Graph sampling is a technique to pick a subset of vertices and/or edges from original graph.
It has a wide spectrum of applications, eg survey hidden population in sociology [54] …

Given a social network with diffusion probabilities as edge weights and a positive integer k,
which k nodes should be chosen for initial injection of information to maximize the influence …

Processing-In-Memory (PIM) is an effective technique that reduces data movements by
integrating processing units within memory. The recent advance of “big data” and 3D …

Processing-In-Memory (PIM) architectures based on recent technology advances (eg,
Hybrid Memory Cube) demonstrate great potential for graph processing. However, existing …

This paper surveys domain-specific architectures (DSAs) built from two emerging memory
technologies. Hybrid memory cube (HMC) and high bandwidth memory (HBM) can reduce …

Sampling is a widely used graph reduction technique to accelerate graph computations and
simplify graph visualizations. By comprehensively analyzing the literature on graph …

Community detection is an important research area in social networks analysis where we
are concerned with discovering the structure of the social network. Detecting communities is …

We introduce in this paper the mechanism of graph random features (GRFs). GRFs can be
used to construct unbiased randomized estimators of several important kernels defined on …

Graph Transformers have proved their advantages in graph data mining with elaborate
Positional Encodings, especially in graph-level tasks. However, their application in the node …

We propose Slim Graph: the first programming model and framework for practical lossy
graph compression that facilitates high-performance approximate graph processing …