Making large-scale support vector machine learning practical
Advances in kernel methods
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
OOLAM: an opinion oriented link analysis model for influence persona discovery
Proceedings of the fourth ACM international conference on Web search and data mining
Aspect and sentiment unification model for online review analysis
Proceedings of the fourth ACM international conference on Web search and data mining
Proceedings of the 21st ACM international conference on Information and knowledge management
CINEMA: conformity-aware greedy algorithm for influence maximization in online social networks
Proceedings of the 16th International Conference on Extending Database Technology
Confluence: conformity influence in large social networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Social influence analysis in online social networks is the study of people's influence by analyzing the social interactions between individuals. There have been increasing research efforts to understand the influence propagation phenomenon due to its importance to information dissemination among others. Despite the progress achieved by state-of-the-art social influence analysis techniques, a key limitation of these techniques is that they only utilize positive interactions (e.g., agreement, trust) between individuals, ignoring two equally important factors, namely, negative relationships (e.g., distrust, disagreement) between individuals and conformity of people, which refers to a person's inclination to be influenced. In this paper, we propose a novel algorithm CASINO (Conformity-Aware Social INfluence cOmputation) to study the interplay between influence and conformity of each individual. Given a social network, CASINO first extracts a set of topic-based subgraphs where each subgraph depicts the social interactions associated with a specific topic. Then it optionally labels the edges (relationships) between individuals with positive or negative signs. Finally, it computes the influence and conformity indices of each individual in each signed topic-based subgraph. Our empirical study with several real-world social networks demonstrates superior effectiveness and accuracy of CASINO compared to state-of-the-art methods. Furthermore, we revealed several interesting characteristics of "influentials" and "conformers" in these networks.