#smxlondon Everything You Need to Know About How GraphSearch Works in 15-ish Minutes
-
Upload
kelvin-newman -
Category
Business
-
view
1.209 -
download
3
Transcript of #smxlondon Everything You Need to Know About How GraphSearch Works in 15-ish Minutes
Everything You Need to Know About How GraphSearch Works
in 15-ish MinutesKelvin Newman
@kelvinnewman
Edges, Nodes and a Frickin’ Unicorn
http://www.escapefromcubiclenation.com/
Strategy DirectorSiteVisibility
A digital agency specialising in retail, travel and financial services
This much is Facebook
http://mashable.com/2010/11/19/facebook-traffic-stats/
documents and links
JD Hancock
things and relationships
JD Hancock
but I think it works better for the web
as we know it
JD Hancock
Facebook’s data has a far more
explicit structure than traditional
web text
D Hancock
it’s not that tricky for Google to parse “I Like Nerf Guns”
porkist
they could even have a go at “I was at Cattlegrid in Leeds for
Lunch Yesterday”**if you mark it up in the right way
R_Savvy
On GraphSearch you’re not really making a
search.
You’re just filtering a structured database of all
the data Facebook has.
But it’s a bloody big database
JD Hancock
Every User, Page, Photo, Post & Place is a Node
JD Hancock
Every friendship, checkin, tag or like is an Edge
JD Hancock
GraphSearch Allows you search the Edges as well as the
Nodes
JD Hancock
an inverted index (also referred to as postings file or inverted file) is an index data structure storing a mapping from content, such as words or numbers, to its locations in a database file, or in a document or a set of documents. The purpose of an inverted index is to allow fast full text searches, at a cost of increased processing when a document is added to the database.
The main components of Unicorn are:
■The index -- a many-to-many mapping from attributes (strings) to entities (fbids)
■A framework to build the index from other persistent data and incremental updates
■A framework to retrieve entities from the index based on various constraints on attributes
Suppose your friend has fbid 1234 and lives in New York and likes Downton Abbey. The index corresponding to your friend will include the mappings:
friend:10003 → 1234 lives-in:111 → 1234 like:222 → 1234
Here, we assume your fbid is 10003, and the fbid’s of New York and Downtown Abbey are 111 and 222 respectively.
In addition, friend:10003, lives-in:111, and like:222 may map to other users that share these attributes.
Unicorn makes it easy to find nodes that are connected to another node by searching for an edge-type combined with an input node.
E.g.:■Your friends: friend:10003■People who live in new york: lives-in:111■People who like downtown abbey: like:222
‘Facebook use query-independent signals to come up with a numeric value for importance.
This value is called the “static rank” of the entity.’
JD Hancock
What makes up static rank is still up for debate, but sensibly could be
informed by the elements of Edgerank
aka
the newsfeed algo
Constantly Build Affinity
subodh_chettri
Ask Questions
fontplaydotcom
Have a vote
ChodHound
Bait
Text
derekGavey