6.886 Graph Analytics Spring 2018

Brian WheatmanHyun Ryong Lee

Omar Obeya

Thinking Like a Vertex: A Survey of Vertex-Centric Frameworks for Large-Scale Distributed Graph Processing (Survey)

Big Graph Analytics Platforms (Survey)

Parallel Graph Analytics

Navigating the Maze of Graph Analytics Frameworks using Massive Graph Datasets

How Well do Graph-Processing Platforms Perform? An Empirical Performance Evaluation and Analysis

Large-Scale Distributed Graph Computing Systems: An Experimental Evaluation

An Experimental Comparison of Pregel-like Graph Processing Systems

Architectural Implications on the Performance and Cost of Graph Analytics Systems

An Evaluation and Analysis of Graph Processing Frameworks on Five Key Issues

The Parallel BGL: A Generic Library for Distributed Graph Computations

Signal/Collect: Graph Algorithms for the (Semantic) Web

GPS: A Graph Processing System

Simplifying Scalable Graph Processing with a Domain-Specific Language

Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud

PowerLyra: Differentiated Graph Computation and Partitioning on Skewed Graphs

Exploring the Hidden Dimension in Graph Processing

GraphX: Graph Processing in a Distributed Dataflow Framework

Asynchronous Large-Scale Graph Processing Made Easy

ASPIRE: Exploiting Asynchronous Parallelism in Iterative Algorithms using a Relaxed Consistency based DSM

Efficient Processing of Large Graphs via Input Reduction

Mizan: A System for Dynamic Load Balancing in Large-scale Graph Processing

GoFFish: A Sub-graph Centric Framework for Large-Scale Graph Analytics

KLA: A New Algorithmic Paradigm for Parallel Graph Computations

Computation and Communication Efficient Graph Processing with Distributed Immutable View

SYNC or ASYNC: Time to Fuse for Distributed Graph-Parallel Computation

Scaling Iterative Graph Computations with GraphMap

Presto: Distributed Machine Learning and Graph Processing with Sparse Matrices

From "Think Like a Vertex" to "Think Like a Graph"

NScale: neighborhood-centric large-scale graph analytics in the cloud

High Performance Graph Processing with Locality Oriented Design

TuX2: Distributed Graph Computation for Machine Learning

Latency-Tolerant Software Distributed Shared Memory

Blogel: A Block-Centric Framework for Distributed Computation on Real-World Graphs

PrIter: A Distributed Framework for Prioritized Iterative Computations

Fast Iterative Graph Computation with Block Updates

Maiter: An Asynchronous Graph Processing Framework for Delta-Based Accumulative Iterative Computation

Parallelizing Sequential Graph Computations

GraM: Scaling Graph Computation to the Trillions

LazyGraph: Lazy Data Coherency for Replicas in Distributed Graph-Parallel Computation

One Trillion Edges: Graph Processing at Facebook-Scale

An Experimental Comparison of Partitioning Strategies in Distributed Graph Processing

PGX.D: A Fast Distributed Graph Processing Engine

CoRAL: Confined Recovery in Distributed Asynchronous Graph Processing

Zorro: zero-cost reactive failure recovery in distributed graph processing

PAGE: A Partition Aware Engine for Parallel Graph Computation

MOCgraph: Scalable Distributed Graph Processing Using Message Online Computing

Vertexica: Your Relational Friend for Graph Analytics!

Graph Analytics using Vertica Relational Database

GraphiQL: A Graph Intuitive Query Language for Relational Databases

The case against specialized graph analytics engines