- A1: A Distributed In-Memory Graph Database
- ASPIRE: Exploiting Asynchronous Parallelism in Iterative Algorithms using a Relaxed Consistency based DSM
- AeonG: An Efficient Built-in Temporal Support in Graph Databases (Extended Version)
- AsynGraph: Maximizing Data Parallelism for Efficient Iterative Graph Processing on GPUs
- Auxo: A Temporal Graph Management System
- BG3: A Cost Effective and I/O Efficient Graph Database in ByteDance
- Blaze: Fast Graph Processing on Fast SSDs
- BlitzG: Exploiting high-bandwidth networks for fast graph processing
- Blogel: A Block-Centric Framework for Distributed Computation on Real-World Graphs
- ByteGraph: A High-Performance Distributed Graph Database in ByteDance
- CGgraph: An Ultra-fast Graph Processing System on Modern Commodity CPU-GPU Co-processor
- CGraph: A correlations-aware approach for efficient concurrent iterative graph processing
- CLIP: A Disk I/O Focused Parallel Out-of-core Graph Processing System
- CSMqGraph: Coarse-Grained and Multi-external-storage Multi-queue I/O Management for Graph Computing
- Chaos: Scale-out Graph Processing from Secondary Storage
- Chronograph: A Distributed Processing Platform for Online and Batch Computations on Event-sourced Graphs
- Chronos: A Graph Engine for Temporal Graph Analysis
- CoRAL: Confined Recovery in Distributed Asynchronous Graph Processing
- CommonGraph: Graph Analytics on Evolving Data
- CuSha: Vertex-Centric Graph Processing on GPUs
- Cymbalo: An Efficient Graph Processing Framework for Machine Learning
- D2Graph: An Efficient and Unified Out-of-Core Graph Computing Model
- DD-Graph: A Highly Cost-Effective Distributed Disk-based Graph-Processing Framework
- DFOGraph: An I/O- and Communication-Efficient System for Distributed Fully-out-of-Core Graph Processing
- DZiG: Sparsity-Aware Incremental Processing of Streaming Graphs
- DepGraph: A Dependency-Driven Accelerator for Efficient Iterative Graph Processing
- DiGraph: An Efficient Path-based Iterative Directed Graph Processing System on Multiple GPUs
- Differential dataflow: Differential dataflow
- Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud
- DiterGraph: Toward I/O-Efficient Incremental Computation over Large Graphs with Billion Edges
- DynamoGraph: A Distributed System for Large-scale, Temporal Graph Processing, its Implementation and First Observations
- EGraph: Efficient concurrent GPU-based dynamic graph processing
- EPGraph: An Efficient Graph Computing Model in Persistent Memory System
- EmptyHeaded: A Relational Engine for Graph Processing
- FBSGraph: Accelerating Asynchronous Graph Processing via Forward and Backward Sweeping
- FENNEL: Streaming Graph Partitioning for Massive Scale Graphs
- Fargraph+: Excavating the parallelism of graph processing workload on RDMA-based far memory system
- FlashGraph: Processing Billion-Node Graphs on an Array of Commodity SSDs
- FOG: A Fast Out-of-Core Graph Processing Framework
- ForeGraph: Exploring Large-scale Graph Processing on Multi-FPGA Architecture
- Frog: Asynchronous graph processing on GPU with hybrid coloring model
- G-Store: High-Performance Graph Store for Trillion-Edge Processing
- G-Tran: A High Performance Distributed Graph Database with a Decentralized Architecture
- GBASE: A Scalable and General Graph Management System
- GFlink: An In-Memory Computing Architecture on Heterogeneous CPU-GPU Clusters for Big Data
- GGraph: An Efficient Structure-Aware Approach for Iterative Graph Processing
- GPOP: A scalable cache- and memory-efficient framework for Graph Processing Over Partitions
- GPS: A Graph Processing System
- GRAM: Scaling Graph Computation to the Trillions
- GRAPE: Parallelizing Sequential Graph Computations
- GRE: A Graph Runtime Engine for Large-Scale Distributed Graph-Parallel Applications
- GStream: A Graph Streaming Processing Method for Large-scale Graphs on GPUs
- Garaph: Efficient GPU-accelerated Graph Processing on a Single Machine with Balanced Replication
- GasCL: A Vertex-Centric Graph Model for GPUs
- GeaFlow: A Graph Extended and Accelerated Dataflow System
- Gemini: A Computation-Centric Distributed Graph Processing System
- Giraph Unchained: Barrierless Asynchronous Parallel Execution in Pregel-like Graph Processing Systems
- Giraph
- GoFFish: A Sub-graph Centric Framework for Large-Scale Graph Analytics
- GraPU: Accelerate Streaming Graph Analysis through Preprocessing Buffered Updates
- GraVF: A Vertex-Centric Distributed Graph Processing Framework on FPGAs
- Gradoop: Analyzing Temporal Graphs with Gradoop
- GrapH: Traffic-Aware Graph Processing
- Graph3S: A Simple, Speedy and Scalable Distributed Graph Processing System
- GraphA: An efficient ReRAM-based architecture to accelerate large scale graph processing
- GraphABCD: Scaling Out Graph Analytics with Asynchronous Block Coordinate Descent
- GraphBolt: Dependency-Driven Synchronous Processing of Streaming Graphs
- GraphBuilder: Scalable Graph ETL Framework
- GraphCP: An I/O-Efficient Concurrent Graph Processing Framework
- GraphCage: Cache Aware Graph Processing on GPUs
- GraphChi: Large-Scale Graph Computation on Just a PC
- GraphD: Distributed Vertex-Centric Graph Processing Beyond the Memory Limit
- GraphDuo: A Dual-Model Graph Processing Framework
- GraphFly: Efficient Asynchronous Streaming Graphs Processing via Dependency-Flow
- GraphGen: An FPGA Framework for Vertex-Centric Graph Computation
- GraphGrind: addressing load imbalance of graph partitioning
- GraphH(1): High Performance Big Graph Analytics in Small Clusters
- GraphH(2): A Processing-in-Memory Architecture for Large-scale Graph Processing
- GraphIA: An In-situ Accelerator for Large-scale Graph Processing
- GraphIn: An Online High Performance Incremental Graph Processing Framework
- GraphLab: A New Framework For Parallel Machine Learning
- GraphM: An Efficient Storage System for High Throughput of Concurrent Graph Processing
- GraphMP(1): An Efficient Semi-External-Memory Big Graph Processing System on a Single Machine
- GraphMP(2): I/O-Efficient Big Graph Analytics on a Single Commodity Machine
- GraphMap: scalable iterative graph processing using NoSQL
- GraphMat: High performance graph analytics made productive
- GraphOne: A Data Store for Real-time Analytics on Evolving Graphs
- GraphP: Reducing Communication for PIM-based Graph Processing with Efficient Data Partition
- GraphPEG: Accelerating Graph Processing on GPUs
- GraphPIM: Enabling Instruction-Level PIM Offloading in Graph Computing Frameworks
- GraphPhi: Efficient Parallel Graph Processing on Emerging Throughput-oriented Architectures
- GraphPulse: An Event-Driven Hardware Accelerator for Asynchronous Graph Processing
- GraphQ: Graph Query Processing with Abstraction Refinement
- GraphR: Accelerating Graph Processing Using ReRAM
- GraphReduce: Processing Large-Scale Graphs on Accelerator-Based Systems
- GraphSD: A State and Dependency aware Out-of-Core Graph Processing System
- GraphScSh: Efficient I/O Scheduling and Graph Sharing for Concurrent Graph Processing
- GraphScope: A Unified Engine For Big Graph Processing
- GraphTides: A Framework for Evaluating Stream-based Graph Processing Platforms
- GraphTinker: A High Performance Data Structure for Dynamic Graph Processing
- GraphTune: An Efficient Dependency-Aware Substrate to Alleviate Irregularity in Concurrent Graph Processing
- GraphTwist: Fast Iterative Graph Computation with Two-tier Optimizations
- GraphX: A Resilient Distributed Graph System on Spark
- GraphZ: Improving the Performance of Large-Scale Graph Analytics on Small-Scale Machines
- Graphene: Fine-Grained IO Management for Graph Computing
- Graphflow: An Active Graph Database
- Graphie: Large-Scale Asynchronous Graph Traversals on Just a GPU
- Graspan: A Single-machine Disk-based Graph System for Interprocedural Static Analyses of Large-scale Systems Code
- Grasper: A High Performance Distributed System for OLAP on Property Graphs
- GridGraph: Large-Scale Graph Processing on a Single Machine Using 2-Level Hierarchical Partitioning
- Groute: Asynchronous Multi-GPU Programming Model with Applications to Large-scale Graph Processing
- HGraph: I/O-efficient Distributed and Iterative Graph Computing by Hybrid Pushing/Pulling
- HPGraph: A High Parallel Graph Processing System Based on Flash Array
- HUS-Graph: I/O-Efficient Out-of-Core Graph Processing with Hybrid Update Strategy
- HaLoop: Efficient Iterative Data Processing on Large Clusters
- HipG: Parallel Processing of Large-Scale Graphs
- HitGraph: High-throughput Graph Processing Framework on FPGA
- HotGraph: Efficient Asynchronous Processing for Real-World Graphs
- HyTGraph: GPU-Accelerated Graph Processing with Hybrid Transfer Management
- HyVE: Hybrid Vertex-Edge Memory Hierarchy for Energy-Efficient Graph Processing
- HybridGraph: Hybrid Pulling:Pushing for I:O-Efficient Distributed and Iterative Graph Computing
- ImmortalGraph: A System for Storage and Analysis of Temporal Graphs
- JanusGraph
- JetStream: Graph Analytics on Streaming Data with Event-Driven Hardware Accelerator
- KickStarter: Fast and Accurate Computations on Streaming Graphs via Trimmed Approximations
- Kineograph: Taking the Pulse of a Fast-Changing and Connected World
- L-PowerGraph: a lightweight distributed graph-parallel communication mechanism
- LCC-Graph: A High-Performance Graph-Processing Framework with Low Communication Costs
- LFGraph: Simple and Fast Distributed Graph Analytics
- LLAMA: Efficient Graph Analytics Using Large Multiversioned Arrays.
- LOSC: Efficient Out-of-Core Graph Processing with Locality-optimized Subgraph Construction
- LSGraph: A Locality-centric High-performance Streaming Graph Engine
- LargeGraph: An Efficient Dependency-Aware GPU-Accelerated Large-Scale Graph Processing
- LazyGraph: Lazy Data Coherency for Replicas in Distributed Graph-Parallel Computation
- LightGraph: Lighten Communication in Distributed Graph-Parallel Processing
- Ligra: A Lightweight Graph Processing Framework for Shared Memory
- LiveGraph: A Transactional Graph Storage System with Purely Sequential Adjacency List Scans
- Lumos: Dependency-Driven Disk-based Graph Processing
- MMap: Fast Billion-Scale Graph Computation on a PC via Memory Mapping
- MOCgraph: Scalable Distributed Graph Processing Using Message Online Computing
- MOSAIC: Processing a Trillion-Edge Graph on a Single Machine
- Maiter: An Asynchronous Graph Processing Framework for Delta-based Accumulative Iterative Computation
- MapGraph: A High Level API for Fast Development of High Performance Graph Analytics on GPUs
- Medusa: Simplified Graph Processing on GPUs
- Mizan: A System for Dynamic Load Balancing in Large-scale Graph Processing
- MultiLogVC: Efficient Out-of-Core Graph Processing Framework for Flash Storage
- NGraph: Parallel Graph Processing in Hybrid Memory Systems
- NPGraph: An Efficient Graph Computing Model in NUMA-Based Persistent Memory Systems
- NScale: Neighborhood-centric Analytics on Large Graphs
- NXgraph: An Efficient Graph Processing System on a Single Machine*
- Naiad: A Timely Dataflow System
- Neo4j
- PGAbB: A Block-Based Graph Processing Framework for Heterogeneous Platforms
- PGX.D: A Fast Distributed Graph Processing Engine
- PartitionedVC: Partitioned External Memory Graph Analytics Framework for SSDs
- PathGraph: A Path Centric Graph Processing System
- Pimiento: A Vertex-Centric Graph-Processing Framework on a Single Machine
- PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs
- PowerLyra: Differentiated Graph Computation and Partitioning on Skewed Graphs
- PrIter: A Distributed Framework for Prioritized Iterative Computations
- Pregel: A System for Large-Scale Graph Processing
- Pregelix: Big(ger) Graph Analytics on A Dataflow Engine
- Quegel: A General-Purpose Query-Centric Framework for Querying Big Graphs
- Raphtory: Streaming Analysis Of Distributed Temporal Graphs
- ReGraph: A Graph Processing Framework that Alternately Shrinks and Repartitions the Graph
- Ringo: Interactive Graph Analytics on Big-Memory Machines
- RisGraph: A Real-Time Streaming System for Evolving Graphs to Support Sub-millisecond Per-update Analysis at Millions Ops/s
- SGraph: A Distributed Streaming System for Processing Big Graphs
- STINGER: High Performance Data Structure for Streaming Graphs
- SaGraph: A Similarity-aware Hardware Accelerator for Temporal Graph Processing
- ScalaGraph: A Scalable Accelerator for Massively Parallel Graph Processing
- ScaleG: A Distributed Disk-based System for Vertex-centric Graph Processing
- Scaph: Scalable GPU-Accelerated Graph Processing with Value-Driven Differential Scheduling
- Seraph: an Efficient, Low-cost System for Concurrent Graph Processing
- ShenTu: Processing Multi-Trillion Edge Graphs on Millions of Cores in Seconds
- Subway: Minimizing Data Transfer during Out-of-GPU-Memory Graph Processing
- SympleGraph: Distributed Graph Processing with Precise Loop-Carried Dependency Guarantee
- TDGraph: A Topology-Driven Accelerator for High-Performance Streaming Graph Processing
- TIDE: Dynamic Interaction Graphs with Probabilistic Edge Decay
- TeGraph+: Scalable Temporal Graph Processing Enabling Flexible Edge Modifications
- TeGraph: A Novel General-Purpose Temporal Graph Computing Engine
- Tegra: Efficient Ad-Hoc Analytics on Evolving Graphs
- ThunderGP: HLS-based graph processing framework on FPGAs
- TigerGraph: A Native MPP Graph Database
- Tigr: Transforming Irregular Graphs for GPU-Friendly Graph Processing
- Tornado: A System For Real-Time Iterative Analysis Over Evolving Data
- Trinity: A Distributed Graph Engine on a Memory Cloud
- Tripoline: Generalized Incremental Graph Processing via Graph Triangle Inequality
- TuGraph
- TurboGraph: A Fast Parallel Graph Engine Handling Billion-scale Graphs in a Single PC
- VENUS: A System for Streamlined Graph Computation on a Single PC
- VGL: a high-performance graph processing framework for the NEC SX-Aurora TSUBASA vector architecture
- VeilGraph: Approximating Graph Streams
- Version Traveler: Fast and Memory-Efficient Version Switching in Graph Processing Systems
- Weaver: A High-Performance, Transactional Graph Database Based on Refinable Timestamps
- WolfGraph: the Edge-Centric graph processing on GPU
- Wonderland: A Novel Abstraction-Based Out-Of-Core Graph Processing System
- X-Stream: Edge-centric Graph Processing using Streaming Partitions
- XPGraph: XPline-Friendly Persistent Memory Graph Stores for Large-Scale Evolving Graphs
- Zorro: Zero-Cost Reactive Failure Recovery in Distributed Graph Processing
- faimGraph: High Performance Management of Fully-Dynamic Graphs under tight Memory Constraints on the GPU
- iGraph: an incremental data processing system for dynamic graph
- iMapReduce: A Distributed Computing Framework for Iterative Computation
- iPregel: A Combiner-Based In-Memory Shared Memory Vertex-Centric Framework
- iTurboGraph: Scaling and Automating Incremental Graph Analytics
- xDGP: A Dynamic Graph Processing System with Adaptive Partitioning