15 Result(s)
within
Chapter
George Fletcher
-
Chapter and Conference Paper
Benchmarking GNNs with GenCAT Workbench
We present GenCAT Workbench, an end-to-end framework with which users can generate synthetic attributed graphs with node labels and evaluate their graph analytic methods, e.g., graph neural networks (GNNs), on...
-
Chapter and Conference Paper
GNN Transformation Framework for Improving Efficiency and Scalability
We propose a framework that automatically transforms non-scalable GNNs into precomputation-based GNNs which are efficient and scalable for large-scale graphs. The advantages of our framework are two-fold; 1) i...
-
Chapter and Conference Paper
ReLOG: A Unified Framework for Relationship-Based Access Control over Graph Databases
Relationship-Based Access Control (ReBAC) is a paradigm to specify access constraints in terms of interpersonal relationships. To express these graph-like constraints, a variety of ReBAC models with varying fe...
-
Chapter
Data Models
In this chapter, we introduce the property graph model. The property graph model is important for graph-based data management as it is implemented in many systems and used as a reference model for various rese...
-
Chapter
Introduction
Graph data management systems have experienced a renaissance in recent years. The reason for this is clear: with a confluence of trends in society, science, and technology, graph-structured data sets are incre...
-
Chapter
Query Languages
In this chapter we give a presentation of property graph query languages. We begin with the core language functionalities of graph navigation queries and (unions of) conjunctions of navigational queries. Our a...
-
Chapter
Query Processing
The diversity of applications in which graphs are used as primary data models led to a proliferation of a variety of graph processing tasks. For example, in social networks, one might be interested in looking ...
-
Chapter
Research Challenges
Throughout the book we have highlighted open research challenges. In this final chapter we collect and consolidate these challenges, providing an overview of what we see as important open problems for the grap...
-
Chapter
Constraints
Graph-shaped data differs from structured data mainly because of the lack of an underlying schema and metadata. Graph datasets typically blend values with metadata information without a clear distinction among...
-
Chapter
Data Structures and Indexes
A property graph is a complex structure requiring some care to be represented in the linear memory model1 of computers. A memory representation for property graphs should be: (1) concise, i.e., represent a given ...
-
Chapter
Physical Operators
This chapter discusses how graph-centric features used in the graph query languages of Chapter 3 introduce new challenges in physical query evaluation. We focus particularly on the design and implementation of...
-
Chapter and Conference Paper
Multi-strategy Differential Evolution
We propose the Multi-strategy Differential Evolution (MsDE) algorithm to construct and maintain a self-adaptive ensemble of search strategies while solving an optimization problem. The ensemble of strategies i...
-
Chapter and Conference Paper
Clustering-Structure Representative Sampling from Graph Streams
Most existing sampling algorithms on graphs (i.e., network-structured data) focus on sampling from memory-resident static graphs and assume the entire graphs are always available. However, the graphs encounter...
-
Chapter
Query Specification
We describe in this chapter graph query specification techniques to help users formulate path queries from examples provided as input or via graph exploration. This problem amounts to learning queries from exa...
-
Chapter
A Survey of Benchmarks for Graph-Processing Systems
Benchmarking is a process that informs the public about the capabilities of systems-under-test, focuses on expected and unexpected system-bottlenecks, and promises to facilitate system tuning and new systems d...
