Scope of IEEE Transactions on Computers

IEEE Transactions on Computers (TC) is a monthly publication with a wide distribution to researchers, developers, technical managers, and educators in the computer field. It publishes papers on research in areas of current interest to the readers. These areas include, but are not limited to, the following: a) computer organizations and architectures; b) operating systems, software systems, and communication protocols; c) real-time systems and embedded systems; d) digital devices, computer components, and interconnection networks; e) specification, design, prototyping, and testing methods and tools; f) performance, fault tolerance, reliability, security, and testability; g) case studies and experimental and theoretical evaluations; and h) new and important applications and trends.

Additional Information

IEEE Transactions on Computers publishes papers on research in areas of current interest to the readers, including but not limited to the following: a) computer organizations and architectures (multicores, manycores, accelerators, application-specific, domain-specific and reconfigurable processors, processing-in-memory, near-data processing, and  datacenters); b) operating systems, software systems, and cloud computing  (runtime systems, parallel and distributed systems, virtualization, and software-hardware interactions); c) real-time, mobile and embedded systems (Internet of Things, edge computing, wearables, actuators, and sensor networks); d) digital devices, computer components, and interconnection networks (volatile and non-volatile emerging memory technologies, solid-state devices for storage, emerging technologies for interconnects); e) specification, design, prototyping, and testing methods and tools; f) performance, fault tolerance, reliability, security, and testability (availability, scalability, energy/power management); g) case studies and experimental and theoretical evaluations (workload characterization, tracing, analyzing, and troubleshooting) ; and h) new and important applications and trends (computing issues for emerging technologies and applications, machine learning, approximate computing, quantum computing, neuromorphic computing, and analog computing).

TC is a scholarly, archival journal published monthly. In addition to full papers, brief contributions are also published.

Special Issue on Graph-Centric Computing: Algorithms, Systems, and Architectures
提出日: 2026-07-15

IEEE Transactions on Computers seeks original manuscripts for a special issue/section on Graph-Centric Computing: Algorithms, Systems, and Architectures scheduled to appear in March 2027.Graph-centric computing has emerged as a foundational paradigm for a broad range of modern workloads that challenge traditional computing systems. This special issue solicits high-quality, original research contributions that advance the state of the art in algorithms, systems, and architectures for graph-centric computation. Graph workloads arise across artificial intelligence, graph databases and property-graph systems, scientific computing, data-intensive analytics, and cyber-physical systems, including large-scale, static and dynamic, and attribute-rich graph workloads. These workloads place extreme pressure on memory hierarchies and data movement, motivating new approaches to locality management and the use of emerging and disaggregated memory technologies, and making scalability across cores, accelerators, memory hierarchies, and distributed systems a central challenge.

Topics of interest include, but are not limited to:

    Scalable graph algorithms and irregular computation models for static and dynamic graphs
    Graph-centric libraries, low-level application programming interfaces, and performance-portable programming interfaces for graph and sparse computations
    Graph-centric artificial intelligence: learning and inference on graph structures, graph neural networks, representation learning, knowledge graphs, and graph-enabled retrieval workflows
    Graph databases, property graphs, and complex graph data models
    Graph processing in scientific computing and scientific machine learning
    Memory systems, data movement, and locality management for graph-centric workloads
    Emerging and disaggregated memory technologies and their impact on graph processing
    Scalable systems, runtimes, and architectures for static and dynamic graph workloads
    Application-driven graph computing, including hardware design automation, finance and cybersecurity, transportation and cyber-physical infrastructure networks, and healthcare and biomedical analytics
    Emerging computing paradigms for graph processing

https://www.computer.org/digital-library/journals/tc/cfp-graph-centric-computing