Demo Data from EDBT 2026 / reviewers in the wild / expert
Runjie Ma
dblp:307/2926
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0003-6289-680XORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Databases, data management, data science and information retrieval · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Graph learning · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Distributed systems · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Graph learning
graph construction |
0.8 | 1 | 2024 | GraphStorm: All-in-one Graph Machine Learning Framework for Industry Applications · KDD 2024 |
Machine learning › Graph learning
graph neural network |
0.8 | 1 | 2024 | GraphStorm: All-in-one Graph Machine Learning Framework for Industry Applications · KDD 2024 |
Machine learning › Graph learning › graph neural network
scalable graph neural network |
0.8 | 1 | 2024 | GraphStorm: All-in-one Graph Machine Learning Framework for Industry Applications · KDD 2024 |
Methods — techniques the papers use, named apart from their topics
graph neural network · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | GraphStorm: All-in-one Graph Machine Learning Framework for Industry ApplicationsabstractGraph machine learning (GML) is effective in many business applications. However, making GML easy to use and applicable to industry applications with massive datasets remain challenging. We developed GraphStorm, which provides an end-to-end solution for scalable graph construction, graph model training and inference. GraphStorm has the following desirable properties: (a) Easy to use: it can perform graph construction and model training and inference with just a single command; (b) Expert-friendly: GraphStorm contains many advanced GML modeling techniques to handle complex graph data and improve model performance; (c) Scalable: every component in GraphStorm can operate on graphs with billions of nodes and can scale model training and inference to different hardware without changing any code. GraphStorm has been used and deployed for over a dozen billion-scale industry applications after its release in May 2023. It is open-sourced in Github: https://github.com/awslabs/graphstorm. Da Zheng 0004, Xiang Song 0003, Qi Zhu 0008, Jian Zhang 0113, Theodore Vasiloudis, Runjie Ma, Houyu Zhang, Zichen Wang 0002, Soji Adeshina, Israt Nisa, Alejandro Mottini, Qingjun Cui, Huzefa Rangwala, Belinda Zeng, Christos Faloutsos, George Karypis |
KDD | 6 |