Shengmin Jin

Shengmin Jin
Syracuse University | SU · Department of Electrical Engineering and Computer Science

PhD Candidate

About

17
Publications
7,510
Reads
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57
Citations
Citations since 2017
17 Research Items
57 Citations
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Introduction
Shengmin Jin currently works at the Department of Electrical Engineering and Computer Science, Syracuse University. Shengmin does research in Data Mining on Social Network and Network Representation.
Additional affiliations
June 2016 - May 2021
Syracuse University
Position
  • PhD Student
May 2016 - September 2016
Syracuse University
Position
  • Research Assistant
September 2015 - May 2016
Syracuse University
Position
  • Research Assistant
Description
  • Mathematical Basis for Computing Science Formal Methods
Education
June 2016 - March 2021
Syracuse University
Field of study
  • Computer Science
August 2014 - May 2016
Syracuse University
Field of study
  • Computer Science
September 2006 - June 2010
Fudan University
Field of study
  • Mathematics

Publications

Publications (17)
Conference Paper
Full-text available
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...
Conference Paper
Full-text available
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...
Article
Full-text available
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...
Article
Full-text available
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...
Article
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...
Chapter
Full-text available
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...
Preprint
Full-text available
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...
Article
Full-text available
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...
Conference Paper
Full-text available
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...
Conference Paper
Full-text available
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...
Conference Paper
Full-text available
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...
Conference Paper
Full-text available
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...
Conference Paper
Full-text available
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...

Network

Cited By

Projects

Projects (2)
Project
A new network representation: Network Shape, to represent networks with 3D Shapes