Title:Topic-Aware Neural Keyphrase Generation for Social Media
Speaker: Ke Wang
A huge volume of user-generated content is daily produced on social media. To facilitate automatic language understanding, we study keyphrase prediction, distilling salient information from massive posts. While most existing methods extract words from source posts to form keyphrases, we propose a sequence-to-sequence (seq2seq) based neural keyphrase generation framework, enabling absent keyphrases to be created. Moreover, our model, being topic-aware, allows joint modeling of corpus level latent topic representations, which helps alleviate the data sparsity that widely exhibited in social media language. Experiments on three datasets collected from English and Chinese social media platforms show that our model significantly outperforms both extraction and generation models that do not exploit latent topics.1 Further discussions show that our model learns meaningful topics, which interprets its superiority in social media keyphrase generation.
Supervisor: Yan Chen, Shixiang Jiao
Title:Leveraging percolation theory to single out influential spreaders in networks
Speaker: Qi Zeng
The formulation of an accurate method to optimally identify influential nodes in complex network topologies remains an unsolved challenge..Here, we present the exact solution of the problem for the specific, but highly relevant, case of the Susceptible-Infected-Removed (SIR) model for epidemic spreading at criticality. By exploiting the mapping between bond percolation and the static properties of SIR, we prove that the recently introduced Non-Backtracking centrality is the optimal criterion for the identification of influential spreaders in locally tree-like networks at criticality.
Supervisor: Ying Liu, Jiaxing Chen