Publications

On the Role of Edge Dependency in Graph Generative Models
Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, and Charalampos Tsourakakis.
In submission.
Paper

Exact Representation of Sparse Networks with Symmetric Nonnegative Embeddings
Sudhanshu Chanpuriya, Ryan A. Rossi, Anup Rao, Tung Mai,
Nedim Lipka, Zhao Song, and Cameron Musco.
Neural Information Processing Systems (NeurIPS) 2023.
Paper | Code

Latent Random Steps as Relaxations of Max-Cut, Min-Cut, and More
Sudhanshu Chanpuriya and Cameron Musco.
Differentiable Almost Everything Workshop at ICML 2023.
Paper

Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs
Sudhanshu Chanpuriya, Ryan A. Rossi, Sungchul Kim, Tong Yu,
Jane Hoffswell, Nedim Lipka, Shunan Guo, and Cameron Musco.
International Conference on Learning Representations (ICLR) 2023.
Paper | Code

Simplified Graph Convolution with Heterophily
Sudhanshu Chanpuriya and Cameron Musco.
Neural Information Processing Systems (NeurIPS) 2022.
Paper | Code

On the Power of Edge Independent Graph Models
Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, and Charalampos Tsourakakis.
Neural Information Processing Systems (NeurIPS) 2021.
Paper | Code

DeepWalking Backwards: From Embeddings Back to Graphs
Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, and Charalampos Tsourakakis.
International Conference on Machine Learning (ICML) 2021.
Paper | Code

Node Embeddings and Exact Low-Rank Representations of Complex Networks
Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, and Charalampos Tsourakakis.
Neural Information Processing Systems (NeurIPS) 2020.
Paper | Code

InfiniteWalk: Deep Network Embeddings as Laplacian Embeddings with a Nonlinearity
Sudhanshu Chanpuriya and Cameron Musco.
Knowledge Discovery and Data Mining (KDD) 2020.
Paper | Code