Accelerating Force-directed Graph Layout with Processing-in-Memory Architecture

Graph visualization with proper layout is widely applied to understand the relationship between entities in a complex system and the topological structure information is mainly used. Real-world graphs often have the community structures property which is ignored in many existing graph layout methods. Thus, we propose a multivariate network layout method using the force-directed method with attribute constraints. This method can effectively take into account the hierarchical structure, connection strength, and quantitative comparison between communities. First, the layout of community centers is generated by a force-directed algorithm in which node count of the community is taken as constraints to enable area balance of community; Second, community force based on node attribute is added in the force-directed algorithm to maintain the community clarity. A visualization system is also developed to allow users to interactively generate community structure-aware layout results. qualitative and quantitative evaluation of the results verifies the usability and effectiveness of the proposed method by comparing it with other methods.

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Accelerating Force-directed Graph Layout with Processing-in-Memory Architecture

Cited by 3 publications

References 36 publications

Hardware-aware 3D Model Workload Selection and Characterization for Graphics and ML Applications

Hardware-aware 3D Model Workload Selection and Characterization for Graphics and ML Applications

AritPIM: High-Throughput In-Memory Arithmetic

Multivariate Network Layout Using Force-Directed Method with Attribute Constraints

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