With the rapid development of social networks and its applications, the demand of publishing and sharing social network data for the purpose of commercial or research is increasing. However, the disclosure risks of sensitive information of social network users are also arising. The paper proposes an effective structural attack to deanonymize social graph data. The attack uses the cumulative degree ofn-hop neighbors of a node as the regional feature and combines it with the simulated annealing-based graph matching method to explore the nodes reidentification in anonymous social graphs. The simulation results on two social network datasets show that the attack is feasible in the nodes reidentification in anonymous graphs including the simply anonymous graph, randomized graph andk-isomorphism graph.
Abstract-With the development of MOOCs, millions of students enrolled into online courses. The discussion forums in MOOCs provide a virtual community for students to interact with each other. The communication in different topics indicates the engagement of students in the courses and social-learning process during the interactions. In this respect, this paper explores the use of Naive Bayesian classification approach for predicting the participation of the forum and the use of Bayesian-based social-learning approach for modelling the opinion formation process during the discussion and indicating the influence of instructors in the discussion forum. Results on data from 1 Coursera course demonstrate that the poster's retention can be well predicted by Naive Bayes classifier based on the combination of different features of the forum postings; additionally, we find that the superposters may not be the participants who will continue posting in the last several weeks. In terms of social-learning, our analysis indicates participants will aggregate information by repeated interactions and the instructors' post can improve the convergence of learning process to the true belief. These results confirm the influence of the instructors' intervention further.
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