69 citations · 327 across the 44 of their papers we have counts for
45 papers
Graph Encoding and Neural Network Approaches for Volleyball Analytics: From Game Outcome to Individual Play Predictions
Rhys Tracy, Haotian Xia, Alex Rasla +2
This research aims to improve the accuracy of complex volleyball predictions and provide more meaningful insights to coaches and players. We introduce a specialized graph encoding…
Link Prediction without Graph Neural Networks
Zexi Huang, Mert Kosan, Arlei Silva +1
Link prediction, which consists of predicting edges based on graph features, is a fundamental task in many graph applications. As for several related problems, Graph Neural Network…
Robust Ante-hoc Graph Explainer using Bilevel Optimization
Kha-Dinh Luong, Mert Kosan, Arlei Lopes Da Silva +1
Explaining the decisions made by machine learning models for high-stakes applications is critical for increasing transparency and guiding improvements to these decisions. This is p…
Learning Prototype Classifiers for Long-Tailed Recognition
Saurabh Sharma, Yongqin Xian, Ning Yu +1
The problem of long-tailed recognition (LTR) has received attention in recent years due to the fundamental power-law distribution of objects in the real-world. Most recent works in…
Global Counterfactual Explainer for Graph Neural Networks
Mert Kosan, Zexi Huang, Sourav Medya +2
Graph neural networks (GNNs) find applications in various domains such as computational biology, natural language processing, and computer security. Owing to their popularity, ther…
Mind Reader: Reconstructing complex images from brain activities
Sikun Lin, Thomas Sprague, Ambuj K Singh
Understanding how the brain encodes external stimuli and how these stimuli can be decoded from the measured brain activities are long-standing and challenging questions in neurosci…
Deep Representations for Time-varying Brain Datasets
Sikun Lin, Shuyun Tang, Scott Grafton +1
Finding an appropriate representation of dynamic activities in the brain is crucial for many downstream applications. Due to its highly dynamic nature, temporally averaged fMRI (fu…
Incorporating Heterophily into Graph Neural Networks for Graph Classification
Jiayi Yang, Sourav Medya, Wei Ye
Graph Neural Networks (GNNs) often assume strong homophily for graph classification, seldom considering heterophily, which means connected nodes tend to have different class labels…
Modeling Human-AI Team Decision Making
Wei Ye, Francesco Bullo, Noah Friedkin +1
AI and humans bring complementary skills to group deliberations. Modeling this group decision making is especially challenging when the deliberations include an element of risk and…
Graph Neural Diffusion Networks for Semi-supervised Learning
Wei Ye, Zexi Huang, Yunqi Hong +1
Graph Convolutional Networks (GCN) is a pioneering model for graph-based semi-supervised learning. However, GCN does not perform well on sparsely-labeled graphs. Its two-layer vers…
A Broader Picture of Random-walk Based Graph Embedding
Zexi Huang, Arlei Silva, Ambuj Singh
Graph embedding based on random-walks supports effective solutions for many graph-related downstream tasks. However, the abundance of embedding literature has made it increasingly…
Event Detection on Dynamic Graphs
Mert Kosan, Arlei Silva, Sourav Medya +2
Event detection is a critical task for timely decision-making in graph analytics applications. Despite the recent progress towards deep learning on graphs, event detection on dynam…
POLE: Polarized Embedding for Signed Networks
Zexi Huang, Arlei Silva, Ambuj Singh
From the 2016 U.S. presidential election to the 2021 Capitol riots to the spread of misinformation related to COVID-19, many have blamed social media for today's deeply divided soc…
Learning Interpretable Models for Coupled Networks Under Domain Constraints
Hongyuan You, Sikun Lin, Ambuj K. Singh
Modeling the behavior of coupled networks is challenging due to their intricate dynamics. For example in neuroscience, it is of critical importance to understand the relationship b…
Expertise and confidence explain how social influence evolves along intellective tasks
Omid Askarisichani, Elizabeth Y. Huang, Abed K. Musaffar +3
Discovering the antecedents of individuals' influence in collaborative environments is an important, practical, and challenging problem. In this paper, we study interpersonal influ…
DANR: Discrepancy-aware Network Regularization
Hongyuan You, Furkan Kocayusufoglu, Ambuj K. Singh
Network regularization is an effective tool for incorporating structural prior knowledge to learn coherent models over networks, and has yielded provably accurate estimates in appl…
The 1995-2018 Global Evolution of the Network of Amicable and Hostile Relations Among Nation-States
Omid Askarisichania, Ambuj K. Singh, Francesco Bullo +1
We draw on the data collected by the Integrated Crisis Early Warning System on millions of international and regional public news stories, and this system's indicators of the orien…
Learning Deep Graph Representations via Convolutional Neural Networks
Wei Ye, Omid Askarisichani, Alex Jones +1
Graph-structured data arise in many scenarios. A fundamental problem is to quantify the similarities of graphs for tasks such as classification. R-convolution graph kernels are pos…
Incorporating User's Preference into Attributed Graph Clustering
Wei Ye, Dominik Mautz, Christian Boehm +2
Graph clustering has been studied extensively on both plain graphs and attributed graphs. However, all these methods need to partition the whole graph to find cluster structures. S…
Tree++: Truncated Tree Based Graph Kernels
Wei Ye, Zhen Wang, Rachel Redberg +1
Graph-structured data arise ubiquitously in many application domains. A fundamental problem is to quantify their similarities. Graph kernels are often used for this purpose, which…
Estimating localized complexity of white-matter wiring with GANs
Haraldur T. Hallgrimsson, Richika Sharan, Scott T. Grafton +1
In-vivo examination of the physical connectivity of axonal projections through the white matter of the human brain is made possible by diffusion weighted magnetic resonance imaging…
Learning Heuristics over Large Graphs via Deep Reinforcement Learning
Sahil Manchanda, Akash Mittal, Anuj Dhawan +3
There has been an increased interest in discovering heuristics for combinatorial problems on graphs through machine learning. While existing techniques have primarily focused on ob…
Influence Minimization Under Budget and Matroid Constraints: Extended Version
Sourav Medya, Arlei Silva, Ambuj Singh
Recently, online social networks have become major battlegrounds for political campaigns, viral marketing, and the dissemination of news. As a consequence, ''bad actors'' are incre…
K-Core Minimization: A Game Theoretic Approach
Sourav Medya, Tiyani Ma, Arlei Silva +1
K-cores are maximal induced subgraphs where all vertices have degree at least k. These dense patterns have applications in community detection, network visualization and protein fu…