You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Copy file name to clipboardExpand all lines: blog/index.html
+25-25Lines changed: 25 additions & 25 deletions
Original file line number
Diff line number
Diff line change
@@ -56,6 +56,31 @@ <h1 class="page-title">
56
56
57
57
58
58
59
+
<articleclass="post">
60
+
<headerclass="post-header">
61
+
<h2class="post-title"><ahref="/blog/tech/2026/07/02/release-v0.1.3-gds.html">NeuG v0.1.3: The Era of Graph Computing Inside the Graph Database</a></h2>
<h2class="post-title"><ahref="/blog/tech/2026/06/16/NEUGBI-BLOG.html">How does AI affect U.S. Employment? Replicating Harvard Business School Research with NeuGBI</a></h2>
<h2class="post-title"><ahref="/blog/tech/2025/04/26/Why-DuckDB-Is-Such-A-Good-Database-Product.html">Why DuckDB Is Such A Good Database Product</a></h2>
Copy file name to clipboardExpand all lines: blog/page2/index.html
+25-26Lines changed: 25 additions & 26 deletions
Original file line number
Diff line number
Diff line change
@@ -60,6 +60,31 @@ <h1 class="page-title">
60
60
61
61
62
62
63
+
<articleclass="post">
64
+
<headerclass="post-header">
65
+
<h2class="post-title"><ahref="/blog/tech/2025/04/26/Why-DuckDB-Is-Such-A-Good-Database-Product.html">Why DuckDB Is Such A Good Database Product</a></h2>
<h2class="post-title"><ahref="/blog/tech/2025/03/25/GOpt-Optimization-Process.html">GOpt Optimization Process: An In-depth Case Study Based on LDBC SNB Queries</a></h2>
<h2class="post-title"><ahref="/blog/tech/2023/08/06/Dynamic-Graph-Sampling-Service-for-Realtime-GNN-Inference-at-Scale.html">Dynamic Graph Sampling Service for Realtime GNN Inference at Scale</a></h2>
Graph neural networks(GNNs) learn graph vertex representations by aggregating multi-hop neighbor information. Industrial applications often adopt mini-batch training to scale out GNNs on large-scale graphs, where neighbor sampling is used during both model training and inference. Since the structure and attributes of real-world graphs often change dynamically, it is imperative that the inferred vertex representation can accurately reflect these updates.</p>
Copy file name to clipboardExpand all lines: blog/page3/index.html
+26-26Lines changed: 26 additions & 26 deletions
Original file line number
Diff line number
Diff line change
@@ -60,6 +60,32 @@ <h1 class="page-title">
60
60
61
61
62
62
63
+
<articleclass="post">
64
+
<headerclass="post-header">
65
+
<h2class="post-title"><ahref="/blog/tech/2023/08/06/Dynamic-Graph-Sampling-Service-for-Realtime-GNN-Inference-at-Scale.html">Dynamic Graph Sampling Service for Realtime GNN Inference at Scale</a></h2>
Graph neural networks(GNNs) learn graph vertex representations by aggregating multi-hop neighbor information. Industrial applications often adopt mini-batch training to scale out GNNs on large-scale graphs, where neighbor sampling is used during both model training and inference. Since the structure and attributes of real-world graphs often change dynamically, it is imperative that the inferred vertex representation can accurately reflect these updates.</p>
<h2class="post-title"><ahref="/blog/tech/2023/07/23/Developing-and-Running-Customized-Analytical-Algorithms-for-GraphScope.html">Developing and Running Customized Analytical Algorithms for GraphScope</a></h2>
We are bringing a number of improvements to GraphScope, alongside the GraphScope 0.16.0 release. This release introduces many new features on backend engines and system stability. We completely remove a legacy Graph Interactive Engine (GIE), while officially replacing it with the latest version based on an intermediate representation (IR) layer. The Graph Learning Engine (GLE) now supports real-time sampling on dynamic graphs. In addition, the Jave SDK of Graph Analytics Engine (GAE) can work on MacOS. Meanwhile, we start to release a nightly version every day, and you can try it with <codeclass="language-plaintext highlighter-rouge">pip3 install graphscope --pre</code>.</p>
We are bringing a number of improvements to GraphScope, alongside the GraphScope 0.16.0 release. This release introduces many new features on backend engines and system stability. We completely remove a legacy Graph Interactive Engine (GIE), while officially replacing it with the latest version based on an intermediate representation (IR) layer. The Graph Learning Engine (GLE) now supports real-time sampling on dynamic graphs. In addition, the Jave SDK of Graph Analytics Engine (GAE) can work on MacOS. Meanwhile, we start to release a nightly version every day, and you can try it with <codeclass="language-plaintext highlighter-rouge">pip3 install graphscope --pre</code>.</p>
GraphScope v0.3.0 is released as scheduled. This release includes new features and major updates for frontend APIs for graph manipulation, integration with other systems as well as code optimization for some operators. Another direction we are working on is to ease the deployment of GraphScope with/without Kubernetes.</p>
GraphScope v0.3.0 is released as scheduled. This release includes new features and major updates for frontend APIs for graph manipulation, integration with other systems as well as code optimization for some operators. Another direction we are working on is to ease the deployment of GraphScope with/without Kubernetes.</p>
<h2class="post-title"><ahref="/blog/tech/2021/03/25/a-review-of-programming-models-for-parallel-graph-processing.html">A Review of Programming Models for Parallel Graph Processing</a></h2>
![github-actions[bot]](https://avatars.githubusercontent.com/in/15368?v=4&size=40){kind=avatar}
0 commit comments