Data Mining Seminar - Graph Mining and Social Network Analysis

Graph Mining and Social
Network Analysis
Data Mining; EECS 4412
Darren Rolfe + Vince Chu
11.06.14

Agenda
Graph Mining
Methods for Mining Frequent Subgraphs
Apriori-based Approach: AGM, FSG
Pattern-Growth Approach: gSpan
Social Networks Analysis
Properties and Features of Social Real Graphs
Models of Graphs we can use
Using those models to predict/other things

Graph Mining
Methods for Mining Frequent Subgraphs

Why Mine Graphs?
A lot of data today can be represented in the form of a graph
Social: Friendship networks, social media networks, email and instant
messaging networks, document citation networks, blogs
Technological: Power grid, the internet
Biological: Spread of virus/disease, protein/gene regulatory networks

What Do We Need To Do
Identify various kinds of graph patterns
Frequent substructures are the very basic patterns that can be discovered in
a collection of graphs, useful for:
characterizing graph sets,
discriminating different groups of graphs,
classifying and clustering graphs,
building graph indices, and
facilitating similarity search in graph databases

Mining Frequent Subgraphs
Performed on a collection of graphs
Notation:
Vertex set of a graph 𝑔 by 𝑉(𝑔)
Edge set of a graph 𝑔 by 𝐸(𝑔)
A label function, 𝐿, maps a vertex or an edge to a label.
A graph 𝑔 is a subgraph of another graph 𝑔’ if there exists a subgraph isomorphism from 𝑔 to 𝑔’.
Given a labeled graph data set, 𝐷 = {𝐺1, 𝐺2, … , 𝐺𝑛}, we deﬁne 𝑠𝑢𝑝𝑝𝑜𝑟𝑡(𝑔) (or 𝑓𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦(𝑔)) as the
percentage (or number) of graphs in 𝐷 where 𝑔 is a subgraph.
A frequent graph is a graph whose support is no less than a minimum support threshold, 𝑚𝑖𝑛_𝑠𝑢𝑝.

Discovering Frequent Substructures
Usually consists of two steps:
1. Generate frequent substructure candidates.
2. Check the frequency of each candidate.
Most studies on frequent substructure discovery focus on the optimization of
the ﬁrst step, because the second step involves a subgraph isomorphism test
whose computational complexity is excessively high (i.e., NP-complete).

Graph Isomorphism
Isomorphism of graphs G and H
is a bijection between the vertex
sets of G and H
𝐹: 𝑉(𝑔) → 𝑉(𝐻)
Such that any two vertices 𝑢 and 𝑣 of
𝐺 are adjacent in 𝐺 if and only if
ƒ(𝑢) and ƒ(𝑣) are adjacent in 𝐻.
A G
B H
C I
D J
A
G B
H
C
I D
J
Graph G Graph H

Frequent
Subgraphs: An
Example
1. Start with a labelled graph
data set.
2. Set a minimum support
threshold for frequent
graph.
3. Generate frequent
substructure candidates.
4. Check the frequency of each
candidate.
A B
C A
B B
C A
A B
C A
C B
A B
Graph 1 Graph 2
Graph 3 Graph 4

Frequent
Subgraphs: An
Example
Let the support minimum for
this example be 50%.
data set.
graph.
candidate.

Frequent
Subgraphs: An
Example
data set.
graph.
candidate.
A B C
A B
B C
A C
A A
B B
A B
C
A A
C
B B
C
A B
C A
A B
C A
B B
C A
C B
A B
B B
C A
B B
C A
k = 1
k = 2
k = 3
k = 4

Frequent
Subgraphs: An
Example
data set.
graph.
candidate.
A B C
A B
B C
A C
A A
B B
A B
C
A A
C
A B
C A
A B
C A
B B
C A
C B
A B
B B
C A
B B
C A
k = 1
k = 2
k = 3
k = 4
B B
C

Frequent
Subgraphs: An
Example
data set.
graph.
candidate.
A B
C A
A B
C A
C B
A B
Graph 1 Graph 2
Graph 3 Graph 4
B B
C A
k = 3, frequency: 3, support: 75%

Frequent
Subgraphs: An
Example
data set.
graph.
candidate.
A B
C A
C B
A B
Graph 1 Graph 2
Graph 3 Graph 4
B B
C A
A B
C A
k = 4, frequency: 2, support: 50%

Apriori-based Approach
Apriori-based frequent substructure mining algorithms share similar
characteristics with Apriori-based frequent itemset mining algorithms.
Search for frequent graphs:
Starts with graphs of small “size”; deﬁnition of graph size depends on algorithm used.
Proceeds in a bottom-up manner by generating candidates having an extra vertex, edge, or
path.
Main design complexity of Apriori-based substructure mining algorithms is
candidate generation step.
Candidate generation problem in frequent substructure mining is harder than that in
frequent itemset mining, because there are many ways to join two substructures.

Apriori-based Approach
1. Generate size 𝑘 frequent subgraph candidates
Generated by joining two similar but slightly different frequent subgraphs that were
discovered in the previous call of the algorithm.
2. Check the frequency of each candidate
3. Generate the size 𝑘 + 1 frequent candidates
4. Continue until candidates are empty

Algorithm: AprioriGraph
Apriori-based Frequent Substructure Mining
Input:
𝐷, a graph data set
𝑚𝑖𝑛_𝑠𝑢𝑝, minimum support threshold
Output:
𝑆 𝑘, frequent substructure set
Method:
𝑆1 ← frequent single-elements in 𝐷
Call 𝐴𝑝𝑟𝑖𝑜𝑟𝑖𝐺𝑟𝑎𝑝ℎ(𝐷, 𝑚𝑖𝑛_𝑠𝑢𝑝, 𝑆1)
procedure AprioriGraph(D, min_sup, Sk)
1 Sk+1 ← ∅;
2 foreach frequent gi ∈ Sk do
3 foreach frequent gj ∈ Sk do
4 foreach size (k+1) graph g formed by merge(gi, gj) do
5 if g is frequent in D and g ∉ Sk+1 then
6 insert g into Sk+1;
7 if sk+1 ≠ ∅ then
8 AprioriGraph(D, min_sup, Sk+1);
9 return;

AGM - Apriori-based Graph Mining
Vertex-based candidate generation method that increases the substructure
size by one vertex at each iteration of AprioriGraph.
𝑘, graph size is the number of vertices in the graph
Two size-k frequent graphs are joined only if they have the same size-(k−1) subgraph.
Newly formed candidate includes the size-(k−1) subgraph in common and the additional
two vertices from the two size-k patterns.
Because it is undetermined whether there is an edge connecting the additional two
vertices, we actually can form two substructures.

AGM: An
Example
Two substructures joined by
two chains.
𝑘, graph size is the number of
vertices in the graph
A B
C A
B B
C A
+
A B
C A
B
A B
C A
B
k = 4
k = 5

FSG – Frequent Subgraph Discovery
Edge-based candidate generation strategy that increases the substructure size
by one edge in each call of AprioriGraph.
𝑘, graph size is the number of edges in the graph
Two size-k patterns are merged if and only if they share the same subgraph having k−1
edges, which is called the core.
Newly formed candidate includes the core and the additional two edges from size-k
patterns.

FSG: An
Example
Two substructure patterns and
their potential candidates.
edges in the graph
B B
C A
B B
C A
+
B B
C A
B
B B
C A
k = 4
k = 5

A A
A A
C
FSG: Another
Example
Two substructure patterns and
their potential candidates.
edges in the graph
AA
AA
B
+
k = 5
k = 6
AA
AA
B
C
A
AA
CB
A A
AA
CB
A

Pitfall: Apriori-based Approach
Generation of subgraph candidates is complicated and expensive.
Level-wise candidate generation → Breadth-first search
To determine whether a size-(k+1) graph is frequent, must check all corresponding size-k
subgraphs to obtain the upper bound of frequency.
Before mining any size-(k+1) subgraph, requires complete mining of size-k subgraphs
Subgraph isomorphism is an NP Subgraph isomorphism is an NP-complete
problem, so pruning is expensive.

Pattern-Growth Approach
1. Initially, start with the frequent vertices as frequent graphs
2. Extend these graphs by adding a new edge such that newly formed graphs are
frequent graphs
A graph g can be extended by adding a new edge e; newly formed graph is denoted by 𝑔  𝑥 𝑒.
If e introduces a new vertex, we denote the new graph by 𝑔  𝑥𝑓 𝑒, otherwise 𝑔  𝑥𝑏 𝑒, where f or
b indicates that the extension is in a forward or backward direction
3. For each discovered graph g, it performs extensions recursively until all the
frequent graphs with g embedded are discovered.
4. The recursion stops once no frequent graph can be generated.

Algorithm: PatternGrowthGraph
Simplistic Pattern Growth-based Frequent Substructure Mining
Input:
𝑔, a frequent graph
𝑚𝑖𝑛_𝑠𝑢𝑝, minimum support threshold
Output:
𝑆, frequent graph set
Method:
𝑆 ← ∅
Call 𝑃𝑎𝑡𝑡𝑒𝑟𝑛𝐺𝑟𝑜𝑤𝑡ℎ𝐺𝑟𝑎𝑝ℎ(𝑔, 𝐷, 𝑚𝑖𝑛_𝑠𝑢𝑝, 𝑆)
procedure PatternGrowthGraph(g, D, min_sup, S)
1 if g ∈ S then return;
2 else insert g into S;
3 scan D once, ﬁnd all edges e that g can be extended to g  𝑥e;
4 foreach frequent g  𝑥e do
5 PatternGrowthGraph(g  𝑥e, D, min_sup, S);
6 return;

Pattern-Growth:
An Example
1. Start with the frequent
vertices as frequent graphs
2. Extend these graphs by
adding a new edge forming
new frequent graphs
3. For each discovered graph
g, recursively extend
4. Stops once no frequent
graph can be generated
A B
C A
B B
C A
A B
C A
C B
A B
Graph 1 Graph 2
Graph 3 Graph 4

Pattern-Growth:
An Example
Let the support minimum for
this example be 50%.
new frequent graphs

Pattern-Growth:
An Example
new frequent graphs
A B
C A
B B
C A
A B
C A
C B
A B
Graph 1 Graph 2
Graph 3 Graph 4
Let’s arbitrarily start with this frequent vertex

Pattern-Growth:
An Example
new frequent graphs
A B
C A
B B
C A
A B
C A
C B
A B
Graph 1 Graph 2
Graph 3 Graph 4
Extend graph (forward); add frequent edge

Pattern-Growth:
An Example
new frequent graphs
Graph 1 Graph 2
Graph 3 Graph 4
Extend frequent graph (forward) again…
A B
C A
B B
C A
A B
C A
C B
A B

Pattern-Growth:
An Example
new frequent graphs
Graph 1 Graph 2
Graph 3 Graph 4
Extend graph (backward); previously seen node
A B
C A
B B
C A
A B
C A
C B
A B

Pattern-Growth:
An Example
new frequent graphs
Graph 1 Graph 2
Graph 3 Graph 4
Stop recursion, try different start vertex…
B B
C A
C B
A B
A B
C A
A B
C A

Pitfall: PatternGrowthGraph
Simple, but not efﬁcient
Same graph can be discovered many times; duplicate graph
Generation and detection of duplicate graphs increases workload

gSpan (Graph-Based Substructure Pattern Mining)
Designed to reduce the generation of duplicate graphs.
Explores via depth-first search (DFS)
DFS lexicographic order and minimum DFS code form a canonical labeling
system to support DFS search.
Discovers all the frequent subgraphs without candidate generation and false
positives pruning. It combines the growing and checking of frequent
subgraphs into one procedure, thus accelerates the mining process.

DFS Subscripting
When performing a DFS in a graph, we construct a DFS tree
One graph can have several different DFS trees
Depth-first discovery of the vertices forms a linear order
Use subscripts to label this order according to their discovery time
i < j means vi is discovered before vj.
vo, the root and vn, the rightmost vertex.
The straight path from v0 to vn, rightmost path.

DFS Code
We transform each subscripted graph to an edge sequence, called a DFS
code, so that we can build an order among these sequences.
The goal is to select the subscripting that generates the minimum sequence
as its base subscripting.
There are two kinds of orders in this transformation process:
1. Edge order, which maps edges in a subscripted graph into a sequence; and
2. Sequence order, which builds an order among edge sequences
An edge is represented by a 5-tuple, (𝑖, 𝑗, 𝑙𝑖, 𝐼(𝑖,𝑗), 𝑙𝑗); 𝑙𝑖 and 𝑙𝑗 are the labels
of 𝑣𝑖 and 𝑣𝑗, respectively, and 𝐼(𝑖,𝑗) is the label of the edge connecting them

DFS Lexicographic Order
For the each DFS tree, we sort the DFS code (tuples) to a set of orderings.
Based on the DFS lexicographic ordering, the minimum DFS code of a given
graph G, written as dfs(G), is the minimal one among all the DFS codes.
The subscripting that generates the minimum DFS code is called the base
subscripting.
Given two graphs 𝐺 and 𝐺’, 𝐺 is isomorphic to 𝐺’ if and only if 𝑑𝑓𝑠(𝐺) = 𝑑𝑓𝑠(𝐺’).
Based on this property, what we need to do for mining frequent subgraphs is to perform only the
right-most extensions on the minimum DFS codes, since such an extension will guarantee the
completeness of mining results.

DFS Code: An
Example
DFS Subscripting
When performing a DFS in a
graph, we construct a DFS tree
One graph can have several
different DFS trees
X
X
Z Y
a
a
b
b
v0 v1 v2 v3
X
X
Z Y
a
a
b
b
X
X
Z Y
a
a
b
b
X
X
Z Y
a
a
b
b

DFS
Lexicographic
Order: An
Example
For the each DFS tree, we sort
the DFS code (tuples) to a set of
orderings.
Based on the DFS lexicographic
ordering, the minimum DFS
code of a given graph G,
written as dfs(G), is the minimal
one among all the DFS codes.
X
X
Z Y
a
a
b
b
X
X
Z Y
a
a
b
b
X
X
Z Y
a
a
b
b
edge γ0
e0 (0, 1, X, a, X) ●
e1 (1, 2, X, a, Z) ●
e2 (2, 0, Z, b, X)
e3 (1, 3, X, b, Y)
edge γ1
e0 (0, 1, X, a, X) ●
e1 (1, 2, X, b, Y)
e2 (1, 3, X, a, Z)
e3 (3, 0, Z, b, X)
edge γ2
e0 (0, 1, Y, b, X)
e1 (1, 2, X, a, X)
e2 (2, 3, X, b, Z)
e3 (3, 1, Z, a, X)

1. Initially, a starting vertex is randomly chosen
2. Vertices in a graph are marked so that we can tell which vertices have been
visited
3. Visited vertex set is expanded repeatedly until a full DFS tree is built
4. Given a graph G and a DFS tree T in G, a new edge e
Can be added between the right-most vertex and another vertex on the
right-most path (backward extension); or
Can introduce a new vertex and connect to a vertex on the right-most
path (forward extension).
Because both kinds of extensions take place on the right-most path, we call them right-
most extension, denoted by 𝑔  𝑟 𝑒

Algorithm: gSpan
Pattern growth-based frequent substructure mining that reduces duplicate graph generation.
Input:
𝑠, a DFS code
𝑚𝑖𝑛_𝑠𝑢𝑝, minimum support
threshold
Output:
𝑆, frequent graph set
Method:
𝑆 ← ∅
Call 𝑔𝑆𝑝𝑎𝑛(𝑠, 𝐷, 𝑚𝑖𝑛_𝑠𝑢𝑝, 𝑆)
procedure gSpan(s, D, min_sup, S)
1 if s ≠ dfs(s) then return;
2 insert s into S;
3 set C to ∅;
4 scan D once, ﬁnd all edges e that s can be right-most extended to s  𝑟e;
5 insert s  𝑟e into C and count its frequency;
6 foreach frequent s  𝑟e in C do
7 gSpan(s  𝑟e, D, min_sup, S);
8 return;

Other Graph Mining
So far the techniques we have discussed:
Can handle only one special kind of graphs:
Labeled, undirected, connected simple graphs without any speciﬁc constraints
Assume that the database to be mined contains a set of graphs
Each consisting of a set of labeled vertices and labeled but undirected edges, with no other
constraints.

Other Graph Mining
Mining Variant and Constrained Substructure Patterns
Closed frequent substructure
where a frequent graph G is closed if and only if there is no proper supergraph G0 that has
the same support as G
Maximal frequent substructure
where a frequent pattern G is maximal if and only if there is no frequent super-pattern of G.
Constraint-based substructure mining
Element, set, or subgraph containment constraint
Geometric constraint
Value-sum constraint

Application: Classification
We mine frequent graph patterns in the training set.
The features that are frequent in one class but rather infrequent in the other
class(es) should be considered as highly discriminative features; used for
model construction.
To achieve high-quality classiﬁcation,
We can adjust: the thresholds on frequency, discriminativeness, and graph connectivity
Based on: the data, the number and quality of the features generated, and the classiﬁcation accuracy.

Application: Cluster analysis
We mine frequent graph patterns in the training set.
The set of graphs that share a large set of similar graph patterns should be
considered as highly similar and should be grouped into similar clusters.
The minimal support threshold can be used as a way to adjust the number of
frequent clusters or generate hierarchical clusters.

Examples of Social Networks
Twitter network
http://willchernoff.com/
Email Network
https://wiki.cs.umd.edu
Air Transportation Network
www.mccormick.northwestern.edu

Social Network Analysis
Nodes often represent
an object or entity such as a person,
computer/server, power generator,
airport, etc
Links represent relationships,
e.g. ‘likes’, ‘follow’s, ‘flies to’, etc
http://www.liacs.nl/~erwin/dbdm2009/GraphMining.pdf

Why are we interested?
It turns out that the structure of
real-world graphs often have
special characteristics
This is important because
structure always affects function
e.g. the structure of a social network
affects how a rumour, or an
infectious disease, spreads
e.g. the structure of a power grid
determines how robust the network
is to power failures
Goal:
1. Identify the characteristics /
properties of graphs; structural and
dynamic / behavioural
2. Generate models of graphs that
exhibit these characteristics
3. Use these tools to make predictions
about the behaviour of graphs

Properties of Real-World Social Graphs
1. Degree Distribution
Plot the fraction of nodes with degree k (denoted pk) vs. k
Our intuition:
Poisson/Normal
Distribution
WRONG!
mathworld.wolfram.com
Correct: Highly Skewed
http://cs.stanford.edu/people/jure/talks/www08tutorial/

1. (continued)
Real-world social networks tend to have a
highly skewed distribution that follows the
Power Law: pk ~ k-a
A small percentage of nodes have very high
degree, are highly connected
Example: Spread of a virus
black squares = infected
pink = infected but not contagious
green = exposed but not infected

2. Small World Effect: for most real graphs, the number
of hops it takes to reach any node from any other node
is about 6 (Six Degrees of Separation).
Milgram did an experiment, asked people in
Nebraska to send letters to people in Boston
Constraint: letters could only be delivered to people
known on a first name basis.
Only 25% of letters made it to their target, but the
ones that did made it in 6 hops

2. (continued)
The distribution of the shortest
path lengths.
Example: MSN Messenger
Network
If we pick a random node in the
network and then count how many
hops it is from every other node, we
get this graph
Most nodes are at a distance of 7
hops away from any other node
http://cs.stanford.edu/people/jure/talks/www08tutorial/

3. Network Resilience
If a node is removed, how is the network affected?
For a real-world graphs, you must remove the highly connected nodes in order to reduce
the connectivity of the graph
Removing a node that is sparsely connected does not have a significant effect on
connectivity
Since the proportion of highly connected nodes in a real-world graph is small, the
probability of choosing and removing such a node at random is small
→ real-world graphs are resilient to random attacks!
→ conversely, targeted attacks on highly connected nodes are very effective!

4. Densification
How does the number of edges in the
graph grow as the number of nodes
grows?
Previous belief: # edges grows linearly
with # nodes i.e. 𝐸(𝑡) ~ 𝑁(𝑡)
Actually, # edges grows superlinearly with
the # nodes, i.e. the # of edges grows
faster than the number of nodes
i.e. 𝐸(𝑡) ~ 𝑁(𝑡) 𝑎
Graph gets denser over time

5. Shrinking Diameter
Diameter is taken to be the longest-shortest path in the graph
As a network grows, the diameter actually gets smaller, i.e. the distance between nodes
slowly decreases

Features/Properties of Graphs
Community structure
Densification
Shrinking diameter

Generators: How do we model graphs
Try:
Generating a random graph
Given n vertices connect each pair i.i.d. with
Probability p
Follows a Poisson distribution
Follows from our intuition
Not useful; no community structure
Does not mirror real-world graphs

Generators: How do we model graphs
(Erdos‐Renyi) Random graphs (1960s)
Exponential random graphs
Small‐world model
Preferential attachment
Edge copying model
Community guided attachment
Forest Fire
Kronecker graphs (today)

Kronecker Graphs
For kronecker graphs all the properties of
real world graphs can actually be proven
Best model we have today
Adjacency matrix, recursive generation

Kronecker Graphs
1. Construct adjacency matrix for a graph G:
𝐴 𝐺 = (𝐴𝑖𝑗) = { 1 𝑖𝑓 𝑖 𝑎𝑛𝑑 𝑗 𝑎𝑟𝑒 𝑎𝑑𝑗𝑎𝑐𝑒𝑛𝑡, 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 }
Side Note: The eigenvalue of a matrix A is the scalar value ƛ for which the
following is true: Av = ƛv (where v is an eigenvector of the matrix A)

Kronecker Graphs
2. Generate the 2nd Kronecker graph by taking the Kronecker product of the
1st graph with itself. The Kronecker product of 2 graphs is defined as:

Kronecker Graphs
Visually, this is just taking the the first matrix and replacing the entries that
were equal to 1 with the second matrix.
3 x 3 matrix 9 x 9 matrix

Kronecker Graphs
We define the Kronecker product of two graphs as the Kronecker product of
their adjacency matrices
Therefore, we can compute the Kth Kronecker graph by iteratively taking the
Kronecker product of an initial graph G1 k times:
Gk = G1 ⛒ G1 ⛒ G1 ⛒ … ⛒ G1

Applying Models to Real World Graphs
Can then predict and understand the structure

Virus Propagation
A form of diffusion; a fundamental process in social networks
Can also refer to spread of rumours, news, etc

Virus Propagation
SIS Model: Susceptible - Infected - Susceptible
Virus birth rate β = the probability that an infected node attacks a neighbour
Virus death rate ẟ = probability that an infected node becomes cured
Heals with Prob ẟ
Infects with Prob β Infects with Prob β
Healthy Node
At risk
Node
Infected NodeInfected Node

Virus Propagation
The virus strength of a graph: s = β/ẟ
The epidemic threshold 𝜏 of a graph is a value such that if:
s = β/ẟ < 𝜏 then an epidemic cannot happen.
So we can ask the question:
Will the virus become epidemic?
Will the rumours/news become viral?
How to find threshold 𝛕 ? Theorem:
𝜏 = 1/ƛ 1,A where ƛ 1,A is the largest eigenvalue of adjacency matrix of the graph
So if s < 𝜏 then there is no epidemic

Link Prediction
Given a social network at time t1, predict the edges that will be added at time t2
Assign connection score(x,y) to each pair of nodes
Usually taken to be the shortest path between the nodes x and y, other measures use # of
neighbours in common, and the Katz measure
Produce a list of scores in decreasing order
The pair at the top of the list are most likely to have a link created between them in the
future
Can also use this measure for clustering

Link Prediction
Score(x,y) = # of neighbours in
common
Top score = score(B,C) = 5G
A B
F
C
E H I J
D
Likely new link between
B and C

Viral Marketing
A customer may increase the sales of some product if they interact positively with their
peers in the social network
Assign a network value to a customer

Diffusion in Networks: Influential Nodes
Some nodes in the network can be active
they can spread their influence to other nodes
e.g. news, opinions, etc that propagate through a network of friends
2 models: Threshold model, Independent Contagion model

Data Mining Seminar - Graph Mining and Social Network Analysis

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

En vedette

En vedette (20)

Similaire à Data Mining Seminar - Graph Mining and Social Network Analysis

Similaire à Data Mining Seminar - Graph Mining and Social Network Analysis (20)

Dernier

Dernier (20)

Data Mining Seminar - Graph Mining and Social Network Analysis