Featured Articles is a section of the Special Technical Community on Social Networks that will shed light to pieces of social network research that are considered interesting and promising. Each month one research article will be selected from the IEEE Computer Society Digital Library to be featured in this section. The content featured here will be curated by Symeon Papadopoulos. Eventually, the featured articles will become freely available from this page. |
Featured Articles
October 2016: A Social Influence Approach for Group User Modeling in Group Recommendation Systems
Junpeng Guo, Yanlin Zhu, Aiai Li, Qipeng Wang, Weiguo Han, "A Social Influence Approach for Group User Modeling in Group Recommendation Systems", IEEE Intelligent Systems, vol. 31, no. , pp. 40-48, Sept.-Oct. 2016, doi:10.1109/MIS.2016.28 Abstract: While many studies on typical recommender systems focus on making recommendations to individual users, social activities usually involve groups of users. Issues related to group recommendations are increasingly becoming hot research topics. Among the differences between individual and group recommender systems, the most significant is social factors of group users. Social factors, including personality, expertise factor, interpersonal relationships, and preference similarities, widen the gap between group and individual recommendations. A new approach focuses on the impact of social factors on group recommender systemsâa computational model integrating the influences of personality, expertise factor, interpersonal relationships, and preference similarities. Comparative experiments are conducted on two datasets. The experimental results show that the proposed approach can provide more accurate and satisfactory group recommendations, especially when social influences are significant. |
September 2016: Semantic Filtering for Social Data
Abstract: More than a billion users on the Web are on social networks sharing and consuming short and real-time updates. Consumers of social data face information overload. Although information filtering can help, challenges that are specific to the short-text and real-time nature of social networks must be addressed. Knowledge bases -- particularly those derived from crowd-sourced platforms such as Wikipedia -- can be harnessed for building an intelligent and effective information-filtering system for social networks. |
August 2016: Itrust - interpersonal trust measurements from social interactions
Xiaoming Li, Qing Yang, Xiaodong Lin, Shaoen Wu, Mike Wittie, "Itrust: interpersonal trust measurements from social interactions", IEEE Network, vol. 30, no. 4, pp. 54-58, Jul-Aug 2016, doi: 10.1109/MNET.2016.7513864 |
July 2016: Social Diffusion Analysis With Common-Interest Model for Image Annotation
June 2016: Spammers Are Becoming "Smarter" on Twitter
May 2016: How Socially Aware Are Social Media Privacy Controls?
April 2016: Whither Social Media Governance?
March 2016: Distributed resource allocation in cloud-based wireless multimedia social networks
Guofang Nan, Zhifei Mao, Minqiang Li, Yang Zhang, Stein Gjessing, Honggang Wang, Mohsen Guizani, "Distributed resource allocation in cloud-based wireless multimedia social networks", IEEE Network, vol. 28, no. 4, pp. 74-80, Jul.-Aug. 2014, doi: 10.1109/MNET.2014.6863135 Abstract: With the rapid penetration of mobile devices, more users prefer to watch multimedia live-streaming via their mobile terminals. Quality of service provision is normally a critical challenge in such multimedia sharing environments. In this article, we propose a new cloud-based WMSN to efficiently deal with multimedia sharing and distribution. We first motivate the use of cloud computing and social contexts in sharing live streaming. Then our WMSN architecture is presented with the description of the different components of the network. After that, we focus on distributed resource management and formulate the bandwidth allocation problem in a gametheoretical framework that is further implemented in a distributed manner. In addition, we note the potential selfish behavior of mobile users for resource competition and propose a cheat-proof mechanism to motivate mobile users to share bandwidth. Illustrative results demonstrate the best responses of different users in the game equilibrium as well as the effectiveness of the proposed cheating avoidance scheme. |
February 2016: Trust Agent-Based Behavior Induction in Social Networks
January 2016: Cross-Platform Identification of Anonymous Identical Users in Multiple Social Media Networks
Xiaoping Zhou, Xun Liang, Haiyan Zhang, Yuefeng Ma, "Cross-Platform Identification of Anonymous Identical Users in Multiple Social Media Networks", IEEE Transactions on Knowledge & Data Engineering, vol.28, no. 2, pp. 411-424, Feb. 2016, doi:10.1109/TKDE.2015.2485222 Abstract: The last few years have witnessed the emergence and evolution of a vibrant research stream on a large variety of online social media network (SMN) platforms. Recognizing anonymous, yet identical users among multiple SMNs is still an intractable problem. Clearly, cross-platform exploration may help solve many problems in social computing in both theory and applications. Since public profiles can be duplicated and easily impersonated by users with different purposes, most current user identification resolutions, which mainly focus on text mining of users’ public profiles, are fragile. Some studies have attempted to match users based on the location and timing of user content as well as writing style. However, the locations are sparse in the majority of SMNs, and writing style is difficult to discern from the short sentences of leading SMNs such as Sina Microblog and Twitter. Moreover, since online SMNs are quite symmetric, existing user identification schemes based on network structure are not effective. The real-world friend cycle is highly individual and virtually no two users share a congruent friend cycle. Therefore, it is more accurate to use a friendship structure to analyze cross-platform SMNs. Since identical users tend to set up partial similar friendship structures in different SMNs, we proposed the Friend Relationship-Based User Identification (FRUI) algorithm. FRUI calculates a match degree for all candidate User Matched Pairs (UMPs), and only UMPs with top ranks are considered as identical users. We also developed two propositions to improve the efficiency of the algorithm. Results of extensive experiments demonstrate that FRUI performs much better than current network structure-based algorithms. |
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