GraphNews

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*GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training* by Tianle Cai, Keyulu Xu, Di He, Tie-Yan Liu, Liwei Wang et al. What ...

We present Arianna+, a framework to design networks of ontologies for representing knowledge enabling smart homes to perform human activity recognition online. In the network, nodes are ontologies...

This is what happens when you bulk add keywords without paying attention to the details. If you’re searching for a specific result in a database or on... 43 comments on LinkedIn

Announcing preview availability of https://t.co/587lbNTOjD - Schema Markup Validator https://t.co/QvvuEGHtqd Guest post from @rrlevering #schemasmv https://t.co/AVi6ZJoC77

Graph clustering aiming to obtain a partition of data using the graph information, has received considerable attention in recent years. However, noisy edges and nodes in the graph may make the...

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This research talks about using Random Walk inspired Anonymous Walks as graph units to derive feature-based and data-driven Graph Embeddings in an unsupervised...

This is a guest post by Russell Waterson, Knowledge Graph Engineer at Data Lens Ltd. Customers use knowledge graphs to consolidate and integrate information assets and make them more readily available. Building knowledge graphs by getting data from disparate existing data sources can be expensive, time-consuming, and complex. Project planning, project management, engineering, maintenance and […]

A new benchmarking study released at this week’s The Knowledge Graph Conference illustrates how large corporates are starting to embrace…

Over the recent years, Graph Neural Networks have become increasingly popular in network analytic and beyond. With that, their architecture noticeable diverges from the classical multi-layered...

Discover the top 5 graph analytics takeaways from Gartner's Data & Analytics Summit, from creating context to improving communication.

The performance of applications, such as personal assistants, search engines, and question-answering systems, rely on high-quality knowledge bases, a.k.a. Knowledge Graphs (KGs). To ensure their...

With the Open Research Knowledge Graph (https://orkg.org) developed by TIB – Leibniz-Informationszentrum Technik und Naturwissenschaften and L3S Research...

Stanford / Winter 2021

With our new tool Stardog Explorer, it’s easier than ever to search, browse, and understand connected data. No querying, no code — just an intuitive interface that anyone can use.

This series summarizes a comprehensive taxonomy for machine learning on graphs and reports details on GraphEDM (Chami et. al), a new framework for unifying different learning approaches Graphs are…

Wireframing/interface design and user experience flow definition activities are a powerful tool to visualize knowledge graph applications.

Hierarchical Graph Neural Networks - Over the recent years, Graph Neural Networks have become increasingly popular in network analytic and beyond. With...

Algorithms have been fundamental to recent global technological advances and, in particular, they have been the cornerstone of technical advances in one field rapidly being applied to another. We...

I find myself searching manually for @FullFact analysis of these quotes. How should this work? Driving fact-checking agenda solely via critical articles is tricky vs need for perspective on fairness of this selection/treatment. Original claims & this article need scrutiny-

Directional Graph Networks Beaini et al.: https://lnkd.in/dKe_q3K #GraphNeuralNetworks #DirectionalGraphNetworks #ArtificialIntelligence...

Your guide to the KG-related research in ML, May edition

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Your guide to the KG-related research in ML, May edition

This paper, originally written in Hungarian by Dénes Kőnig in 1931, proves that in a bipartite graph, the minimum vertex cover and the maximum matching have the same size. This statement...

Guided sampling for large graphs Taking a small break from social networks. In this paper, they suggest an efficient and simple graph sampling method...

Introduction to basic concepts and applications of graph mining