Articles

AirflowLogo — Neo4j ETL using Airflow
January 16, 2023/by Orbifold

Memgraph — Memgraph
November 29, 2022/by Orbifold

NetworkXLogo-1 — NetworkX: an overview
November 21, 2022/by Orbifold

DRKG Visualization — Drug Repurposing using TigerGraph and Graph Machine Learning
February 12, 2022/by Orbifold

Syncfusion — Syncfusion diagramming
December 14, 2021/by Orbifold

Entity Resolution — Entity resolution can be easy
November 5, 2021/by Orbifold

TensorFlow Learning — Graph Machine Learning using TensorFlow
February 2, 2020/by Orbifold

GraphSage — Using GraphSage for node predictions
November 3, 2019/by Orbifold

Link prediction — Graph Link Prediction using GraphSAGE
November 1, 2019/by Orbifold

Graph Laplacian — Using Laplacians for graph learning
October 30, 2019/by Orbifold

Communities — Community detection using NetworkX
October 7, 2019/by Orbifold

Graph Database — What is a graph database?
October 1, 2019/by Orbifold

Graph Attention — Graph attention networks
September 29, 2019/by Orbifold

Cora data set — The Cora dataset
September 29, 2019/by Orbifold

Node2Vec — Node2Vec with weighted random walks
September 26, 2019/by Orbifold

Node2Vec — Node2Vec embedding
September 26, 2019/by Orbifold

Spark GraphFrame — Spark GraphFrame Basics
August 16, 2019/by Orbifold

Towards persistent homology — What is persistent homology?
November 2, 2018/by Orbifold

jena — Apache Jena disaster
October 31, 2018/by Orbifold

Vaticle TypeDB — TypeDB by Vaticle
October 31, 2018/by Orbifold

Visualize big networks within seconds with Cosmograph. Halfway art and science, uses your GPU to speed up force-directed layout. With timeline and graph analytic filtering. And yes, open source. https://t.co/NfTrbKSsgj #Graphs #DataViz https://t.co/Y2jdU6Zx9S

Codon by @exaloop , a high-performance Python compiler that compiles to native machine code without any runtime overhead. Speedups are on the order of 10-100xor more, on a single thread. Codon's performance is typically on par with that of C/C++. https://t.co/g00nZsyR64 #python https://t.co/KbeSpO8IYw

Trillion edges benchmark: new world record beyond 100TB by @TigerGraphDB featuring AMD based Amazon EC2 instances. https://t.co/65sp9zkwPY #GraphDatabase https://t.co/d9BVCsOzem

Watch this space for an updated PowerBI widget based on @yworks. In 5 years since v1 it's easier to create and more powerful (Power Apps, Python & R integration...). Developing graph visualization in @MSPowerBI is so much easier and more fun than Tableau. https://t.co/XOQvZc9FbH https://t.co/iQ0I6n7EOi

The graph of primes exhibits some striking patterns and anomalies, both topologically and in its centrality measures.Can #GraphMachineLearning help here? Based on research with Soumya Jyoti Banerjee https://t.co/MyHBXCcfk6 #graphs https://t.co/RKpVtkGGgs

It takes now less than 100 lines of code to talk to your Neo4j graph (see attached gist). This works with any database really. I used a large biomed graph but this is also arbitrary. Uses GPT3, not ChatGPT. https://t.co/vBeGneS0FO @neo4j #graphs #NLU https://t.co/WTuh8qt81r

JanusGraph, a Gremlin-first #GraphDatabase has a Cypher-for-Gremlin adapter enabling property #Graphs. @TSawyerSoftware has explicit visualization/analytics support. Also check the @Linkurious Ogma dataviz https://t.co/qWF36TbbSp https://t.co/qJYhhnsk93 https://t.co/cwTXhpl8u0 https://t.co/GSkeANqPCC

Here's the real reason why graphs are everywhere. Category Theory is the maths of mathematics and it effectively embodies the idea that #graphs can describe anything and everything. The Wolfram stack excels in this sorta stuff. @WolframResearch https://t.co/UG8rQZlQu4 https://t.co/gLpZeDlIxA