Wa
Watch Your Step: Learning Node Embeddings via Graph Attention, NIPS'18
Hacker NewsWatch Your Step: Learning Node Embeddings via Graph Attention, NIPS'18
Share cardActual performance
2points
1comments
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →
BetaListSuited for BetaList early-adopters · Missing: web3, chat, crypto59%59% predicted probability of success on BetaList, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos26%26% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting18%18% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Incorrect prediction on native model
Similar products
Le
Learning Structural Node Embeddings via Diffusion Wavelets (KDD 18)58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Learning Structural Node Embeddings via Diffusion Wavelets (KDD 18)
Hacker News2Gr
Graph Classification Using Structural Attention, KDD 2018 (PyTorch)27%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Graph Classification Using Structural Attention, KDD 2018 (PyTorch)
Hacker News2Gr
Graph classification using structural attention (PyTorch)25%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Graph classification using structural attention (PyTorch)
Hacker News2Gr
Graphlearn-for-PyTorch, distributed graph learning on PyTorch64%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Graphlearn-for-PyTorch, distributed graph learning on PyTorch
Hacker News9Fenerbahçe LyonMaciCanliIzle5%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
[[Canlı-Yayın!]Fenerbahçe - Lyon Maci Canli Izle 18 Ağustos
Indie HackersMy
My first Canvas project (node graph)54%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
My first Canvas project (node graph)
Hacker News8SV
SVG Weave. A node graph editor that animates SVGs with AI44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
SVG Weave. A node graph editor that animates SVGs with AI
Hacker News4no
node-scrunch42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
node-scrunch
Hacker News1Mo
MongoSync for Backbone/Node42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
MongoSync for Backbone/Node
Hacker News2No
Node Knockout 2013 entries28%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Node Knockout 2013 entries
Hacker News1