53 citations · 60 across the 9 of their papers we have counts for
10 papers
Complex Network Effects on the Robustness of Graph Convolutional Networks
Benjamin A. Miller, Kevin Chan, Tina Eliassi-Rad
Vertex classification -- the problem of identifying the class labels of nodes in a graph -- has applicability in a wide variety of domains. Examples include classifying subject are…
Using Overlapping Methods to Counter Adversaries in Community Detection
Benjamin A. Miller, Kevin Chan, Tina Eliassi-Rad
When dealing with large graphs, community detection is a useful data triage tool that can identify subsets of the network that a data analyst should investigate. In an adversarial…
Defense Against Shortest Path Attacks
Benjamin A. Miller, Zohair Shafi, Wheeler Ruml +3
Identifying shortest paths between nodes in a network is an important task in many applications. Recent work has shown that a malicious actor can manipulate a graph to make traffic…
Attacking Shortest Paths by Cutting Edges
Benjamin A. Miller, Zohair Shafi, Wheeler Ruml +3
Identifying shortest paths between nodes in a network is a common graph analysis problem that is important for many applications involving routing of resources. An adversary that c…
Optimal Edge Weight Perturbations to Attack Shortest Paths
Benjamin A. Miller, Zohair Shafi, Wheeler Ruml +3
Finding shortest paths in a given network (e.g., a computer network or a road network) is a well-studied task with many applications. We consider this task under the presence of an…
PATHATTACK: Attacking Shortest Paths in Complex Networks
Benjamin A. Miller, Zohair Shafi, Wheeler Ruml +3
Shortest paths in complex networks play key roles in many applications. Examples include routing packets in a computer network, routing traffic on a transportation network, and inf…
Topological Effects on Attacks Against Vertex Classification
Benjamin A. Miller, Mustafa Çamurcu, Alexander J. Gomez +2
Vertex classification is vulnerable to perturbations of both graph topology and vertex attributes, as shown in recent research. As in other machine learning domains, concerns about…
Model Selection Framework for Graph-based data
Rajmonda S. Caceres, Leah Weiner, Matthew C. Schmidt +2
Graphs are powerful abstractions for capturing complex relationships in diverse application settings. An active area of research focuses on theoretical models that define the gener…
Spectral Anomaly Detection in Very Large Graphs: Models, Noise, and Computational Complexity
Benjamin A. Miller, Nicholas Arcolano, Michael M. Wolf +1
Anomaly detection in massive networks has numerous theoretical and computational challenges, especially as the behavior to be detected becomes small in comparison to the larger net…
A Spectral Framework for Anomalous Subgraph Detection
Benjamin A. Miller, Michelle S. Beard, Patrick J. Wolfe +1
A wide variety of application domains are concerned with data consisting of entities and their relationships or connections, formally represented as graphs. Within these diverse ap…