63 citations · 430 across the 44 of their papers we have counts for
3 papers · 2 filters
Unsupervised Joint -node Graph Representations with Compositional Energy-Based Models
Leonardo Cotta, Carlos H. C. Teixeira, Ananthram Swami +1
Existing Graph Neural Network (GNN) methods that learn inductive unsupervised graph representations focus on learning node and edge representations by predicting observed edges in…
A Collective Learning Framework to Boost GNN Expressiveness
Mengyue Hang, Jennifer Neville, Bruno Ribeiro
Graph Neural Networks (GNNs) have recently been used for node and graph classification tasks with great success, but GNNs model dependencies among the attributes of nearby neighbor…
Infinity Learning: Learning Markov Chains from Aggregate Steady-State Observations
Jianfei Gao, Mohamed A. Zahran, Amit Sheoran +2
We consider the task of learning a parametric Continuous Time Markov Chain (CTMC) sequence model without examples of sequences, where the training data consists entirely of aggrega…