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An Intro to Building Knowledge Graphs
An Intro to Building Knowledge Graphs
Editor’s note: Sumit Pal is a speaker for ODSC East this April 23-25. Be sure to check out his talk, “Building Knowledge Graphs,” there! Graphs and Knowledge Graphs (KGs) are all around us. We use them every day without realizing it. GPS leverages graph data structures and databases to plot...
·opendatascience.com·
An Intro to Building Knowledge Graphs
Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks
Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks
In the ever-evolving digital audio landscape, Spotify, well-known for its music and talk content, has recently introduced audiobooks to its vast user base. While promising, this move presents significant challenges for personalized recommendations. Unlike music and podcasts, audiobooks, initially available for a fee, cannot be easily skimmed before purchase, posing higher stakes for the relevance of recommendations. Furthermore, introducing a new content type into an existing platform confronts extreme data sparsity, as most users are unfamiliar with this new content type. Lastly, recommending content to millions of users requires the model to react fast and be scalable. To address these challenges, we leverage podcast and music user preferences and introduce 2T-HGNN, a scalable recommendation system comprising Heterogeneous Graph Neural Networks (HGNNs) and a Two Tower (2T) model. This novel approach uncovers nuanced item relationships while ensuring low latency and complexity. We decouple users from the HGNN graph and propose an innovative multi-link neighbor sampler. These choices, together with the 2T component, significantly reduce the complexity of the HGNN model. Empirical evaluations involving millions of users show significant improvement in the quality of personalized recommendations, resulting in a +46% increase in new audiobooks start rate and a +23% boost in streaming rates. Intriguingly, our model's impact extends beyond audiobooks, benefiting established products like podcasts.
·arxiv.org·
Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks
Demystifying Embedding Spaces using Large Language Models
Demystifying Embedding Spaces using Large Language Models
Embeddings are telling a story that we haven't been listening to. Embeddings are everywhere: they power search, recommendations, RAG, and much more. They are…
·linkedin.com·
Demystifying Embedding Spaces using Large Language Models
Knowledge, Data and LLMs
Knowledge, Data and LLMs
Today is a pretty special day. In some sense, this is the day I’ve been waiting for all my life. The day that we figure out how to make…
·medium.com·
Knowledge, Data and LLMs
A word of caution from Netflix against blindly using cosine similarity as a measure of semantic similarity
A word of caution from Netflix against blindly using cosine similarity as a measure of semantic similarity
A word of caution from Netflix against blindly using cosine similarity as a measure of semantic similarity: https://lnkd.in/gX3tR4YK They study linear matrix… | 12 comments on LinkedIn
A word of caution from Netflix against blindly using cosine similarity as a measure of semantic similarity
·linkedin.com·
A word of caution from Netflix against blindly using cosine similarity as a measure of semantic similarity
Graph neural networks
Graph neural networks
Nature Reviews Methods Primers - Graph neural networks are a class of deep learning methods that can model physical systems, generate new molecules and identify drug candidates. This Primer...
·nature.com·
Graph neural networks
A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges
A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges
Graph-structured data exhibits universality and widespread applicability across diverse domains, such as social network analysis, biochemistry, financial fraud detection, and network security. Significant strides have been made in leveraging Graph Neural Networks (GNNs) to achieve remarkable success in these areas. However, in real-world scenarios, the training environment for models is often far from ideal, leading to substantial performance degradation of GNN models due to various unfavorable factors, including imbalance in data distribution, the presence of noise in erroneous data, privacy protection of sensitive information, and generalization capability for out-of-distribution (OOD) scenarios. To tackle these issues, substantial efforts have been devoted to improving the performance of GNN models in practical real-world scenarios, as well as enhancing their reliability and robustness. In this paper, we present a comprehensive survey that systematically reviews existing GNN models, focusing on solutions to the four mentioned real-world challenges including imbalance, noise, privacy, and OOD in practical scenarios that many existing reviews have not considered. Specifically, we first highlight the four key challenges faced by existing GNNs, paving the way for our exploration of real-world GNN models. Subsequently, we provide detailed discussions on these four aspects, dissecting how these solutions contribute to enhancing the reliability and robustness of GNN models. Last but not least, we outline promising directions and offer future perspectives in the field.
·arxiv.org·
A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges
Tony Seale Knowledge Graph Chatbot
Tony Seale Knowledge Graph Chatbot
I am thrilled to introduce a new AI Study Guide (https://lnkd.in/g4rPZVHW) dedicated to Tony Seale, another of my favorite authors, thought leaders, and…
Knowledge Graph
·linkedin.com·
Tony Seale Knowledge Graph Chatbot
PyGraft: Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertips
PyGraft: Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertips
Knowledge graphs (KGs) have emerged as a prominent data representation and management paradigm. Being usually underpinned by a schema (e.g., an ontology), KGs capture not only factual information but also contextual knowledge. In some tasks, a few KGs established themselves as standard benchmarks. However, recent works outline that relying on a limited collection of datasets is not sufficient to assess the generalization capability of an approach. In some data-sensitive fields such as education or medicine, access to public datasets is even more limited. To remedy the aforementioned issues, we release PyGraft, a Python-based tool that generates highly customized, domain-agnostic schemas and KGs. The synthesized schemas encompass various RDFS and OWL constructs, while the synthesized KGs emulate the characteristics and scale of real-world KGs. Logical consistency of the generated resources is ultimately ensured by running a description logic (DL) reasoner. By providing a way of generating both a schema and KG in a single pipeline, PyGraft's aim is to empower the generation of a more diverse array of KGs for benchmarking novel approaches in areas such as graph-based machine learning (ML), or more generally KG processing. In graph-based ML in particular, this should foster a more holistic evaluation of model performance and generalization capability, thereby going beyond the limited collection of available benchmarks. PyGraft is available at: https://github.com/nicolas-hbt/pygraft.
·arxiv.org·
PyGraft: Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertips
KGLM-Loop: A Bi-Directional Data Flywheel for Knowledge Graph Refinement and Hallucination Detection in Large Language Models
KGLM-Loop: A Bi-Directional Data Flywheel for Knowledge Graph Refinement and Hallucination Detection in Large Language Models
KGLM-Loop: A Bi-Directional Data Flywheel for Knowledge Graph Refinement and Hallucination Detection in Large Language Models ☀ 🌑 In the pursuit of…
KGLM-Loop: A Bi-Directional Data Flywheel for Knowledge Graph Refinement and Hallucination Detection in Large Language Models
·linkedin.com·
KGLM-Loop: A Bi-Directional Data Flywheel for Knowledge Graph Refinement and Hallucination Detection in Large Language Models
Decoding the Semantic Layer
Decoding the Semantic Layer
We've been hearing the term "Semantic layer" without truly understanding the semantics of it. So, here is episode 11 of #DnABytes and today's topic is:…
Decoding the Semantic Layer
·linkedin.com·
Decoding the Semantic Layer
Telecom GenAI based Network Operations: The Integration of LLMs, GraphRAG, Reinforcement Learning, and Scoring Models
Telecom GenAI based Network Operations: The Integration of LLMs, GraphRAG, Reinforcement Learning, and Scoring Models
Telecom GenAI based Network Operations: The Integration of LLMs, GraphRAG, Reinforcement Learning, and Scoring Models With the increasing complexity of… | 12 comments on LinkedIn
Telecom GenAI based Network Operations: The Integration of LLMs, GraphRAG, Reinforcement Learning, and Scoring Models
·linkedin.com·
Telecom GenAI based Network Operations: The Integration of LLMs, GraphRAG, Reinforcement Learning, and Scoring Models
Why do LangChain and Autogen use graphs? Here are the top reasons
Why do LangChain and Autogen use graphs? Here are the top reasons
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
Why do LangChain and Autogen use graphs? Here are the top reasons
·linkedin.com·
Why do LangChain and Autogen use graphs? Here are the top reasons
The latest in GNN technology - PyG 2.5
The latest in GNN technology - PyG 2.5
🚀 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…
the latest in GNN technology - PyG 2.5
·linkedin.com·
The latest in GNN technology - PyG 2.5
Neural Scaling Laws on Graphs
Neural Scaling Laws on Graphs
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.
·arxiv.org·
Neural Scaling Laws on Graphs