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Examining what is possible for StackOverflow with a graph database

Analyzing StackOverflow with Neo4j and Clojure

Joining multiple disparate data-sources, commonly dubbed Master-Data-Management (MDM), is usually not a fun exercise. I would like to show you how using a graph database (Neo4j) and an interesting dataset (developer-oriented collaboration sites) to put the fun back into MDM. This approach will allow you to quickly and sensibly merge data from different sources into a consistent picture and query across the data efficiently to answer your most pressing questions.

You can read the associated blog posts on my blog. The blog posts cover the hows and whys of the project, while this and other GraphGists will examine how to answer specific questions of the data.

The first step won’t have much to do with Master Data Managment. Here I’ve imported data from StackOverflow's API in order to start asking questions about what the site could do more easily with a Neo4j database available to it.

I’ve created a Clojure project to import questions, tags, answers, and associated users into Neo4j. This gives me the following model:

StackOverflow Model

I imported about 1000 questions from the API, but since GraphGists render in the browser I’ve limited the dataset here. Be warned that this GraphGist may still take some time to completely render

  create constraint on (n:`Answer`) assert n.`answer_id` is unique;
  create constraint on (n:`Question`) assert n.`question_id` is unique;
  create constraint on (n:`Tag`) assert n.`text` is unique;
  create constraint on (n:`User`) assert n.`user_id` is unique;
  create (_1251:`Question` {`answer_count`:1, `creation_date`:1423027961, `is_answered`:true, `last_activity_date`:1423549857, `link`:"http://stackoverflow.com/questions/28314155/slow-aggregation-on-big-neo4j-graph", `question_id`:28314155, `score`:1, `title`:"Slow aggregation on big neo4j graph", `view_count`:51})
  create (_1252:`User` {`accept_rate`:60, `display_name`:"mif", `link`:"http://stackoverflow.com/users/1559577/mif", `profile_image`:"https://www.gravatar.com/avatar/11319c5eddcd15fcb55a2a3fc3418ad8?s=128&d=identicon&r=PG", `reputation`:37, `user_id`:1559577, `user_type`:"registered"})
  create (_1253:`Tag` {`text`:"performance"})
  create (_1254:`Tag` {`text`:"neo4j"})
  create (_1255:`Tag` {`text`:"cypher"})
  create (_1256:`Question` {`answer_count`:3, `creation_date`:1375125005, `is_answered`:true, `last_activity_date`:1423549688, `link`:"http://stackoverflow.com/questions/17932256/how-to-visualize-a-neo4j-graph", `question_id`:17932256, `score`:5, `title`:"How to visualize a neo4j graph", `view_count`:5294})
  create (_1257:`User` {`accept_rate`:76, `display_name`:"QuentinTanioartino", `link`:"http://stackoverflow.com/users/2511306/quentintanioartino", `profile_image`:"http://i.stack.imgur.com/HChZ5.jpg?s=128&g=1", `reputation`:166, `user_id`:2511306, `user_type`:"registered"})
  create (_1258:`Tag` {`text`:"java"})
  create (_1259:`Tag` {`text`:"eclipse"})
  create (_1260:`Tag` {`text`:"user-interface"})
  create (_1261:`Tag` {`text`:"japplet"})
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  create (_1264:`Tag` {`text`:"clojure"})
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  create (_1268:`User` {`display_name`:"Joern", `link`:"http://stackoverflow.com/users/4519861/joern", `profile_image`:"https://www.gravatar.com/avatar/d4db69394439fcdb7c9f3dc28542cdba?s=128&d=identicon&r=PG&f=1", `reputation`:1, `user_id`:4519861, `user_type`:"registered"})
  create (_1269:`Tag` {`text`:"spring"})
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  create (_1280:`Tag` {`text`:"relationship"})
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  create (_1282:`Tag` {`text`:"direction"})
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  create _1865-[:`ASKED`]->_1864
  create _1868<-[:`TAGS`]-_1255
  create _1868<-[:`TAGS`]-_1254
  create _1869-[:`ASKED`]->_1868
  create _1885-[:`ASKED`]->_2097
  create _1885-[:`ASKED`]->_2056
  create _1885-[:`ASKED`]->_1884
  create _1916-[:`ASKED`]->_2521
  create _1916-[:`ASKED`]->_2311
  create _1916-[:`ASKED`]->_2110
  create _1916-[:`ASKED`]->_1915
  create _1916-[:`PROVIDED`]->_2708
  create _1916-[:`PROVIDED`]->_2489
  create _1929-[:`ASKED`]->_2109
  create _1929-[:`ASKED`]->_1928
  create _1929-[:`PROVIDED`]->_2277
  create _2004-[:`ASKED`]->_2206
  create _2004-[:`ASKED`]->_2179
  create _2004-[:`ASKED`]->_2003
  create _2004-[:`PROVIDED`]->_2043
  create _2014-[:`ANSWERS`]->_1811
  create _2015-[:`ANSWERS`]->_1811
  create _2016-[:`ANSWERS`]->_1813
  create _2017-[:`ANSWERS`]->_1817
  create _2018-[:`ANSWERS`]->_1813
  create _2019-[:`ANSWERS`]->_1818
  create _2020-[:`ANSWERS`]->_1818
  create _2021-[:`ANSWERS`]->_1824
  create _2022-[:`ASKED`]->_2309
  create _2022-[:`PROVIDED`]->_3625
  create _2022-[:`PROVIDED`]->_3623
  create _2022-[:`PROVIDED`]->_3189
  create _2022-[:`PROVIDED`]->_3165
  create _2022-[:`PROVIDED`]->_3163
  create _2022-[:`PROVIDED`]->_2969
  create _2022-[:`PROVIDED`]->_2955
  create _2022-[:`PROVIDED`]->_2937
  create _2022-[:`PROVIDED`]->_2712
  create _2022-[:`PROVIDED`]->_2502
  create _2022-[:`PROVIDED`]->_2270
  create _2022-[:`PROVIDED`]->_2021
  create _2023-[:`ANSWERS`]->_1827
  create _2024-[:`ANSWERS`]->_1829
  create _2025-[:`ANSWERS`]->_1831
  create _2026-[:`ANSWERS`]->_1834
  create _2027-[:`ASKED`]->_3215
  create _2027-[:`PROVIDED`]->_3405
  create _2027-[:`PROVIDED`]->_3171
  create _2027-[:`PROVIDED`]->_2957
  create _2027-[:`PROVIDED`]->_2948
  create _2027-[:`PROVIDED`]->_2026
  create _2028-[:`ANSWERS`]->_1836
  create _2029-[:`PROVIDED`]->_2028
  create _2030-[:`ANSWERS`]->_1838
  create _2031-[:`ANSWERS`]->_1840
  create _2032-[:`ANSWERS`]->_1836
  create _2033-[:`PROVIDED`]->_2032
  create _2034-[:`ANSWERS`]->_1842
  create _2035-[:`ANSWERS`]->_1845
  create _2036-[:`PROVIDED`]->_2035
  create _2037-[:`ANSWERS`]->_1847
  create _2038-[:`ANSWERS`]->_1849
  create _2039-[:`PROVIDED`]->_2480
  create _2039-[:`PROVIDED`]->_2479
  create _2039-[:`PROVIDED`]->_2473
  create _2039-[:`PROVIDED`]->_2269
  create _2039-[:`PROVIDED`]->_2038
  create _2040-[:`ANSWERS`]->_1853
  create _2041-[:`ANSWERS`]->_1854
  create _2042-[:`ANSWERS`]->_1847
  create _2043-[:`ANSWERS`]->_1829
  create _2044-[:`ANSWERS`]->_1847
  create _2045-[:`ASKED`]->_3586
  create _2045-[:`ASKED`]->_3570
  create _2045-[:`ASKED`]->_3513
  create _2045-[:`ASKED`]->_3270
  create _2045-[:`PROVIDED`]->_2044
  create _2046-[:`ANSWERS`]->_1861
  create _2047-[:`ASKED`]->_3118
  create _2047-[:`ASKED`]->_2882
  create _2047-[:`PROVIDED`]->_3635
  create _2047-[:`PROVIDED`]->_3624
  create _2047-[:`PROVIDED`]->_3414
  create _2047-[:`PROVIDED`]->_2720
  create _2047-[:`PROVIDED`]->_2715
  create _2047-[:`PROVIDED`]->_2711
  create _2047-[:`PROVIDED`]->_2705
  create _2047-[:`PROVIDED`]->_2046
  create _2048-[:`ANSWERS`]->_1858
  create _2049-[:`ANSWERS`]->_1864
  create _2050-[:`ANSWERS`]->_1858
  create _2051-[:`PROVIDED`]->_2478
  create _2051-[:`PROVIDED`]->_2050
  create _2052-[:`ANSWERS`]->_1868
  create _2053<-[:`TAGS`]-_1342
  create _2053<-[:`TAGS`]-_2055
  create _2053<-[:`TAGS`]-_1280
  create _2053<-[:`TAGS`]-_1254
  create _2054-[:`ASKED`]->_2053
  create _2056<-[:`TAGS`]-_2057
  create _2056<-[:`TAGS`]-_1254
  create _2056<-[:`TAGS`]-_1613
  create _2058<-[:`TAGS`]-_1255
  create _2058<-[:`TAGS`]-_1254
  create _2059-[:`ASKED`]->_2058
  create _2060<-[:`TAGS`]-_1325
  create _2060<-[:`TAGS`]-_1254
  create _2060<-[:`TAGS`]-_1324
  create _2061-[:`ASKED`]->_2177
  create _2061-[:`ASKED`]->_2060
  create _2061-[:`PROVIDED`]->_2275
  create _2062<-[:`TAGS`]-_1254
  create _2063-[:`ASKED`]->_2564
  create _2063-[:`ASKED`]->_2542
  create _2063-[:`ASKED`]->_2333
  create _2063-[:`ASKED`]->_2062
  create _2063-[:`PROVIDED`]->_2728
  create _2064<-[:`TAGS`]-_2067
  create _2064<-[:`TAGS`]-_1285
  create _2064<-[:`TAGS`]-_2066
  create _2064<-[:`TAGS`]-_1254
  create _2064<-[:`TAGS`]-_1429
  create _2065-[:`ASKED`]->_2064
  create _2068<-[:`TAGS`]-_2071
  create _2068<-[:`TAGS`]-_1356
  create _2068<-[:`TAGS`]-_1254
  create _2068<-[:`TAGS`]-_2070
  create _2068<-[:`TAGS`]-_1692
  create _2069-[:`ASKED`]->_2241
  create _2069-[:`ASKED`]->_2141
  create _2069-[:`ASKED`]->_2068
  create _2072<-[:`TAGS`]-_1254
  create _2072<-[:`TAGS`]-_2074
  create _2072<-[:`TAGS`]-_1259
  create _2072<-[:`TAGS`]-_1258
  create _2073-[:`ASKED`]->_2072
  create _2077<-[:`TAGS`]-_1485
  create _2077<-[:`TAGS`]-_1342
  create _2077<-[:`TAGS`]-_1254
  create _2077<-[:`TAGS`]-_1512
  create _2078-[:`ASKED`]->_2077
  create _2079<-[:`TAGS`]-_1281
  create _2079<-[:`TAGS`]-_1254
  create _2080-[:`ASKED`]->_2079
  create _2080-[:`PROVIDED`]->_2259
  create _2085<-[:`TAGS`]-_1254
  create _2085<-[:`TAGS`]-_1723
  create _2085<-[:`TAGS`]-_1258
  create _2086-[:`ASKED`]->_2085
  create _2087<-[:`TAGS`]-_1255
  create _2087<-[:`TAGS`]-_1254
  create _2088-[:`ASKED`]->_2087
  create _2088-[:`PROVIDED`]->_2263
  create _2089<-[:`TAGS`]-_1356
  create _2089<-[:`TAGS`]-_1254
  create _2089<-[:`TAGS`]-_2091
  create _2089<-[:`TAGS`]-_1692
  create _2090-[:`ASKED`]->_2089
  create _2094<-[:`TAGS`]-_1356
  create _2094<-[:`TAGS`]-_2096
  create _2094<-[:`TAGS`]-_1336
  create _2094<-[:`TAGS`]-_1254
  create _2094<-[:`TAGS`]-_1370
  create _2095-[:`ASKED`]->_2094
  create _2097<-[:`TAGS`]-_1255
  create _2097<-[:`TAGS`]-_1254
  create _2098<-[:`TAGS`]-_1255
  create _2098<-[:`TAGS`]-_1254
  create _2098<-[:`TAGS`]-_1253
  create _2099-[:`ASKED`]->_2098
  create _2100<-[:`TAGS`]-_1254
  create _2101<-[:`TAGS`]-_1255
  create _2101<-[:`TAGS`]-_1254
  create _2102-[:`ASKED`]->_2101
  create _2103<-[:`TAGS`]-_1255
  create _2103<-[:`TAGS`]-_1254
  create _2104-[:`ASKED`]->_2103
  create _2105<-[:`TAGS`]-_1764
  create _2105<-[:`TAGS`]-_1285
  create _2105<-[:`TAGS`]-_1254
  create _2106<-[:`TAGS`]-_1342
  create _2106<-[:`TAGS`]-_1254
  create _2106<-[:`TAGS`]-_2108
  create _2106<-[:`TAGS`]-_1336
  create _2106<-[:`TAGS`]-_1335
  create _2107-[:`ASKED`]->_2106
  create _2109<-[:`TAGS`]-_1254
  create _2109<-[:`TAGS`]-_1500
  create _2110<-[:`TAGS`]-_1255
  create _2110<-[:`TAGS`]-_1254
  create _2110<-[:`TAGS`]-_1321
  create _2111<-[:`TAGS`]-_1254
  create _2111<-[:`TAGS`]-_2113
  create _2112-[:`ASKED`]->_2111
  create _2114<-[:`TAGS`]-_1254
  create _2115-[:`ASKED`]->_2114
  create _2116<-[:`TAGS`]-_2119
  create _2116<-[:`TAGS`]-_1342
  create _2116<-[:`TAGS`]-_2118
  create _2116<-[:`TAGS`]-_1254
  create _2117-[:`ASKED`]->_2116
  create _2250-[:`ANSWERS`]->_2053
  create _2251-[:`ANSWERS`]->_2056
  create _2252-[:`ANSWERS`]->_2060
  create _2253-[:`ANSWERS`]->_2062
  create _2254-[:`ANSWERS`]->_2064
  create _2255-[:`ANSWERS`]->_2068
  create _2256-[:`ANSWERS`]->_2072
  create _2257-[:`ANSWERS`]->_2077
  create _2258-[:`PROVIDED`]->_2257
  create _2259-[:`ANSWERS`]->_2079
  create _2260-[:`ANSWERS`]->_2079
  create _2261-[:`ANSWERS`]->_2077
  create _2262-[:`ANSWERS`]->_2085
  create _2263-[:`ANSWERS`]->_2087
  create _2264-[:`ANSWERS`]->_2089
  create _2265-[:`PROVIDED`]->_2264
  create _2266-[:`ANSWERS`]->_2094
  create _2267-[:`ANSWERS`]->_2097
  create _2268-[:`ANSWERS`]->_2098
  create _2269-[:`ANSWERS`]->_2100
  create _2270-[:`ANSWERS`]->_2101
  create _2271-[:`ANSWERS`]->_2103
  create _2272-[:`ANSWERS`]->_2058
  create _2273-[:`ANSWERS`]->_2058
  create _2274-[:`ANSWERS`]->_2105
  create _2275-[:`ANSWERS`]->_2106
  create _2276-[:`ANSWERS`]->_2109
  create _2277-[:`ANSWERS`]->_2109
  create _2278-[:`ANSWERS`]->_2110
  create _2279-[:`ANSWERS`]->_2111
  create _2280-[:`ANSWERS`]->_2114
  create _2281-[:`PROVIDED`]->_2282
  create _2281-[:`PROVIDED`]->_2280
  create _2282-[:`ANSWERS`]->_2116

One of the first things that I thought of was creating a way to suggest tags to a user while they are filling out a question. The following query finds all questions which have both the neo4j and python tags and then aggregates the most popular tags on those questions.

Related tags for python
  MATCH (question:Question)<-[:TAGS]-(tag:Tag)
  WHERE tag.text IN ['neo4j', 'python']
  WITH question, count(DISTINCT tag) AS count WHERE count = 2 WITH question
  MATCH question<-[:TAGS]-(tag:Tag)
  WHERE NOT(tag.text IN ['neo4j', 'python'])
  RETURN tag.text, COUNT(tag) ORDER BY COUNT(tag) DESC

If this were a query in an application we would replace the two occurrences of the array of tags with a parameter.

Table 1. Related tags for python, results from full import

py2neo

71

cypher

32

bulbs

20

django

19

neo4django

11

Because the tags are attached to the question and not the answer, it would normally be a bit harder to query for what tags users are answering for. Of course, an extra relationship isn’t much of a problem for Neo4j.

Say we wanted the StackOverflow application to find topic experts based on given tags. This might be useful if the question was going unanswered and we wanted the system to do a targeted E-Mail to experts to see if they would be interested in contributing. Here is how you would do that in Neo4j:

Finding experts
  MATCH (question:Question)<-[:TAGS]-(tag:Tag)
  WHERE tag.text IN ['neo4j', 'python']
  WITH question, count(DISTINCT tag) AS count WHERE count = 2 WITH question
  MATCH question<-[:ANSWERS]-(answer:Answer)<-[:PROVIDED]-(user:User)
  RETURN user.display_name, count(answer)
  ORDER BY count(answer) DESC
Table 2. Finding exports, results from full import

Nigel Small

15

espeed

5

Peter Neubauer

3

Javier de la Rosa

2

Wes Freeman

2

If we wanted to look across a number of users to find out what subjects they are knowledgable in, we could query like this:

Finding expertise
  MATCH (user:User)-[:PROVIDED]->(answer:Answer)-[:ANSWERS]->(:Question)<-[:TAGS]-(tag:Tag)
  WITH user, tag, count(answer.score) AS count
  ORDER BY count DESC
  WHERE count > 1 AND NOT(tag.text IN ['neo4j', 'cypher'])
  RETURN user.display_name, collect({ count: count, tag: tag.text })
  LIMIT 10

F.O.O

[{count: 2, tag: "spring-data-neo4j"}]

agpt

[{count: 2, tag: "spring-data-neo4j"},{count: 2, tag: "java"}]

ean5533

[{count: 2, tag: "java"}]

stephen mallette

[{count: 11, tag: "gremlin"},{count: 6, tag: "graph-databases"},{count: 5, tag: "tinkerpop"},{count: 4, tag: "titan"},{count: 2, tag: "orient-db"}]

Javier de la Rosa

[{count: 2, tag: "neo4j.py"},{count: 2, tag: "python"}]

bendaizer

[{count: 2, tag: "graph-databases"},{count: 2, tag: "nosql"}]

funglejunk

[{count: 2, tag: "java"}]

MYK

[{count: 2, tag: "ajax"},{count: 2, tag: "jquery"}]

TobyTheDog

[{count: 2, tag: "database"}]

lassewesth

[{count: 3, tag: "spring-data-neo4j"},{count: 2, tag: "spring-security"}]

Maybe we want to find out if one user has often answered question for another user.

Potential "mentor" relationships
MATCH (answerer:User)-[:PROVIDED]->(a:Answer)-[:ANSWERS]->(q:Question)<-[:ASKED]-(asker:User)
RETURN asker.display_name, answerer.display_name, count(q)
ORDER BY count(q) DESC
Table 3. Potential "mentor" relationships, results from full import
asker.display_name answerer.display_name count(q)

Clam

subvertallchris

5

deemeetree

Stefan Armbruster

4

salvador

Stefan Armbruster

3

red-devil

Michael Hunger

3

penta

Stefan Armbruster

3

And those are a few ways which we can explore the StackOverflow model with Neo4j.

Soon I will be developing more resources on how to combine this StackOverflow data with other datasources. Stay tuned to my blog!

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