# Graph Processing

## Memory

- Analysis and Optimization of the Memory Hierarchy forGraph Processing Workloads (HPCA 2019)
- Rethinking the Memory Hierarchy for Modern Languages (Micro 2018)
- Memory Hierarchy for Web Search (HPCA 2018)
- Accelerating PageRank using Partition-Centric Processing (ATC 2018)
- GPOP: A scalable cache- and memory-efficient framework for Graph Processing Over Partitions (2019)
- Exploiting Locality in Graph Analytics throughHardware-Accelerated Traversal Scheduling (Micro 2018)
- Data prefetching by dependence graph precomputation (ISCA 2018)
- Graph Prefetching Using Data Structure Knowledge (ICS 2016)
- GraphIA: An In-situ Accelerator forLarge-scale Graph Processing (MEMSYS 2018)
- Large-Scale Graph Processing on Emerging Storage Devices (FAST 2019)

## General/Survey

- Everything you always wanted to know about multicore graph processing but were afraid to ask (ATC 2017)
- Graph Processing on GPUs: A Survey (ACM Computing Surveys 2018)

## Graph Abstraction

## Out-Of-Core

- Wonderland: A Novel Abstraction-Based Out-Of-Core Graph Processing System (Asplos 2018)
- Graphene: Fine-Grained IO Management for Graph Computing (FAST 2017)
- To Push or To Pull: On Reducing Communication and Synchronization in Graph Computations (HPDC 2017)
- Energy efficient architecture for graph analytics accelerators (ISCA 2016)
- Exploring the Hidden Dimension in Graph Processing (OSDI 2016)
- Efficient Processing of Large Graphs via Input Reduction (HPDC 2016)
- Chaos: Scale-out Graph Processing from Secondary Storage (SOSP 2015)
- FlashGraph: Processing Billion-Node Graphs on an Array of Commodity SSDs (FAST 2015)
- PowerLyra: differentiated graph computation and partitioning on skewed graphs (Eurosys 2015)
- X-Stream: Edge-centric Graph Processing using Streaming Partitions (SOSP 2013)
- GraphChi: Large-Scale Graph Computation on Just a PC (OSDI 2012) (Video)

## In-memory (Cluster)

- GraphLab: A New Framework For Parallel Machine Learning (arXiv 2014) (Video)
- Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud (arXiv 2012)
- PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs (OSDI 2012)

## New Memory

## PIM

- GraphP: Reducing Communication for PIM-based Graph Processing with Efficient Data Partition (HPCA 2018)
- GraphPIM: Enabling Instruction-Level PIM Offloading in Graph Computing Frameworks (HPCA 2017)
- A Scalable Processing-in-Memory Accelerator for Parallel Graph Processing (ISCA 2015) (Slides)

## GPU

- Frog: Asynchronous graph processing on GPU with hybrid coloring model (TOPDC 2018)
- Optimizing Graph Processing on GPUs (TPDS 2017)
- Garaph: Efficient GPU-accelerated Graph Processing on a Single Machine with Balanced Replication (ATC 2017)
- Everything you always wanted to know about multicore graph processing but were afraid to ask (ATC 2017)
- Gunrock: GPU Graph Analytics (TOPC 2017)
- Gunrock: A High-Performance Graph Processing Library on the GPU (PPoPP 2016)

## Graph Database

**VIDEO:**Graph Databases Will Change Your Freakinâ€™ Life (Best Intro Into Graph Databases)**VIDEO:**What are Graph Databases and Why should I care? - Dave Bechberger (NDC London 2017)

## Algorithms

- ICML 2017
- Connected Subgraph Detection with Mirror Descent on SDPs (ICML 2017)
- Coresets for Vector Summarization with Applications to Network Graphs (ICML 2017)
- Analysis and Optimization of Graph Decompositions by Lifted Multicuts (ICML 2017)
- Cost-Optimal Learning of Causal Graphs (ICML 2017)
- Bayesian inference on random simple graphs with power law degree distributions (ICML 2017)
- Deriving Neural Architectures from Sequence and Graph Kernels (ICML 2017)
- Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs (ICML 2017)
- Graph-based Isometry Invariant Representation Learning (ICML 2017)