This paper studies incremental graph partitioning. Given a (vertex-cut or edge-cut) partition
C (G) of a graph G and updates ΔG to G, it is to compute changes ΔO to C (G), yielding a …

Graphs are a tremendously suitable data representations that model the relationships of
entities in many application domains, such as recommendation systems, machine learning …

High-dimensional vector similarity search (HVSS) is gaining prominence as a powerful tool
for various data science and AI applications. As vector data scales up, in-memory indexes …

Shared memory systems are becoming increasingly complex as they typically integrate
several storage devices. That brings different access latencies or bandwidth rates …

Graph partitioning is a mandatory step in large-scale distributed graph processing. When
partitioning real-world power-law graphs, the edge partitioning algorithm performs better …

Modular robots are defined as autonomous kinematic machines with variable morphology.
They are composed of several thousands or even millions of modules that are able to …

Recently, a class of multilevel graph clustering (graph partitioning) algorithms have been
extensively studied due to their practical utility. Although these newly proposed algorithms …

Public schools in the United States offer tuition-free primary and secondary education to
their students, and are divided into school districts funded by the local and state …

With the increasing scale of integrated circuits, hypergraph partitioning is usually applied to
Very Large Scale Integration (VLSI) circuit layout and other applications to reduce the …

Geospatial knowledge graphs provide critical technology for integrating geographic
information and semantic knowledge, which are very useful for geographic data analysis. As …