# ElasticGraph hackathon ideas?

**URL:** <https://discuss.elastic.co/t/elasticgraph-hackathon-ideas/58569>\
**Category:** Elasticsearch\
**Created:** [August 22, 2016, 11:21am UTC](https://discuss.elastic.co/t/elasticgraph-hackathon-ideas/58569 "2016-08-22T11:21:07Z")\
**Posts on this page:** 3\
**Page:** 1

<div class="post-metadata">

**Author:** ![Pe\_Ter](https://avatars.discourse-cdn.com/v4/letter/p/49beb7/32.png) [@Pe\_Ter](https://discuss.elastic.co/u/Pe_Ter)\
**Post date:** [August 22, 2016, 11:21am UTC](https://discuss.elastic.co/t/elasticgraph-hackathon-ideas/58569/1 "2016-08-22T11:21:07Z")

</div>

I've been informed I'm going to organize an ElasticGraph hackathon in a couple of days for a mixed junior/senior frontend/backend dev team of 8 people.

Who's worked on some nifty ElasticGraph projects to kickstart some ideas?

---

<div class="post-metadata">

**Author:** ![Mark\_Harwood](https://sea2.discourse-cdn.com/elastic/user_avatar/discuss.elastic.co/mark_harwood/32/10538_2.png) [@Mark\_Harwood](https://discuss.elastic.co/u/Mark_Harwood)\
**Post date:** [August 22, 2016, 2:06pm UTC](https://discuss.elastic.co/t/elasticgraph-hackathon-ideas/58569/2 "2016-08-22T14:06:18Z")

</div>

Some ideas:

### StackOverflow graph.

Get a dump of the StackOverflow data. Create one doc per question+responses. Index tags and user handles. Explore graph of strongly-related tags and the users who are experts in the area. Provide drill-down into docs to show timelines etc. See [https://www.youtube.com/watch?v=1QwmJ\_FCMqU&feature=youtu.be](https://www.youtube.com/watch?v=1QwmJ_FCMqU&feature=youtu.be)

### Review fraud

Download Amazon review data. [https://snap.stanford.edu/data/web-Amazon.html](https://snap.stanford.edu/data/web-Amazon.html)  
Examine relationships between reviewers, product IDs, timeOfPost, language etc

### Panama papers

This blog post has some tips and scripts: [https://www.elastic.co/blog/using-elastic-graph-and-kibana-to-analyze-panama-papers](https://www.elastic.co/blog/using-elastic-graph-and-kibana-to-analyze-panama-papers)

### Recommendation engine

Take your userID/click data and build a doc per-user which summarises the products they have bought. Then explore "people who bought X also tend to by Y" type connections using Graph. LastFM data is fun to play with: [http://mtg.upf.edu/node/1671](http://mtg.upf.edu/node/1671). Do the same for bad actors too by exploring behaviours of "people who did bad activity X also tended to do activity Y".

Would be interested to hear how you get on 🙂

---

<div class="post-metadata">

**Author:** ![system](https://us1.discourse-cdn.com/elastic/original/3X/1/a/1ac57faf039f6b580b3f104ef42a2a89e41014de.png) [@system](https://discuss.elastic.co/u/system)\
**Post date:** [July 5, 2017, 10:26pm UTC](https://discuss.elastic.co/t/elasticgraph-hackathon-ideas/58569/3 "2017-07-05T22:26:10Z")

</div>