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Graph Neural Networks (GNNs) have emerged as a powerful technique for learning on relational data. Owing to the relatively limited number of message passing steps they perform -- and hence a smaller receptive field -- there has been significant interest in improving their expressivity by incorporating structural aspects of the underlying graph. In this paper, we explore the use of affinity measures as features in graph neural networks, in particular measures arising from random walks, including effective resistance, hitting and commute times. We propose message passing networks based on these features and evaluate their performance on a variety of node and graph property prediction tasks. Our architecture has lower computational complexity, while our features are invariant to the permutations of the underlying graph. The measures we compute allow the network to exploit the connectivity properties of the graph, thereby allowing us to outperform relevant benchmarks for a wide variety of tasks, often with significantly fewer message passing steps. On one of the largest publicly available graph regression datasets, OGB-LSC-PCQM4Mv1, we obtain the best known single-model validation MAE at the time of writing.

It was an honor to present the initial results of the Chat with the Data benchmark last week at the The Alan Turing Institute Knowledge Graph meetup (link to… | 11 comments on LinkedIn

Neural Graphical Models (NGMs) provide a solution to the challenges posed by traditional graphical models, offering greater flexibility, broader applicability, and improved performance in various domains. Learn more:

Innovative and open-source visualization application that transforms various data formats, such as JSON, YAML, XML, CSV and more, into interactive graphs.

A benchmark study comparing the performance of KùzuDB vs. Neo4j on an artificial social network dataset

Tired of updating your #scraping templates or feeding full webpages to LLMs? Use #Cypher instead! I was in the same boat just 3 months ago when I was working… | 16 comments on LinkedIn

Can LLMs replace GNNs as the foundational model in graph machine learning? Over the past decade, the landscape of machine learning with graphs has seen a… | 19 comments on LinkedIn

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Happy to announce PyGraft, a configurable #Python tool to generate both synthetic #schemas and #knowledgeGraphs easily, supporting several RDFS and OWL constructs. Paper: https://t.co/p1Ei3PIhVz Code: https://t.co/ID6gU3elqK (also available on PyPI) @nicolas_hubr @mdaquin

On August 14, 2023, the paper Natural Language is All a Graph Needs by Ruosong Ye, Caiqi Zhang, Runhui Wang, Shuyuan Xu and Yongfeng Zhang hit the arXiv streets and made quite a bang! The paper outlines a model called InstructGLM that adds further evidence that the future of graph representation lea

Less than five years after its initial release, Redis has announced that RedisGraph will be discontinued. Why?

Since the recent prosperity of Large Language Models (LLMs), there have been interleaved discussions regarding how to reduce hallucinations from LLM responses, how to increase the factuality of LLMs, and whether Knowledge Graphs (KGs), which store the world knowledge in a symbolic form, will be replaced with LLMs. In this paper, we try to answer these questions from a new angle: How knowledgeable are LLMs? To answer this question, we constructed Head-to-Tail, a benchmark that consists of 18K question-answer (QA) pairs regarding head, torso, and tail facts in terms of popularity. We designed an automated evaluation method and a set of metrics that closely approximate the knowledge an LLM confidently internalizes. Through a comprehensive evaluation of 14 publicly available LLMs, we show that existing LLMs are still far from being perfect in terms of their grasp of factual knowledge, especially for facts of torso-to-tail entities.

Mentions of new concepts appear regularly in texts and require automated approaches to harvest and place them into Knowledge Bases (KB), e.g., ontologies and taxonomies. Existing datasets suffer from three issues, (i) mostly assuming that a new concept is pre-discovered and cannot support out-of-KB mention discovery; (ii) only using the concept label as the input along with the KB and thus lacking the contexts of a concept label; and (iii) mostly focusing on concept placement w.r.t a taxonomy of atomic concepts, instead of complex concepts, i.e., with logical operators. To address these issues, we propose a new benchmark, adapting MedMentions dataset (PubMed abstracts) with SNOMED CT versions in 2014 and 2017 under the Diseases sub-category and the broader categories of Clinical finding, Procedure, and Pharmaceutical / biologic product. We provide usage on the evaluation with the dataset for out-of-KB mention discovery and concept placement, adapting recent Large Language Model based methods.

The Memory Game: Investigating the Accuracy of AI Models in Storing and Recalling Facts … 🧠 ... Comparing LLMs and Knowledge Graph on Factual Knowledge I’m… | 18 comments on LinkedIn

No, this is not the chat a father has with Lucius L. Malfoy when he asks to walk your daughter along a path to the prom.

See how to integrate a language language model with TigerGraph to empower question answering with your connected data.

We propose the LLMs4OL approach, which utilizes Large Language Models (LLMs) for Ontology Learning (OL). LLMs have shown significant advancements in natural language processing, demonstrating their ability to capture complex language patterns in different knowledge domains. Our LLMs4OL paradigm investigates the following hypothesis: \textit{Can LLMs effectively apply their language pattern capturing capability to OL, which involves automatically extracting and structuring knowledge from natural language text?} To test this hypothesis, we conduct a comprehensive evaluation using the zero-shot prompting method. We evaluate nine different LLM model families for three main OL tasks: term typing, taxonomy discovery, and extraction of non-taxonomic relations. Additionally, the evaluations encompass diverse genres of ontological knowledge, including lexicosemantic knowledge in WordNet, geographical knowledge in GeoNames, and medical knowledge in UMLS.

“There is a lot of research on establishing mappings between different ontologies, but hardly any work on how to leverage such mappings in a query federation engine. With @Sijin_Cheng and @ferradest, we have embarked on changing that. Paper at @CoopIS2023 https://t.co/vF1emf9R6Z”

Ah, Gartner's Hype Cycle. It's always fun to see what's on the roller coaster. I think the position of Knowledge Graphs is about right - the KM community is… | 10 comments on LinkedIn

The age of Big Data inevitably brought computationally intensive problems to the enterprise. Central to today’s efficient business operations are the activities of data capturing and storage, search, sharing, and data analytics.

Graph Database Market is anticipated to reach USD XX.X MN by 2030, this market report provides the growth, trends, key players & forecast of the market based on in-depth research by industry experts. The global market size, share along with drivers and restraints are covered in the graph database market report

In the realm of Artificial Intelligence (AI), ontology engineering plays a pivotal role in structuring knowledge and facilitating semantic…

In the case of interpersonal knowledge graphs, everyone in the community can contribute to the disambiguation and enrichment of the community’s online presence, and at the same time help with findability, accessibility, interoperability and reuse (the FAIR principles). And that applies not only to someone else finding your path to research discovery, but you being able to retrace your own steps whenever you need to.

Current anti-money laundering (AML) systems, predominantly rule-based, exhibit notable shortcomings in efficiently and precisely detecting instances of money laundering. As a result, there has been a recent surge toward exploring alternative approaches, particularly those utilizing machine learning. Since criminals often collaborate in their money laundering endeavors, accounting for diverse types of customer relations and links becomes crucial. In line with this, the present paper introduces a graph neural network (GNN) approach to identify money laundering activities within a large heterogeneous network constructed from real-world bank transactions and business role data belonging to DNB, Norway's largest bank. Specifically, we extend the homogeneous GNN method known as the Message Passing Neural Network (MPNN) to operate effectively on a heterogeneous graph. As part of this procedure, we propose a novel method for aggregating messages across different edges of the graph. Our findings highlight the importance of using an appropriate GNN architecture when combining information in heterogeneous graphs. The performance results of our model demonstrate great potential in enhancing the quality of electronic surveillance systems employed by banks to detect instances of money laundering. To the best of our knowledge, this is the first published work applying GNN on a large real-world heterogeneous network for anti-money laundering purposes.

What Mark said. Three things have been perennially true ever since we started Neo4j and kicked off the graph database category: 1) Every year or so there's a…

Redis Inc. is phasing out RedisGraph. This blog post explains the motivation behind this decision and the implications for existing customers and community members.

Knowledge graphs – a graphic representation of entities in a linked network − have the potential to solve many data integration challenges that pose a significant barrier to the use of AI.