Search Results for author:
Found 6 papers, 4 papers with code
On the Role of Edge Dependency in Graph Generative Models
Our evaluation, conducted on real-world datasets, focuses on assessing the output quality and overlap of our proposed models in comparison to other popular models.
ADAMM: Anomaly Detection of Attributed Multi-graphs with Metadata: A Unified Neural Network Approach
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?
On the Power of Edge Independent Graph Models
We prove that subject to a bounded overlap condition, which ensures that the model does not simply memorize a single graph, edge independent models are inherently limited in their ability to generate graphs with high triangle and other subgraph densities.
DeepWalking Backwards: From Embeddings Back to Graphs
Our findings are a step towards a more rigorous understanding of exactly what information embeddings encode about the input graph, and why this information is useful for learning tasks.
Node Embeddings and Exact Low-Rank Representations of Complex Networks
In this work we show that the results of Seshadhri et al. are intimately connected to the model they use rather than the low-dimensional structure of complex networks.
TwitterMancer: Predicting Interactions on Twitter Accurately
This paper investigates the interplay between different types of user interactions on Twitter, with respect to predicting missing or unseen interactions.
