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Brian Gallagher
Researcher at Lawrence Livermore National Laboratory
Publications - 60
Citations - 5689
Brian Gallagher is an academic researcher from Lawrence Livermore National Laboratory. The author has contributed to research in topics: Statistical relational learning & Relational database. The author has an hindex of 24, co-authored 60 publications receiving 5119 citations. Previous affiliations of Brian Gallagher include University of Massachusetts Amherst.
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Proceedings ArticleDOI
MaxProp: Routing for Vehicle-Based Disruption-Tolerant Networks
TL;DR: The evaluations show that MaxProp performs better than protocols that have access to an oracle that knows the schedule of meetings between peers, and performs well in a wide variety of DTN environments.
Proceedings ArticleDOI
RolX: structural role extraction & mining in large graphs
Keith Henderson,Brian Gallagher,Tina Eliassi-Rad,Hanghang Tong,Sugato Basu,Leman Akoglu,Danai Koutra,Christos Faloutsos,Lei Li +8 more
TL;DR: This paper proposes RolX (Role eXtraction), a scalable (linear in the number of edges), unsupervised learning approach for automatically extracting structural roles from general network data, and compares network role discovery with network community discovery.
Proceedings ArticleDOI
Why collective inference improves relational classification
TL;DR: This work describes the necessary and sufficient conditions for reduced classification error based on experiments with real and simulated data, and characterizes different types of statistical models used for making inference in relational data.
Proceedings ArticleDOI
Fast best-effort pattern matching in large attributed graphs
TL;DR: The G-Ray ("Graph X-Ray") method finds high-quality subgraphs in time linear on the size of the data graph, where nodes have attributes, such as a social network where the nodes are labelled with each person's job title.
Proceedings ArticleDOI
It's who you know: graph mining using recursive structural features
Keith Henderson,Brian Gallagher,Lei Li,Leman Akoglu,Tina Eliassi-Rad,Hanghang Tong,Christos Faloutsos +6 more
TL;DR: ReFeX (Recursive Feature eXtraction), a novel algorithm, that recursively combines local features with neighborhood features; and outputs regional features -- capturing "behavioral" information in large graphs, is proposed.