50 citations · 56 across the 5 of their papers we have counts for
5 papers
Leveraging the Graph Structure of Neural Network Training Dynamics
Fatemeh Vahedian, Ruiyu Li, Puja Trivedi +2
Understanding the training dynamics of deep neural networks (DNNs) is important as it can lead to improved training efficiency and task performance. Recent works have demonstrated…
Refining Network Alignment to Improve Matched Neighborhood Consistency
Mark Heimann, Xiyuan Chen, Fatemeh Vahedian +1
Network alignment, or the task of finding meaningful node correspondences between nodes in different graphs, is an important graph mining task with many scientific and industrial a…
CONE-Align: Consistent Network Alignment with Proximity-Preserving Node Embedding
Xiyuan Chen, Mark Heimann, Fatemeh Vahedian +1
Network alignment, the process of finding correspondences between nodes in different graphs, has many scientific and industrial applications. Existing unsupervised network alignmen…
Heterogeneous network approach to predict individuals' mental health
Shikang Liu, Fatemeh Vahedian, David Hachen +4
Depression and anxiety are critical public health issues affecting millions of people around the world. To identify individuals who are vulnerable to depression and anxiety, predic…
Weighted Random Walk Sampling for Multi-Relational Recommendation
Fatemeh Vahedian, Robin Burke, Bamshad Mobasher
In the information overloaded web, personalized recommender systems are essential tools to help users find most relevant information. The most heavily-used recommendation framework…