Shengmin JinAmazon · Amazon Stores
Shengmin Jin
PhD
About
20
Publications
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Introduction
Shengmin Jin is an applied scientist II at Amazon. He got his Ph.D degree in computer science from Syracuse University in 2022. Shengmin does research in Data Mining on Social Network, Network Representation and Causal Inference on recommender system.
Additional affiliations
Education
June 2016 - March 2021
August 2014 - May 2016
September 2006 - June 2010
Publications
Publications (20)
Noise, traditionally considered a nuisance in computational systems, is reconsidered for its unexpected and counter-intuitive benefits across a wide spectrum of domains, including nonlinear information processing, signal processing, image processing, machine learning, network science, and natural language processing. Through a comprehensive review...
A robust system should perform well under random failures or targeted attacks, and networks have been widely used to model the underlying structure of complex systems such as communication, infrastructure, and transportation networks. Hence, network robustness becomes critical to understanding system robustness. In this paper, we propose a spectral...
Network representation learning has played a critical role in studying networks. One way to study a graph is to focus on its spectrum, i.e., the eigenvalue distribution of its associated matrices. Recent advancements in spectral graph theory show that spectral moments of a network can be used to capture the network structure and various graph prope...
Graphs are ubiquitous across the globe and within science and engineering. Some powerful classifiers are proposed to classify nodes in graphs, such as Graph Convolutional Networks (GCNs). However, as graphs are growing in size, node classification on large graphs can be space and time consuming due to using whole graphs. Hence, some questions are r...
A large body of research has focused on analyzing large networks and graphs. However, network and graph data is often anonymized for reasons such as protecting data privacy. Under such circumstances, it is difficult to verify the source of network data, which leads to questions such as: Given an anonymized graph, can we identify the network from wh...
Most people consider their friends to be more positive than themselves, exhibiting a Sentiment Paradox. Psychology research attributes this paradox to human cognition bias. With the goal to understand this phenomenon, we study sentiment paradoxes in social networks. Our work shows that social connections (friends, followees, or followers) of users...
Graphs are ubiquitous across the globe and within science and engineering. With graphs growing in size, node classification on large graphs can be space and time consuming, even with powerful classifiers such as Graph Convolutional Networks (GCNs). Hence, some questions are raised, particularly, whether one can keep only some of the edges of a grap...
Most people consider their friends to be more positive than themselves, exhibiting a Sentiment Paradox. Psychology research attributes this paradox to human cognition bias. With the goal to understand this phenomenon, we study sentiment paradoxes in social networks. Our work shows that social connections (friends, followees, or followers) of users...
Failure propagation in power systems, and the possibility of becoming a cascading event, depend significantly on power system operating conditions. To make informed operating decisions that aim at preventing cascading failures, it is crucial to know the most probable failures based on operating conditions that are close to real-time conditions. In...
Network visualization has played a critical role in graph analysis, as it not only presents a big picture of a network but also helps reveal the structural information of a network. The most popular visual representation of networks is the node-link diagram. However , visualizing a large network with the node-link diagram can be challenging due to...
Research on networks is commonly performed using anonymized network data for various reasons such as protecting data privacy. Under such circumstances, it is difficult to verify the source of network data, which leads to questions such as: Given an anonymized graph, can we identify the network from which it is collected? Or if one claims the graph...
There has been a surge of interest in machine learning in graphs, as graphs and networks are ubiquitous across the globe and within science and engineering: road networks, power grids, protein-protein interaction networks, scientific collaboration networks, social networks, to name a few. Recent machine learning research has focused on efficient an...
Sentiment analysis research has focused on using text for predicting sentiments without considering the unavoidable peer influence on user emotions and opinions. The lack of large-scale ground-truth data on sentiments of users in social networks has limited research on how predictable sentiments are from social ties. In this paper, using a large-sc...
Understanding the role emotions play in social interactions has been a central research question in the social sciences. However, the challenge of obtaining large-scale data on human emotions has left the most fundamental questions on emotions less explored: How do emotions vary across individuals, evolve over time, and are connected to social ties...