Transcript pptx - UGA CS home page

Graphs
(Part I)
Shannon Quinn
(with thanks to William Cohen of CMU and Jure Leskovec,
Anand Rajaraman, and Jeff Ullman of Stanford University)
Why I’m talking about graphs
• Lots of large data is graphs
– Facebook, Twitter, citation data, and other social
networks
– The web, the blogosphere, the semantic web, Freebase,
Wikipedia, Twitter, and other information networks
– Text corpora (like RCV1), large datasets with discrete
feature values, and other bipartite networks
• nodes = documents or words
• links connect document  word or word  document
– Computer networks, biological networks (proteins,
ecosystems, brains, …), …
– Heterogeneous networks with multiple types of nodes
• people, groups, documents
Properties of Graphs
Descriptive Statistics
• Number of connected
components
• Diameter
• Degree distribution
• …
Models of Growth/Formation
•
•
•
•
Erdos-Renyi
Preferential attachment
Stochastic block models
….
Let’s look at some examples of graphs … but
first, why are these statistics important?
An important question
• How do you explore a dataset?
– compute statistics (e.g., feature histograms,
conditional feature histograms, correlation
coefficients, …)
– sample and inspect
• run a bunch of small-scale experiments
• How do you explore a graph?
– compute statistics (degree distribution, …)
– sample and inspect
• how do you sample?
Protein-Protein Interactions
Can we identify
functional modules?
Nodes: Proteins
J. Leskovec, A. Rajaraman, J. Ullman: Mining
Edges: Physical
of Massive Datasets, http://www.mmds.org
5
Protein-Protein Interactions
Functional modules
Nodes: Proteins
J. Leskovec, A. Rajaraman, J. Ullman: Mining
Edges: Physical
of Massive Datasets, http://www.mmds.org
6
Facebook Network
Can we identify
social
communities?
Nodes: Facebook
J. Leskovec, A. Rajaraman, J. Ullman: Mining
of Massive Datasets, http://www.mmds.org Users
7
Facebook Network
Social communities
High school
Summer
internship
Stanford (Basketball)
Stanford (Squash)
Nodes: Facebook
J. Leskovec, A. Rajaraman, J. Ullman: Mining
of Massive Datasets, http://www.mmds.org Users
8
Blogs
Citations
Blogs
Blogs
Graphs
• Set V of vertices/nodes v1,… , set E of edges (u,v),….
– Edges might be directed and/or weighted and/or labeled
• Degree of v is #edges touching v
– Indegree and Outdegree for directed graphs
• Path is sequence of edges (v0,v1),(v1,v2),….
• Geodesic path between u and v is shortest path connecting them
– Diameter is max u,v in V {length of geodesic between u,v}
– Effective diameter is 90th percentile
– Mean diameter is over connected pairs
• (Connected) component is subset of nodes that are all pairwise connected
via paths
• Clique is subset of nodes that are all pairwise connected via edges
• Triangle is a clique of size three
Graphs
• Some common properties of
graphs:
– Distribution of node
degrees
– Distribution of cliques
(e.g., triangles)
– Distribution of paths
• Diameter (max
shortest-path)
• Effective diameter
(90th percentile)
• Connected
components
– …
• Some types of graphs to
consider:
– Real graphs (social &
otherwise)
– Generated graphs:
• Erdos-Renyi
“Bernoulli” or
“Poisson”
• Watts-Strogatz “small
world” graphs
• Barbosi-Albert
“preferential
attachment”
• …
Erdos-Renyi graphs
• Take n nodes, and connect each pair with
probability p
– Mean degree is z=p(n-1)
k z
 n k
z
e
nk
Pr[degree (v)  k ]  pk    p (1  p) 
for fixed z, large n
k!
k 
 n  k n k p k (np) k z k
  p 


k!
k!
k!
k 
e
z
Erdos-Renyi graphs
• Take n nodes, and connect each pair with probability
p
– Mean degree is z=p(n-1)
– Mean number of neighbors distance d from v is zd
– How large does d need to be so that zd >=n ?
• If z>1, d = log(n)/log(z)
• If z<1, you can’t do it
– So:
• There tend to be either many small components (z<1)
or one large one (z>1) giant connected component)
– Another intuition:
• If there are a two large connected components, then
with high probability a few random edges will link
them up.
Erdos-Renyi graphs
• Take n nodes, and connect each pair with
probability p
– Mean degree is z=p(n-1)
– Mean number of neighbors distance d from v is
zd
– How large does d need to be so that zd >=n ?
• If z>1, d = log(n)/log(z)
• If z<1, you can’t do it
– So:
• If z>1, diameters tend to be small (relative to n)
Sociometry, Vol. 32, No. 4. (Dec., 1969), pp. 425-443.
64 of 296
chains
succeed,
avg chain
length is
6.2
Illustrations of the Small World
• Millgram’s experiment
• Erdős numbers
– http://www.ams.org/mathscinet/searchauthors.html
• Bacon numbers
– http://oracleofbacon.org/
• LinkedIn
– http://www.linkedin.com/
– Privacy issues: the whole network is not
visible to all
Erdos-Renyi graphs
• Take n nodes, and connect each pair with
probability p
– Mean degree is z=p(n-1)
n k
nk


Pr[degree (v)  k ]  pk    p (1  p)
k 
This is usually not a good model
of degree distribution in natural
networks
Degree distribution
• Plot cumulative degree
– X axis is degree
– Y axis is #nodes that have
degree at least k
• Typically use a log-log scale
– Straight lines are a power
law; normal curve dives
to zero at some point
– Left: trust network in
epinions web site from
Richardson & Domingos
Degree distribution
• Plot cumulative degree
– X axis is degree
– Y axis is #nodes that have
degree at least k
• Typically use a log-log scale
– Straight lines are a power
law; normal curve dives
to zero at some point
• This defines a “scale”
for the network
– Left: trust network in
epinions web site from
Richardson & Domingos
pk  k

Graphs
• Some common properties of
graphs:
– Distribution of node
degrees
– Distribution of cliques
(e.g., triangles)
– Distribution of paths
• Diameter (max
shortest-path)
• Effective diameter
(90th percentile)
• Connected
components
– …
• Some types of graphs to
consider:
– Real graphs (social &
otherwise)
– Generated graphs:
• Erdos-Renyi
“Bernoulli” or
“Poisson”
• Watts-Strogatz “small
world” graphs
• Barbosi-Albert
“preferential
attachment”
• …
Graphs
• Some common properties of
graphs:
– Distribution of node degrees:
often scale-free
– Distribution of cliques (e.g.,
triangles)
– Distribution of paths
• Diameter (max shortestpath)
• Effective diameter (90th
percentile) often small
• Connected components
usually one giant CC
– …
• Some types of graphs to
consider:
– Real graphs (social &
otherwise)
– Generated graphs:
• Erdos-Renyi
“Bernoulli” or “Poisson”
• Watts-Strogatz “small
world” graphs
• Barbosi-Albert
“preferential
attachment” generates
scale-free graphs
• …
Barabasi-Albert Networks
• Science 286 (1999)
• Start from a small number of node, add a new
node with m links
• Preferential Attachment
• Probability of these links to connect to existing
nodes is proportional to the node’s degree
 (ki ) 
ki
k j
j
• ‘Rich gets richer’
• This creates ‘hubs’: few nodes with very large
degrees
Random graph
(Erdos Renyi)
Preferential attachment
(Barabasi-Albert)
Graphs
• Some common properties of
graphs:
– Distribution of node degrees:
often scale-free
– Distribution of cliques (e.g.,
triangles)
– Distribution of paths
• Diameter (max shortestpath)
• Effective diameter (90th
percentile) often small
• Connected components
usually one giant CC
– …
• Some types of graphs to
consider:
– Real graphs (social &
otherwise)
– Generated graphs:
• Erdos-Renyi
“Bernoulli” or “Poisson”
• Watts-Strogatz “small
world” graphs
• Barbosi-Albert
“preferential
attachment” generates
scale-free graphs
• …
Homophily
• One definition: excess edges between similar nodes
– E.g., assume nodes are male and female and
Pr(male)=p, Pr(female)=q.
– Is Pr(gender(u)≠ gender(v) | edge (u,v)) >= 2pq?
• Another def’n: excess edges between common
neighbors of v
# triangles connected to v
CC (v) 
# pairs connected to v
1
CC (V , E ) 
CC (v)

|V | v
Homophily
• Another def’n: excess edges between
common neighbors of v
# triangles connected to v
CC (v) 
# pairs connected to v
1
CC (V , E ) 
CC (v)

|V | v
# triangles in graph
CC ' (V , E ) 
# length 3 paths in graph
Homophily
• In a random Erdos-Renyi graph:
# triangles in graph
1
CC ' (V , E ) 
 for large n
# length 3 paths in graph n
In natural graphs two of your mutual friends
might well be friends:
• Like you they are both in the same class (club,
field of CS, …)
• You introduced them
Watts-Strogatz model
• Start with a ring
• Connect each node to k
nearest neighbors
• homophily
• Add some random
shortcuts from one point
to another
•  small diameter
• Degree distribution not
scale-free
• Generalizes to d
dimensions
An important question
• How do you explore a dataset?
– compute statistics (e.g., feature histograms,
conditional feature histograms, correlation
coefficients, …)
– sample and inspect
• run a bunch of small-scale experiments
• How do you explore a graph?
– compute statistics (degree distribution, …)
– sample and inspect
• how do you sample?
KDD 2006
Brief summary
• Define goals of sampling:
– “scale-down” – find G’<G with similar statistics
– “back in time”: for a growing G, find G’<G that
is similar (statistically) to an earlier version of
G
• Experiment on real graphs with plausible
sampling methods, such as
– RN – random nodes, sampled uniformly
–…
• See how well they perform
Brief summary
• Experiment on real graphs with plausible
sampling methods, such as
– RN – random nodes, sampled uniformly
• RPN – random nodes, sampled by PageRank
• RDP – random nodes sampled by in-degree
– RE – random edges
– RJ – run PageRank’s “random surfer” for n
steps
– RW – run RWR’s “random surfer” for n steps
– FF – repeatedly pick r(i) neighbors of i to
“burn”, and then recursively sample from them
10% sample – pooled on five datasets
d-statistic measures agreement between
distributions
• D=max{|F(x)-F’(x)|} where F, F’ are cdf’s
• max over nine different statistics
Parallel Graph Computation
• Distributed computation and/or multicore
parallelism
– Sometimes confusing. We will talk mostly
about distributed computation.
• Are classic graph algorithms parallelizable? What
about distributed?
– Depth-first search?
– Breadth-first search?
– Priority-queue based traversals (Djikstra’s,
Prim’s algorithms)
MapReduce for Graphs
• Graph computation almost always iterative
• MapReduce ends up shipping the whole graph
on each iteration over the network (map>reduce->map->reduce->...)
– Mappers and reducers are stateless
Iterative Computation is Difficult
• System is not optimized for iteration:
Iterations
Data
Data
Data
CPU 1
CPU 1
CPU 3
Data
Data
Data
Data
Data
CPU 2
CPU 3
Data
Data
Data
Data
Data
Data
CPU 2
CPU 3
Disk Penalty
Data
Data
Data
Startup Penalty
Data
CPU 1
Disk Penalty
CPU 2
Data
Startup Penalty
Data
Disk Penalty
Startup Penalty
Data
Data
Data
Data
Data
Data
Data
Data
MapReduce and Partitioning
• Map-Reduce splits the keys randomly between
mappers/reducers
• But on natural graphs, high-degree vertices
(keys) may have million-times more edges
than the average
Extremely uneven distribution
Time of iteration = time of slowest job.
Curse of the Slow Job
Iterations
Data
Data
CPU 1
Data
CPU 1
Data
CPU 1
Data
Data
Data
Data
Data
Data
Data
Data
CPU 2
CPU 2
CPU 2
Data
Data
Data
Data
Data
Data
Data
Data
CPU 3
CPU 3
CPU 3
Data
Data
Data
Data
Data
Barrier
Data
Barrier
Data
Barrier
Data
http://www.www2011india.com/proceeding/proceedings/p607.pdf
Map-Reduce is Bulk-Synchronous
Parallel
• Bulk-Synchronous Parallel = BSP (Valiant, 80s)
– Each iteration sees only the values of previous
iteration.
– In linear systems literature: Jacobi iterations
• Pros:
– Simple to program
– Maximum parallelism
– Simple fault-tolerance
• Cons:
– Slower convergence
– Iteration time = time taken by the slowest node
Triangle Counting in Twitter Graph
Total:
34.8 Billion Triangles
40M Users
1.2B Edges
Hadoop
GraphLab
1536 Machines
423 Minutes
64 Machines, 1024 Cores
1.5 Minutes
Hadoop results from [Suri & Vassilvitskii '11]
PageRank
5.5 hrs
Hadoop
1 hr
Twister
GraphLab
8 min
40M Webpages, 1.4 Billion Links (100 iterations)
Hadoop results from [Kang et al. '11]
Twister (in-memory MapReduce) [Ekanayake et al. ‘10]
Graph algorithms
• PageRank implementations
– in memory
– streaming, node list in memory
– streaming, no memory
– map-reduce
• A little like Naïve Bayes variants
– data in memory
– word counts in memory
– stream-and-sort
– map-reduce
Google’s PageRank
web
site xxx
web
site xxx
web
site xxx
web site a b c
defg
web
web site yyyy
web site a b c
defg
web site yyyy
site
pdq pdq ..
Inlinks are “good”
(recommendations)
Inlinks from a “good” site
are better than inlinks from
a “bad” site
but inlinks from sites with
many outlinks are not as
“good”...
“Good” and “bad” are
relative.
Google’s PageRank
web
site xxx
web
site xxx
Imagine a “pagehopper”
that always either
• follows a random link, or
web site a b c
defg
• jumps to random page
web
web site yyyy
web site a b c
defg
web site yyyy
site
pdq pdq ..
Google’s PageRank
(Brin & Page, http://www-db.stanford.edu/~backrub/google.html)
web
site xxx
web
site xxx
Imagine a “pagehopper”
that always either
• follows a random link, or
web site a b c
defg
• jumps to random page
web
web site yyyy
web site a b c
defg
web site yyyy
site
pdq pdq ..
PageRank ranks pages by
the amount of time the
pagehopper spends on a
page:
• or, if there were many
pagehoppers, PageRank is
the expected “crowd size”
Random Walks
avoids messy “dead ends”….
Random Walks: PageRank
Random Walks: PageRank
Graph = Matrix
Vector = Node  Weight
v
M
A B
C D E
F
1
G H I
J
A
A _
1
1
B
1
_
1
B
C 1
1
_
C 3
A 3
D
_
1
1
D
E
1
_
1
E
1
1
_
F
F
G
1
C
_
H
1
1
G
_
1
1
H
I
1
1
_
1
I
J
1
1
1
_
J
M
2
A
B
G
I
H
J
F
D
E
PageRank in Memory
• Let u = (1/N, …, 1/N)
– dimension = #nodes N
• Let A = adjacency matrix: [aij=1  i links to j]
• Let W = [wij = aij/outdegree(i)]
– wij is probability of jump from i to j
• Let v0 = (1,1,….,1)
– or anything else you want
• Repeat until converged:
– Let vt+1 = cu + (1-c)Wvt
• c is probability of jumping “anywhere randomly”
Streaming PageRank
• Assume we can store v but not W in memory
• Repeat until converged:
– Let vt+1 = cu + (1-c)Wvt
• Store A as a row matrix: each line is
– i ji,1,…,ji,d [the neighbors of i]
• Store v’ and v in memory: v’ starts out as cu
• For each line “i ji,1,…,ji,d “
– For each j in ji,1,…,ji,d
Everything needed
for update is right
• v’[j] += (1-c)v[i]/d
there in row….
Streaming PageRank:
with some long rows
• Repeat until converged:
– Let vt+1 = cu + (1-c)Wvt
• Store A as a list of edges: each line is: “i d(i) j”
• Store v’ and v in memory: v’ starts out as cu
• For each line “i d j“
• v’[j] += (1-c)v[i]/d
We need to get the
degree of i and store
it locally
Streaming PageRank: preprocessing
•
•
•
•
Original encoding is edges (i,j)
Mapper replaces i,j with i,1
Reducer is a SumReducer
Result is pairs (i,d(i))
• Then: join this back with edges (i,j)
• For each i,j pair:
– send j as a message to node i in the degree table
• messages always sorted after non-messages
– the reducer for the degree table sees i,d(i) first
• then j1, j2, ….
• can output the key,value pairs with key=i, value=d(i), j
PageRank in MapReduce
More on graph algorithms
• PageRank is a one simple example of a graph algorithm
– but an important one
– personalized PageRank (aka “random walk with restart”) is an
important operation in machine learning/data analysis settings
• PageRank is typical in some ways
– Trivial when graph fits in memory
– Easy when node weights fit in memory
– More complex to do with constant memory
– A major expense is scanning through the graph many times
• … same as with SGD/Logistic regression
• disk-based streaming is much more expensive than memory-based
approaches
• Locality of access is very important!
• gains if you can pre-cluster the graph even approximately
• avoid sending messages across the network – keep them local
Machine Learning in Graphs - 2010
Some ideas
• Combiners are helpful
– Store outgoing incrementVBy messages and
aggregate them
– This is great for high indegree pages
• Hadoop’s combiners are suboptimal
– Messages get emitted before being
combined
– Hadoop makes weak guarantees about
combiner usage
This slide again!
Some ideas
• Most hyperlinks are within a domain
– If we keep domains on the same machine
this will mean more messages are local
– To do this, build a custom partitioner that
knows about the domain of each nodeId and
keeps nodes on the same domain together
– Assign node id’s so that nodes in the same
domain are together – partition node ids by
range
– Change Hadoop’s Partitioner for this
Some ideas
• Repeatedly shuffling the graph is expensive
– We should separate the messages about the
graph structure (fixed over time) from
messages about pageRank weights
(variable)
– compute and distribute the edges once
– read them in incrementally in the reducer
• not easy to do in Hadoop!
– call this the “Schimmy” pattern
Schimmy
Relies on fact that keys are sorted, and sorts the graph
input the same way…..
Schimmy
Results
More details at…
• Overlapping Community Detection at Scale: A Nonnegative Matrix
Factorization Approach by J. Yang, J. Leskovec. ACM International
Conference on Web Search and Data Mining (WSDM), 2013.
• Detecting Cohesive and 2-mode Communities in Directed and
Undirected Networks by J. Yang, J. McAuley, J. Leskovec. ACM
International Conference on Web Search and Data Mining (WSDM),
2014.
• Community Detection in Networks with Node Attributes by J. Yang, J.
McAuley, J. Leskovec. IEEE International Conference On Data Mining
(ICDM), 2013.
J. Leskovec, A. Rajaraman, J. Ullman: Mining
of Massive Datasets, http://www.mmds.org
71
Semi-Supervised Learning
With Graphs
Shannon Quinn
(with thanks to William Cohen at CMU)
Semi-supervised learning
• A pool of labeled examples L
• A (usually larger) pool of unlabeled examples U
• Can you improve accuracy somehow using U?
Semi-Supervised Bootstrapped
Learning/Self-training
Extract cities:
Paris
Pittsburgh
Seattle
Cupertino
San Francisco
Austin
denial
mayor of arg1
live in arg1
anxiety
selfishness
Berlin
arg1 is home of
traits such as arg1
Semi-Supervised Bootstrapped
Learning via Label Propagation
mayor of arg1
Paris
Pittsburgh
arg1 is home of
San Francisco
Austin
anxiety
live in arg1
traits such as arg1
denial
Seattle
selfishness
Semi-Supervised Bootstrapped
Learning via Label Propagation
mayor of arg1
Paris
Pittsburgh
arg1 is home of
San Francisco
Austin
traits such as arg1
traits such as arg1
live in arg1
denial
denial
Seattle
Nodes “near” seeds
Information from
other categories
tells youanxiety
“how far”
(when to stop
propagating)
arrogance
selfishness
selfishness
Nodes “far from” seeds
ASONAM-2010 (Advances in Social
Networks Analysis and Mining)
Network Datasets with Known Classes
•UBMCBlog
•AGBlog
•MSPBlog
•Cora
•Citeseer
RWR - fixpoint of:
Seed selection
1. order by PageRank, degree, or randomly
2. go down list until you have at least k examples/class
CoEM/HF/wvRN
• One definition [MacSkassy & Provost, JMLR 2007]:…
CoEM/HF/wvRN
• Another definition in [X. Zhu, Z. Ghahramani, and J.
Lafferty, ICML 2003]
– A harmonic field – the score of each node in the graph
is the harmonic, or linearly weighted, average of its
neighbors’ scores (harmonic field, HF)
CoEM/HF/wvRN
• Another
justification of
the same
algorithm….
• … start with
co-training
with a naïve
Bayes learner
How to do this minimization?
First, differentiate to find min is at
Jacobi method:
• To solve Ax=b for x
• Iterate:
• … or: