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.
Kieron O'Hara, "Are We Getting Privacy the Wrong Way Round?," IEEE Internet Computing, vol. 17, no. 4, pp. 89-92, July-Aug. 2013, doi:10.1109/MIC.2013.62
Abstract: Individualists, communitarians, and technological determinists agree that privacy's benefits accrue to individuals, and that its costs (in terms of less security or efficiency) fall on society. As such, it is the individual's choice to give privacy away. However, privacy does benefit wider society in important respects, and so this consensus is flawed.
Ting Hua, Chang-Tien Lu, Naren Ramakrishnan, Feng Chen, Jaime Arredondo, David Mares, Kristen Summers, "Analyzing Civil Unrest through Social Media," Computer, vol. 46, no. 12, pp. 80-84, Dec. 2013, doi:10.1109/MC.2013.442
Abstract: Mining and analyzing data from social networks such as Twitter can reveal new insights into the causes of civil disturbances, including trigger events and the role of political entrepreneurs and organizations in galvanizing public opinion.
Maria R. Lee, Tsung Teng Chen, "Understanding Social Computing Research," IT Professional, vol. 15, no. 6, pp. 56-62, Nov.-Dec. 2013, doi:10.1109/MITP.2012.121
Abstract: Social computing is an emerging field, encompassing a wide range of topics. A broad understanding of the major topics involved in social computing is important for both scholars and practitioners. The authors present and analyze the voluminous social computing related studies to date, applying document co-citation analysis, pathfinder networks, core-document analysis, and the Herfindahl-Hirschman index. The results not only provide insight to this subject area but also afford a conduit for the future research in this discipline.
Jianwei Niu, Jing Peng, Lei Shu, Chao Tong, Wanjiun Liao, "An Empirical Study of a Chinese Online Social Network--Renren," Computer, vol. 46, no. 9, pp. 78-84, Sept. 2013, doi:10.1109/MC.2013.1
Abstract: Deeper knowledge of social networks' structure and temporal evolution enhances data mining for both research and education purposes. An empirical analysis of a Chinese social network, Renren, shows that it follows an exponentially truncated power law in degree distribution, and has a short average node distance.
Wei Tan, M. Brian Blake, Iman Saleh, Schahram Dustdar, "Social-Network-Sourced Big Data Analytics," IEEE Internet Computing, vol. 17, no. 5, pp. 62-69, Sept.-Oct. 2013, doi:10.1109/MIC.2013.100
Abstract: Very large datasets, also known as big data, originate from many domains, including healthcare, energy, weather, business, and social networks. Deriving knowledge is more difficult than ever when we must do it by intricately processing big data. Organizations rely on third-party, commodity computing resources or clouds to gather the computational resources required to manipulate data of this magnitude. Although social networks are perhaps among the largest big data producers, the collaboration that results from leveraging this paradigm could help to solve big data processing challenges. Here, the authors explore using personal ad hoc clouds comprised of individuals in social networks to address such challenges.
Luca Maria Aiello, Giorgos Petkos, Carlos Martin, David Corney, Symeon Papadopoulos, Ryan Skraba, Ayse Goker, Ioannis Kompatsiaris, Alejandro Jaimes, "Sensing Trending Topics in Twitter," IEEE Transactions on Multimedia, vol. 15, no. 6, pp. 1268-1282, June 2013, doi:10.1109/TMM.2013.2265080
Abstract: Online social and news media generate rich and timely information about real-world events of all kinds. However, the huge amount of data available, along with the breadth of the user base, requires a substantial effort of information filtering to successfully drill down to relevant topics and events. Trending topic detection is therefore a fundamental building block to monitor and summarize information originating from social sources. There are a wide variety of methods and variables and they greatly affect the quality of results. We compare six topic detection methods on three Twitter datasets related to major events, which differ in their time scale and topic churn rate. We observe how the nature of the event considered, the volume of activity over time, the sampling procedure and the pre-processing of the data all greatly affect the quality of detected topics, which also depends on the type of detection method used. We find that standard natural language processing techniques can perform well for social streams on very focused topics, but novel techniques designed to mine the temporal distribution of concepts are needed to handle more heterogeneous streams containing multiple stories evolving in parallel. One of the novel topic detection methods we propose, based on n-grams cooccurrence and df-idft topic ranking, consistently achieves the best performance across all these conditions, thus being more reliable than other state-of-the-art techniques.
Pauline Anthonysamy, Phil Greenwood, Awais Rashid, "Social Networking Privacy: Understanding the Disconnect from Policy to Controls," Computer, vol. 46, no. 6, pp. 60-67, June 2013, doi:10.1109/MC.2012.326
Xiwang Yang, Yang Guo, Yong Liu, "Bayesian-Inference-Based Recommendation in Online Social Networks," IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 4, pp. 642-651, April 2013, doi:10.1109/TPDS.2012.192
Abstract: In this paper, we propose a Bayesian-inference-based recommendation system for online social networks. In our system, users share their content ratings with friends. The rating similarity between a pair of friends is measured by a set of conditional probabilities derived from their mutual rating history. A user propagates a content rating query along the social network to his direct and indirect friends. Based on the query responses, a Bayesian network is constructed to infer the rating of the querying user. We develop distributed protocols that can be easily implemented in online social networks. We further propose to use Prior distribution to cope with cold start and rating sparseness. The proposed algorithm is evaluated using two different online rating data sets of real users. We show that the proposed Bayesian-inference-based recommendation is better than the existing trust-based recommendations and is comparable to Collaborative Filtering (CF) recommendation. It allows the flexible tradeoffs between recommendation quality and recommendation quantity. We further show that informative Prior distribution is indeed helpful to overcome cold start and rating sparseness.
Hongxin Hu, Gail-Joon Ahn, Jan Jorgensen, "Multiparty Access Control for Online Social Networks: Model and Mechanisms," IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 7, pp. 1614-1627, July 2013, doi:10.1109/TKDE.2012.97
Online social networks (OSNs) have experienced tremendous growth in recent years and become a de facto portal for hundreds of millions of Internet users. These OSNs offer attractive means for digital social interactions and information sharing, but also raise a number of security and privacy issues. While OSNs allow users to restrict access to shared data, they currently do not provide any mechanism to enforce privacy concerns over data associated with multiple users. To this end, we propose an approach to enable the protection of shared data associated with multiple users in OSNs. We formulate an access control model to capture the essence of multiparty authorization requirements, along with a multiparty policy specification scheme and a policy enforcement mechanism. Besides, we present a logical representation of our access control model that allows us to leverage the features of existing logic solvers to perform various analysis tasks on our model. We also discuss a proof-of-concept prototype of our approach as part of an application in Facebook and provide usability study and system evaluation of our method.
Pavlos Basaras, Dimitrios Katsaros, Leandros Tassiulas, "Detecting Influential Spreaders in Complex, Dynamic Networks," Computer, vol. 46, no. 4, pp. 24-29, April 2013, doi:10.1109/MC.2013.75
A hybrid of node degree and k-shell index is more effective at identifying influential spreaders and has less computational overhead than either of these traditional measures.