For information propagation on social networks, one key problem is inferring propagation probability, i.e., the probability that one piece of information is propagated from one individual to the other. Existing methods mainly address this problem in a pair-wise manner, determining the propagation probability among pairs of individuals. This over-represented manner makes them suffer severe overfitting problem for pair of individuals without observed interactions, and thus limits their prediction accuracy. In this paper, instead of inferring the pair-wise propagation probability, we learn two low-rank vectors for each individual, representing its influence and susceptibility respectively. Based on this concise representation, we propose a probabilistic model to effectively learn individuals influence and susceptibility from the log of information cascades. We evaluate the performance of our method on the dataset from the largest social media in China, and show that our method outperforms existing methods at inferring propagation probability. Social media is revolutionizing the dissemination of information, with its great ease in information delivery, access and filtering. Different from conventional media, information propagation in social media proceeds along social relationships among individuals. Therefore, for the modeling of information propagation on social networks, one key problem is inferring the interpersonal influence between individuals, which is fundamental to influence maximization [11] , social recommendation, and viral marketing [12] . Existing studies mainly aim to determine the propagation probability of information among individuals, based on the structure of social network, the record of information cascade, and demographic characteristics of individuals. Kempe et al. [11] implemented the independent cascade model for information propagation, assuming a uniform propagation probability or a degree-modulated propagation probability. Goyal et al. [7] learned the propagation probability for independent cascade model, taking the structure of social network and the log of information cascade as inputs.
Wang et al. REF proposed a model to decouple the influence measured in a pair-wise way into two latent vectors representing influence and susceptibility of a node.
Learning user-specific latent influence and susceptibility from information cascades
AAAI 2015
arXiv: Social and Information Networks
Computer Science Physics