Twitter Graph Visualization

Hacker News

My startup YesGraph is testing a new way to visualize your Twitter graph. This is a bit of a side project for us, but the result is so cool we decided to open it up to see what others think. If you want to see your graph, go here and connect your Twitter account. We request read-only access, so your account is safe. https://www.yesgraph.com/twinmaps/ Creating high quality visualizations is really hard, and there is so much for us to do. We’d love to get your feedback after you check out your graph. Take our 60s survey here: http://goo.gl/forms/W4PwSfqbySXBTNqg1 The shape and structure of everyone’s graph is different too, which is another interesting layer. We should probably build better clustering and comparison tools for seeing how your network intersects with a friend. The next step could be with more utility - like being able to follow people you find in the graph. We should also do better clustering by assigning a label to a group based upon shared interests. Clustering algorithms are hard because there is no ground truth. Here is my personal graph. You can see a Dropbox blue peninsula in the top left. https://www.yesgraph.com/visualize/twitter/99e35955da510815adeedc61488af57b

Visit product View on Hacker NewsJul 21, 2016

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new, visual, open · Missing: mac, agents, macos

48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, visualize, way · Missing: mobile apps, ios, entrepreneurs

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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