Title: Anomalous role of information diffusion in epidemic spreading
Speaker: Zhicheng Guo
A widely held belief in network epidemiology is that information diffusion making the individuals aware of the epidemic and thus driving them to seek protections from non-pharmaceutical or pharmaceutical resources can help suppress its spreading. However, as the COVID-19 pandemic has revealed, excessive information diffusion can trigger irrational acquisition and hoarding behaviors, which can lead to shortages of the resources even for those in urgent need and consequently, worsening the disease spreading. To develop a quantitative understanding of the effect of information diffusion on epidemic spreading subject to allocations of limited resources has become an urgently important problem with broad implications. We construct a multiplex network framework to characterize the complex interplay among resource allocation, information diffusion, and epidemic spreading, and develop a microscopic Markov chain theory to analyze their coevolution dynamics. There are two mainndings. Firstly, if the infected individuals have a large recovery probability, information diffusion plays the expected \normal” role of suppressing the epidemic. However, if the recovery probability is low, information diffusion can anomalously worsen the spreading, regardless of the available resources insofar as they are limited. Secondly, different types of resources can lead to distinct phase transitions underlying the epidemic outbreak: with abundant cure focused resources, the phase transition is of the second order, but if the resources are of the protection type and they are not as abundant, the transition becomes first order and a hysteresis loop emerges. The generality of the findings is established through simulations of synthetic and empirical three-layer networks with results in agreement with the theoretical predictions.
Supervisor: Kexian Zheng
Title: Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree
Speaker: Li Li
We propose a method based on neural networks to identify the sentiment polarity of opinion words expressed on a specific aspect of a sentence. Although a large majority of works typically focus on leveraging the expressive power of neural networks in handling this task, we explore the possibility of integrating dependency trees with neural networks for representation learning. To this end, we present a convolution over a dependency tree (CDT) model which exploits a Bi-directional Long Short Term Memory (Bi-LSTM) to learn representations for features of a sentence, and further enhance the embeddings with a graph convolutional network (GCN) which operates directly on the dependency tree of the sentence. Our approach propagates both contextual and dependency information from opinion words to aspect words, offering discriminative properties for supervision. Experimental results ranks our approach as the new state of-the-art in aspect-based sentiment classification.
Supervisor: Wei Zhang