Graphs. Such a simple idea. Map a problem onto a graph then solve it by searching over the graph or by exploring the structure of the graph. What could be easier? Turns out, however, that working with graphs is a vast and complex field. Keeping up is challenging. To help keep up, you just need an editor who knows most people working with graphs, and have that editor gather nearly 70 researchers to summarize their work with graphs. The result is the book Massive Graph Analytics. — Timothy G. Mattson, Senior Principal Engineer, Intel Corp Expertise in massive-scale graph analytics is key for solving real-world grand challenges from healthcare to sustainability to detecting insider threats, cyber defense, and more. This book provides a comprehensive introduction to massive graph analytics, featuring contributions from thought leaders across academia, industry, and government. Massive Graph Analytics will be beneficial to students, researchers, and practitioners in academia, national

Cambridge Core - Communications and Signal Processing - Introduction to Graph Signal Processing

TigerGraph unveiled a new tool that provides users with a dedicated, open source environment for building machine learning models with graph databases.

[🥳new video🧠] You thought that the curvature of space and Ricci flow (famously used by Grisha Perelman to solve a 1.000.000$ millennium problem (Poincaré...

Information extraction, drug discovery, and software analysis are just a few applications of this versatile tool.

Knowledge graphs, the technology powering Google, Facebook and Apple, is now unlocking value across the financial sector. Knowledge graphs are transforming critical capabilities and enterprises are increasingly looking to the technology to enhance their tax strategy, perform compliance and improve customer service.

Nature Machine Intelligence - The number of graph neural network papers in this journal has grown as the field matures. We take a closer look at some of the scientific applications.

In the last couple of months, I’ve been noticing a gradual shift in the kind of articles that we receive at Data Science Central. We still get a fair amount of data science content, but increasingly (and admittedly with a bit of encouragement) we’re seeing more articles centered around graphs and semantics. I don’t believe… Read More »DSC Weekly Digest 22 February 2022: Graphology

You’ve probably seen web editors based on the idea of blocks. I’m typing this in WordPress, which has a little + button that brings up a long list of potential blocks that you can inser…

Can Machine Learning Do Symbolic Manipulation?  I spent some time over the holidays engaged in a fascinating online conversation. The gist of it was a variation of an argument that has been going on in the realm of artificial intelligence from the time of Minsky and Seymour Papert: Whether it is possible for neural networks to… Read More »DSC Weekly Digest 04 Jan 2022: Can Machine Learning Do Symbolic Manipulation?

New Video! Zhaocheng Zhu explains his paper NeurIPS'21 paper "Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction...

Anti-Money Laundering Alert Optimization Using Machine Learning with Graphs https://t.co/e7fQg57Mrt pic.twitter.com/8nZ7QzeUD5— Aaron Bradley (@aaranged) December 15, 2021

What if we model a graph as a set of subgraphs instead of a set of interconnected nodes? Hint: expressive power + equivariance! 🧵Joint work by a super team: @beabevi_ * @dereklim_lzh * @balasrini32 @ChenCaiUCSD @gblearning42 @mmbronstein @HaggaiMaronhttps://t.co/6rW4e47RhN pic.twitter.com/Ajl8L5xdDc— Fabrizio Frasca (@ffabffrasca) December 15, 2021

MedGraph: An experimental semantic information retrieval method using knowledge graph embedding for the biomedical citations indexed in PubMed https://t.co/B3tmoVilDt pic.twitter.com/DvEVoU53ec— Aaron Bradley (@aaranged) December 14, 2021

Graph-based hierarchical record clustering for unsupervised entity resolution https://t.co/I0saMXcQFL pic.twitter.com/eB7Cphflp3— Aaron Bradley (@aaranged) December 14, 2021

Embedding knowledge on ontology into the corpus by topic to improve the performance of deep learning methods in sentiment analysis - Scientific Reports https://t.co/W0lKPRCh84 #DL #AI #ML #DeepLearning #ArtificialIntelligence #MachineLearning #ComputerVision #AutonomousVehicles— Deep_In_Depth (@Deep_In_Depth) December 11, 2021

UFO: Unified Foundational Ontology / Giancarlo Guizzardi et al. https://t.co/GuTFxstIqV pic.twitter.com/9BJG1GUdIt— Aaron Bradley (@aaranged) December 16, 2021

Excited to share a Colab tutorial on graph neural networks with the JAX+Jraph stack, which we use in-house at DeepMind! Developed by Lisa Wang and Nikola... 12 comments on LinkedIn

Last week Google added Graph Neural Network support to TensorFlow. This comes in the wake of PyTorch Geometric getting a lot of support from Meta (formerly...

G.V() is an all-in-one Gremlin IDE to write, test and analyze results for your TinkerPop-enabled graph database - in open beta now and looking for feedback: https://t.co/0FLK7kC9K9 #graphdb pic.twitter.com/7U8PeKFsm9— TinkerPop (@apachetinkerpop) November 22, 2021

How to tune the hyperparameters of FastRP to your specific problem

Papers With Code highlights trending Machine Learning research and the code to implement it.

Classical event logs have the fundamental shortcoming of describing process behavior only in isolated process executions from the viewpoint of a single case entity. Most real-life processes involve…

Excited to share our collaboration with @GoogleAI: SMORE is a scalable knowledge graph completion and multi-hop reasoning system that scales to hundreds of millions of entities and relations. @ren_hongyu, @hanjundai, et al.https://t.co/O2xM9iYMjihttps://t.co/BImVmbZAFi pic.twitter.com/H52aFOejkv— Jure Leskovec (@jure) November 1, 2021

This week’s publication of the Pandora Papers--which the International Consortium of International Journalists based on a trove of private data leaked

While many of the programming libraries encapsulate the inner working details of graph and other algorithms, as a data scientist it helps a lot having a reason…

Distribution Knowledge Embedding for Graph Pooling https://t.co/YsQBXwbFtZ pic.twitter.com/ZXbpOKDrLC— Aaron Bradley (@aaranged) September 30, 2021

Super nice talk by @matej_zecevic on #Neuro-#Causality and our integration of graph neural networks and structural causal models. 🎞️👉 https://t.co/S2XNuOqZ61 🙏 to @JackccLu for inviting Matej! pic.twitter.com/jamZ20WoGt— Kristian Kersting (@kerstingAIML) September 28, 2021