Parallel Multi-Scale Network Embedding with Walklets
A lightweight implementation of Walklets from "Don't Walk Skip! Online Learning of Multi-scale Network Embeddings" (ASONAM 2017). - benedekrozemberczki/walklets
Parallel Multi-Scale Network Embedding with Walklets launched on May 5, 2019 with 5 votes, #1,258 of 3,072 launches that month and more than 50% of that year's launches. The site was still online in September 2026, 7 years on.
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We show the potential of modern, embedded graph databases in the browser by demonstrating a fully in-browser chatbot that can perform Graph RAG using Kuzu (the graph database we're building) and WebLLM, a popular in-browser inference engine for LLMs. The post retrieves from the graph via a Text-to-Cypher pipeline that translates a user question into a Cypher query, and the LLM uses the retrieved results to synthesize a response. As LLMs get better, and WebGPU and Wasm64 become more widely adopted, we expect to be able to do more and more in the browser in combination with LLMs, so a lot of…
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Many important real-world datasets come in the form of graphs or networks: social networks, knowledge graphs, protein-interaction networks, the World Wide Web, etc. (just to name a few). Yet, until recently, very little attention has been devoted to the generalization of neural...
A Higher-Order Graph Convolutional Layer, NeurIPS 2018 (PyTorch)2019 · github.com · ▲9An implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019). - benedekrozemberczki/MixHop-and-N-GCN
MixHop: Higher-Order Graph Convolutional Architectures, ICML 20192019 · github.com · ▲5An implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019). - benedekrozemberczki/MixHop-and-N-GCN
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