This article gives a practical introduction to the potential of causal graphs.

Built upon the shoulders of graph theory, the field of complex networks has become a central tool for studying real systems across various fields of research. Represented as graphs, different systems can be studied using the same analysis methods, which allows for their comparison. Here, we challenge the wide-spread idea that graph theory is a universal analysis tool, uniformly applicable to any kind of network data. Instead, we show that many classical graph metrics (including degree, clustering coefficient and geodesic distance) arise from a common hidden propagation model: the discrete cascade. From this perspective, graph metrics are no longer regarded as combinatorial measures of the graph, but as spatio-temporal properties of the network dynamics unfolded at different temporal scales. Once graph theory is seen as a model-based (and not a purely data-driven) analysis tool, we can freely or intentionally replace the discrete cascade by other canonical propagation models and define new network metrics. This opens the opportunity to design, explicitly and transparently, dedicated analyses for different types of real networks by choosing a propagation model that matches their individual constraints. In this way, we take stand that network topology cannot always be abstracted independently from network dynamics, but shall be jointly studied. Which is key for the interpretability of the analyses. The model-based perspective here proposed serves to integrate into a common context both the classical graph analysis and the more recent network metrics defined in the literature which were, directly or indirectly, inspired by propagation phenomena on networks.

Links * Python Examples * JS Examples * YouTube Last week we highlighted LangGraph - a new package (available in both Python and JS) to better enable creation of LLM workflows containing cycles, which are a critical component of most agent runtimes. As a part of the launch, we highlighted two simple runtimes:

⚡ Building language agents as graphs ⚡

Leveraging Graph Algorithms to Enable Responsible AI Reasoning 💡 Large language models have shown immense promise in their ability to generate remarkably…

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Nature Physics - The strengths of connections in networks of neurons are heavy-tailed, with some neurons connected much more strongly than most. Now a simple network model can explain how this...

By Dr. Verónica Espinoza. 2023

Yesterday, I re-shared a huge list of Python visualisation tools - and now, here comes a list of network visualisation tools (these two lists certainly… | 74 comments on LinkedIn

K-medoids is an approach for discovering clusters in data. It is similar to the well-known k-means algorithm.

Graph-based data is ubiquitous in enterprise, across the industry verticals, and increasingly needed for machine learning use cases…

Cracking the code of complex systems. It's not the 'Matrix'. 🕶️ A primer on Networks and Graphs. 🕸️ Networks represent the connections between discrete…

Chaotic mess or meaningful maps? 🌪️🌐 Zooming out reveals the hidden logic beneath. Let's unpack the key structures emerging in high-dimensional…

Understanding data points through the context of their relationships

Graph Theory and Its Implications: A Quantitative Point of View In the technical universe of quantitative finance, graph theory has been gaining prominence… | 15 comments on LinkedIn

Many global corporations are managing multiple supply chains, and they depend on those operations to not only deliver goods on time but to respond to divergent customer and supplier needs. According to a McKinsey study, it’s estimated that significant disruptions to production now occur every 3.7 years on average, adding new urgency to supply chain […]

This is the second chapter in our blog series on GPU-accelerated graph analysis using RAPIDS cuGraph. In the first chapter, we introduced…

We have seen recently a surge in vector databases in this era of generative AI. The idea behind vector databases is to index the data with vectors that relate… | 30 comments on LinkedIn

By Paula Wojcik, Bastian Bunzeck & 1 more. This articles explores character representations in Wikidata across various language editions.

Change & Risk Require New Enterprise Tools As AI systems, bots (both digital and physical), AI leveraged smart digital identities, and IoT devices invade your enterprise, it increases the pace of change and risk. This brief article focuses on why you should be deploying TODA, EMS and graphs in your

News from eight different companies shows analytics, data management, streaming data and AI are increasingly intermingled, all in the cloud.

Dublin, April 24, 2023 (GLOBE NEWSWIRE) -- The "Global Graph Analytics Market: Analysis By Component, By Deployment, By Enterprise Size, By Application,...

Orb is an open-source library developed by Memgraph you can use to visualize graphs by adding just a few lines to your frontend code. This blog post will show you all the cool features Orb offers and how to implement them in your project. Or don't and have slow and appalling graph visualizations - it's your choice. Seriously, use it... it's very easy and fun!

Siren promises the benefits of knowledge graphs, without the hassle of reinventing your existing data management and infrastructure

State of the art in analytics and AI can help address some of the most pressing issues in scientific research. Here is how top scientists are using them to facilitate coronavirus research.

A sui generis, multi-model open source database, designed from the ground up to be distributed. ArangoDB keeps up with the times and uses graph, and machine learning, as the entry points for its offering.

New players, new features, platforms, and ticking boxes. Let's talk graph with Neo4j.

What are the options for querying graphs, and how do we go from that to the equivalent of a virtual machine for databases?