23 citations · 46 across the 9 of their papers we have counts for
5 papers · 1 filter
TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers
Alan John Varghese, Aniruddha Bora, Mengjia Xu +1
Dynamic graph embedding has emerged as a very effective technique for addressing diverse temporal graph analytic tasks (i.e., link prediction, node classification, recommender syst…
Norm-based Generalization Bounds for Compositionally Sparse Neural Networks
Tomer Galanti, Mengjia Xu, Liane Galanti +1
In this paper, we investigate the Rademacher complexity of deep sparse neural networks, where each neuron receives a small number of inputs. We prove generalization bounds for mult…
Scalable algorithms for physics-informed neural and graph networks
Khemraj Shukla, Mengjia Xu, Nathaniel Trask +1
Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale proc…
DynG2G: An Efficient Stochastic Graph Embedding Method for Temporal Graphs
Mengjia Xu, Apoorva Vikram Singh, George Em Karniadakis
Dynamic graph embedding has gained great attention recently due to its capability of learning low dimensional graph representations for complex temporal graphs with high accuracy.…
Understanding graph embedding methods and their applications
Mengjia Xu
Graph analytics can lead to better quantitative understanding and control of complex networks, but traditional methods suffer from high computational cost and excessive memory requ…