14 citations · 32 across the 8 of their papers we have counts for
6 papers · 1 filter
ULTRA-MC: A Unified Approach to Learning Mixtures of Markov Chains via Hitting Times
Fabian Spaeh, Konstantinos Sotiropoulos, Charalampos E. Tsourakakis
This study introduces a novel approach for learning mixtures of Markov chains, a critical process applicable to various fields, including healthcare and the analysis of web users.…
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…
ADAMM: Anomaly Detection of Attributed Multi-graphs with Metadata: A Unified Neural Network Approach
Konstantinos Sotiropoulos, Lingxiao Zhao, Pierre Jinghong Liang +1
Given a complex graph database of node- and edge-attributed multi-graphs as well as associated metadata for each graph, how can we spot the anomalous instances? Many real-world pro…
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…