13 citations · 26 across the 9 of their papers we have counts for
9 papers
On the Role of Edge Dependency in Graph Generative Models
Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos +1
In this work, we introduce a novel evaluation framework for generative models of graphs, emphasizing the importance of model-generated graph overlap (Chanpuriya et al., 2021) to en…
Latent Random Steps as Relaxations of Max-Cut, Min-Cut, and More
Sudhanshu Chanpuriya, Cameron Musco
Algorithms for node clustering typically focus on finding homophilous structure in graphs. That is, they find sets of similar nodes with many edges within, rather than across, the…
Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs
Sudhanshu Chanpuriya, Ryan A. Rossi, Sungchul Kim +5
Temporal networks model a variety of important phenomena involving timed interactions between entities. Existing methods for machine learning on temporal networks generally exhibit…
Simplified Graph Convolution with Heterophily
Sudhanshu Chanpuriya, Cameron Musco
Recent work has shown that a simple, fast method called Simple Graph Convolution (SGC) (Wu et al., 2019), which eschews deep learning, is competitive with deep methods like graph c…
Exact Representation of Sparse Networks with Symmetric Nonnegative Embeddings
Sudhanshu Chanpuriya, Ryan A. Rossi, Anup Rao +4
Many models for undirected graphs are based on factorizing the graph's adjacency matrix; these models find a vector representation of each node such that the predicted probability…
On the Power of Edge Independent Graph Models
Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos +1
Why do many modern neural-network-based graph generative models fail to reproduce typical real-world network characteristics, such as high triangle density? In this work we study t…
DeepWalking Backwards: From Embeddings Back to Graphs
Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos +1
Low-dimensional node embeddings play a key role in analyzing graph datasets. However, little work studies exactly what information is encoded by popular embedding methods, and how…
Node Embeddings and Exact Low-Rank Representations of Complex Networks
Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos +1
Low-dimensional embeddings, from classical spectral embeddings to modern neural-net-inspired methods, are a cornerstone in the modeling and analysis of complex networks. Recent wor…
InfiniteWalk: Deep Network Embeddings as Laplacian Embeddings with a Nonlinearity
Sudhanshu Chanpuriya, Cameron Musco
The skip-gram model for learning word embeddings (Mikolov et al. 2013) has been widely popular, and DeepWalk (Perozzi et al. 2014), among other methods, has extended the model to l…