligence and evidence from mobile device extracts. J. Netw. Comput. Appl. Sun, G., Xie, Y., Liao, D., Yu, H., Chang, V., 2017. User-defined privacy locationsharing system in mobile online social networks. J. Netw. Comput. Appl. Yan, Z., Shi, W., 2017. CloudFile: a cloud data access control system based on mobile social trust. J. Netw. Comput. Appl. Yang, W., Wang, G., Bhuiyand, M.Z.A., Choo, K.K.R., 2017. Hypergraph partitioning for social networks based on information entropy modularity. J. Netw. Comput. Appl. Zhang, Y., Song, B., Zhang, P., 2017a. Social behavior study under pervasive social networking based on decentralized deep reinforcement learning. J. Netw. Comput. Appl. Zhang, Z., Wang, L., Liu, D., Zhang, Y., 2017b. Peer discovery for D2D communications based on social attribute and service attribute. J. Netw. Comput. Appl. Zhou, Y., Zhang, B., Sun, X., Zheng, Q., Liu, T., 2017. Analyzing and modeling dynamics of information diffusion in microblogging social network. J. Netw. Comput. Appl.
Guest editors Zheng Yan State Key Laboratory on Integrated Services Networks, School of Cyber Engineering, Xidian University, Xi'an 710071, China Department of Communications and Networking, Aalto University, Espoo 02150, Finland E-mail address:
[email protected]
Jun Liu MOEKLINNS Lab, Department of Computer Science, Xi’an Jiaotong University, Xi'an 710048, China E-mail address:
[email protected]
Laurence T. Yang Department of Computer Science, St Francis Xavier University, Canada E-mail address:
[email protected]
