GraphNews

Telecom GenAI based Network Operations: The Integration of LLMs, GraphRAG, Reinforcement Learning, and Scoring Models With the increasing complexity of… | 12 comments on LinkedIn

LLM frameworks like LangChain are moving towards a graph-based approach for handling their workflows. This represents the initial steps of a much larger… | 90 comments on LinkedIn

🚀 Join us for a special webinar on March 6th, 8am PT/5pm CET, as we unveil the latest in GNN technology - PyG 2.5! 🎉 Dive deep into the features with a live…

Deep graph models (e.g., graph neural networks and graph transformers) have become important techniques for leveraging knowledge across various types of graphs. Yet, the scaling properties of deep graph models have not been systematically investigated, casting doubt on the feasibility of achieving large graph models through enlarging the model and dataset sizes. In this work, we delve into neural scaling laws on graphs from both model and data perspectives. We first verify the validity of such laws on graphs, establishing formulations to describe the scaling behaviors. For model scaling, we investigate the phenomenon of scaling law collapse and identify overfitting as the potential reason. Moreover, we reveal that the model depth of deep graph models can impact the model scaling behaviors, which differ from observations in other domains such as CV and NLP. For data scaling, we suggest that the number of graphs can not effectively metric the graph data volume in scaling law since the sizes of different graphs are highly irregular. Instead, we reform the data scaling law with the number of edges as the metric to address the irregular graph sizes. We further demonstrate the reformed law offers a unified view of the data scaling behaviors for various fundamental graph tasks including node classification, link prediction, and graph classification. This work provides valuable insights into neural scaling laws on graphs, which can serve as an essential step toward large graph models.

Data provenance is something people love in theory, but never practice... I have just rewatched an excellent appearance by Jaron Lanier from a couple of… | 52 comments on LinkedIn

6 Rules to help you decide whether to use a graph or a table to solve your analytics problem.

Machine learning on graphs, especially using graph neural networks (GNNs), has seen a surge in interest due to the wide availability of graph data across a broad spectrum of disciplines, from life to social and engineering sciences. Despite their practical success, our theoretical understanding of the properties of GNNs remains highly incomplete. Recent theoretical advancements primarily focus on elucidating the coarse-grained expressive power of GNNs, predominantly employing combinatorial techniques. However, these studies do not perfectly align with practice, particularly in understanding the generalization behavior of GNNs when trained with stochastic first-order optimization techniques. In this position paper, we argue that the graph machine learning community needs to shift its attention to developing a more balanced theory of graph machine learning, focusing on a more thorough understanding of the interplay of expressive power, generalization, and optimization.

Complex networks pervade various real-world systems, from the natural environment to human societies. The essence of these networks is in their ability to transition and evolve from microscopic disorder-where network topology and node dynamics intertwine-to a macroscopic order characterized by certain collective behaviors. Over the past two decades, complex network science has significantly enhanced our understanding of the statistical mechanics, structures, and dynamics underlying real-world networks. Despite these advancements, there remain considerable challenges in exploring more realistic systems and enhancing practical applications. The emergence of artificial intelligence (AI) technologies, coupled with the abundance of diverse real-world network data, has heralded a new era in complex network science research. This survey aims to systematically address the potential advantages of AI in overcoming the lingering challenges of complex network research. It endeavors to summarize the pivotal research problems and provide an exhaustive review of the corresponding methodologies and applications. Through this comprehensive survey-the first of its kind on AI for complex networks-we expect to provide valuable insights that will drive further research and advancement in this interdisciplinary field.

Writer CEO May Habib says its semantic graphing approach is an alternative to the chunking process of RAG using vector databases.

Data provenance is something people love in theory, but never practice... I have just rewatched an excellent appearance by Jaron Lanier from a couple of… | 39 comments on LinkedIn

Relational Harmony and a New Hope for Dimensionality ⛓️ In an era where data complexity escalates and the quest for meaningful technology integration… | 30 comments on LinkedIn

Knowledge hypergraphs are garnering a lot of attention – and deservedly so. You can find my two previous posts on knowledge hypergraphs and on more adaptive conceptualizations for hypergraphs as well as Kurt Cagle's focused and more practically minded pieces on Hypergraphs and RDF, on Named Graphs (

Achieving Robust Reasoning by Combining Neural and Symbolic Representations 🌗 Recent breakthroughs in large language models (LLMs) demonstrate impressive…

Knowledge engineering is a discipline that focuses on the creation and maintenance of processes that generate and apply knowledge. Traditionally, knowledge engineering approaches have focused on knowledge expressed in formal languages. The emergence of large language models and their capabilities to effectively work with natural language, in its broadest sense, raises questions about the foundations and practice of knowledge engineering. Here, we outline the potential role of LLMs in knowledge engineering, identifying two central directions: 1) creating hybrid neuro-symbolic knowledge systems; and 2) enabling knowledge engineering in natural language. Additionally, we formulate key open research questions to tackle these directions.

Translating visual networks into graphs using AI is a game changer for accelerating analytics on information that is historically difficult to access.

Rapid Growth in Data Volume and Complexity

Check out this chatbot (https://lnkd.in/gv8Afk57) that I created entirely based on the prolific writings of Mike Dillinger, PhD . This chatbot helps you better…

"It is possible to build KGs with LMs through prompt engineering. But are we boiling the ocean? Can we improve the quality of the generated graph elements by using - dare I say it - SLMs (small language models)"

Over the past 5 years, news about AI has been filled with amazing research – at first focused on graph neural networks (GNNs) and more recently about large language models (LLMs). Understand that business tends to use connected data – networks, graphs – whether you’re untangling supply networks in Manufacturing, working on drug discovery for Pharma, or mitigating fraud in Finance. Starting from supplier agreements, bill of materials, internal process docs, sales contracts, etc., there’s a graph inside nearly every business process, one that is defined by language. This talk addresses how to leverage both natural language and graph technologies together for AI applications in industry. We’ll look at how LLMs get used to build and augment graphs, and conversely how graph data gets used to ground LLMs for generative AI use cases in industry – where a kind of “virtuous cycle” is emerging for feedback loops based on graph data. Our team has been engaged, on the one hand, with enterprise use cases in manufacturing. On the other hand we’ve worked as intermediaries between research teams funded by enterprise and open source projects needed by enterprise – particularly in the open source ecosystem for AI models. Also, there are caveats; this work is not simple. Translating from latest research into production-ready code is especially complex and expensive. Let’s examine caveats which other teams should understand, and look toward practical examples.

In this blog post, we will explore how Knowledge Graphs can benefit from LLMs, and vice versa.

Leveraging Structured Knowledge to Automatically Detect Hallucination in Large Language Models 🔺 🔻 Large Language Models has sparked a revolution in AI’s… | 25 comments on LinkedIn

Sometimes the words “taxonomy” and “ontology” are used interchangeably, and while they are closely related, they are not the same thing. They are both considered kinds of knowledge organization systems to support information and knowledge management.

Semantics friends and colleagues, Announcing the release of a new front-end application development library for RDF. Most of us have heard time and again how…

(This post & prompt technique was inspired by a conversation with Nicky Clarke and a recent post by Kurt Cagle, Rethinking Hypergraphs.) Building prompts for ultra-large context windows requires a complete reimagining of how prompts are created.

OccO: Occupation Ontology. Contribute to Occupation-Ontology/OccO development by creating an account on GitHub.