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Browsing by Author "Sha, Hao"
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Item Dynamic topic modeling of the COVID-19 Twitter narrative among U.S. governors and cabinet executives(2020-04-19) Sha, Hao; Al Hasan, Mohammad; Mohler, George; Brantingham, P.; Computer and Information Science, School of ScienceA combination of federal and state-level decision making has shaped the response to COVID-19 in the United States. In this paper, we analyze the Twitter narratives around this decision making by applying a dynamic topic model to COVID-19 related tweets by U.S. Governors and Presidential cabinet members. We use a network Hawkes binomial topic model to track evolving sub-topics around risk, testing, and treatment. We also construct influence networks amongst government officials using Granger causality inferred from the network Hawkes process.Item Learning network event sequences using long short-term memory and second-order statistic loss(Wiley, 2021-02) Sha, Hao; Al Hasan, Mohammad; Mohler, George; Computer and Information Science, School of ScienceModeling temporal event sequences on the vertices of a network is an important problem with widespread applications; examples include modeling influences in social networks, preventing crimes by modeling their space–time occurrences, and forecasting earthquakes. Existing solutions for this problem use a parametric approach, whose applicability is limited to event sequences following some well-known distributions, which is not true for many real life event datasets. To overcome this limitation, in this work, we propose a composite recurrent neural network model for learning events occurring in the vertices of a network over time. Our proposed model combines two long short-term memory units to capture base intensity and conditional intensity of an event sequence. We also introduce a second-order statistic loss that penalizes higher divergence between the generated and the target sequence's distribution of hop count distance of consecutive events. Given a sequence of vertices of a network in which an event has occurred, the proposed model predicts the vertex where the next event would most likely occur. Experimental results on synthetic and real-world datasets validate the superiority of our proposed model in comparison to various baseline methods.Item Parameter Optimization for Interaction between C-Terminal Domains of HIV-1 Capsid Protein(ACS, 2017-04) Sha, Hao; Zhu, Fangqiang; Physics, School of ScienceHIV-1 capsid proteins (CAs) assemble into a capsid that encloses the viral RNA. The binding between a pair of C-terminal domains (CTDs) constitutes a major interface in both the CA dimers and the large CA assemblies. Here, we attempt to use a general residue-level coarse-grained model to describe the interaction between two isolated CTDs in Monte Carlo simulations. With the standard parameters that depend only on the residue types, the model predicts a much weaker binding in comparison to the experiments. Detailed analysis reveals that some Lennard-Jones parameters are not compatible with the experimental CTD dimer structure, thus resulting in an unfavorable interaction energy. To improve the model for the CTD binding, we introduce ad hoc modifications to a small number of Lennard-Jones parameters for some specific pairs of residues at the binding interface. Through a series of extensive Monte Carlo simulations, we identify the optimal parameters for the CTD–CTD interactions. With the refined model parameters, both the binding affinity (with a dissociation constant of 13 ± 2 μM) and the binding mode are in good agreement with the experimental data. This study demonstrates that the general interaction model based on the Lennard-Jones potential, with some modest adjustment of the parameters for key residues, could correctly reproduce the reversible protein binding, thus potentially applicable for simulating the thermodynamics of the CA assemblies.Item Predicting Virality on Networks Using Local Graphlet Frequency Distribution(IEEE, 2018-12) Baas, Andrew; Hung, Frances; Sha, Hao; Al Hasan, Mohammad; Mohler, George; Computer and Information Science, School of ScienceThe task of predicting virality has far-reaching consequences, from the world of advertising to more recent attempts to reduce the spread of fake news. Previous work has shown that graphlet distribution is an effective feature for predicting virality. Here, we investigate the use of aggregated edge-centric local graphlets around source nodes as features for virality prediction. These prediction features are used to predict expected virality for both a time-independent Hawkes model and an independent cascade model of virality. In the Hawkes model, we use linear regression to predict the number of Hawkes events and node ranking, while in the independent cascade model we use logistic regression to predict whether a k-size cascade will multiply by a factor X in size. Our study indicates that local graphlet frequency distribution can effectively capture the variances of the viral processes simulated by Hawkes process and independent-cascade process. Furthermore, we identify a group of local graphlets which might be significant in the viral processes. We compare the effectiveness of our methods with eigenvector centrality-based node choice.Item Solving Prediction Problems from Temporal Event Data on Networks(2021-08) Sha, Hao; Mohler, George; Hasan, Mohammad; Dundar, Murat; Mukhopadhyay, SnehasisMany complex processes can be viewed as sequential events on a network. In this thesis, we study the interplay between a network and the event sequences on it. We first focus on predicting events on a known network. Examples of such include: modeling retweet cascades, forecasting earthquakes, and tracing the source of a pandemic. In specific, given the network structure, we solve two types of problems - (1) forecasting future events based on the historical events, and (2) identifying the initial event(s) based on some later observations of the dynamics. The inverse problem of inferring the unknown network topology or links, based on the events, is also of great important. Examples along this line include: constructing influence networks among Twitter users from their tweets, soliciting new members to join an event based on their participation history, and recommending positions for job seekers according to their work experience. Following this direction, we study two types of problems - (1) recovering influence networks, and (2) predicting links between a node and a group of nodes, from event sequences.Item Source detection on networks using spatial temporal graph convolutional networks(IEEE, 2021-10) Sha, Hao; Al Hasan, Mohammad; Mohler, George; Computer and Information Science, School of ScienceDetecting the source of an outbreak cluster during a pandemic like COVID-19 can provide insights into the transmission process, associated risk factors, and help contain the spread. In this work we study the problem of source detection from multiple snapshots of spreading on an arbitrary network structure. We use a spatial temporal graph convolutional network based model (SD-STGCN) to produce a source probability distribution, by fusing information from temporal and topological spaces. We perform extensive experiments using popular compartmental simulation models over synthetic networks and empirical contact networks. We also demonstrate the applicability of our approach with real COVID-19 case data.