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Data Mining:
Concepts and Techniques
— Chapter 9 —
9.2. Social Network Analysis
Jiawei Han and Micheline Kamber
Department of Computer Science
University of Illinois at Urbana-Champaign
www.cs.uiuc.edu/~hanj
©2006 Jiawei Han and Micheline Kamber. All rights reserved.
Acknowledgements: Based on the slides by Sangkyum Kim and Chen Chen
5/9/2017
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5/9/2017
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Social Network Analysis

Social Networks: An Introduction

Primitives for Network Analysis

Different Network Distributions

Models of Social Network Generation

Mining on Social Network

Summary
5/9/2017
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Social Networks



Social network: A social structure made of nodes (individuals
or organizations) that are related to each other by various
interdependencies like friendship, kinship, like, ...
Graphical representation
 Nodes = members
 Edges = relationships
Examples of typical social networks on the Web
 Social bookmarking (Del.icio.us)
 Friendship networks (Facebook, Myspace, LinkedIn)
 Blogosphere
 Media Sharing (Flickr, Youtube)
 Folksonomies
Web 2.0 Examples






Blogs
 Blogspot
 Wordpress
Wikis
 Wikipedia
 Wikiversity
Social Networking Sites
 Facebook
 Myspace
 Orkut
Digital media sharing websites
 Youtube
 Flickr
Social Tagging
 Del.icio.us
Others
 Twitter
 Yelp
Adapted from H. Liu & N. Agarwal, KDD’08 tutorial
Society
Nodes: individuals
Links: social relationship
(family/work/friendship/etc.)
S. Milgram (1967)
John Guare
Six Degrees of Separation
Social networks: Many individuals with diverse social
interactions between them
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Communication Networks
The Earth is developing an electronic nervous system, a
network with diverse nodes and links are
-computers
-phone lines
-routers
-TV cables
-satellites
-EM waves
Communication
networks: Many
non-identical
components with
diverse
connections
between them
8
Complex systems
Made of many non-identical
elements connected by diverse
interactions.
NETWORK
9
“Natural” Networks and Universality

Consider many kinds of networks:



social, technological, business, economic, content,…
These networks tend to share certain informal properties:

large scale; continual growth

distributed, organic growth: vertices “decide” who to link to

interaction restricted to links

mixture of local and long-distance connections

abstract notions of distance: geographical, content, social,…
Social network theory and link analysis
 Do natural networks share more quantitative universals?

What would these “universals” be?

How can we make them precise and measure them?

How can we explain their universality?
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Social Network Analysis

Social Networks: An Introduction

Primitives for Network Analysis

Different Network Distributions

Models of Social Network Generation

Mining on Social Network

Summary
11
Networks and Their Representations

A network (or a graph): G = (V, E), where V: vertices (or nodes), and
E: edges (or links)




Adjacency matrix:
Aij = 1 if there is an edge between vertices i and j; 0 otherwise
Weighted networks:


Self-edge (self-loop): if an edge connects vertex to itself
Simple network/graph if a network has neither self-edges nor multiedges


Multi-edge: if more than one edge between the same pair of
vertices
Edges having weight (strength), usually a real number
Directed network (directed graph): if each edge has a direction

Aij = 1 if there is an edge from i to j; 0 otherwise
Cocitation and Bibliographic Coupling

Cocitation of vertices i and j: # of vertices having
outgoing edges pointing to both i and j


Cocitation of i and j:
Cociation matrix: It is a symmetric matrix
Diagonal matrix (Cii): total # papers citing i
Bibliographic coupling of vertices i and j: # of other
vertices to which both point
Vertices i and j are
co-cited by 3 papers
i





Bibliographic coupling of i and j:
Cociation matrix:
Diagonal matrix (Bii): total # papers cited by i
Vertices i and j cite
3 same papers
Cocitation & Bibliographic Coupling: Comparison




Two measures are affected by the number of incoming and outgoing
edges that vertices have
For strong cocitation: must have a lot of incoming edges

Must be well-cited (influential) papers, surveys, or books

Takes time to accumulate citations
Strong bib-coupling if two papers have similar citations

A more uniform indicator of similarity between papers

Can be computed as soon as a paper is published

Not change over time
Recent analysis algorithms

HITS explores both cocitation and bibliographic coupling
Bipartite Networks


Bipartite Network: two kinds of vertices, and
edges linking only vertices of unlike types
Incidence matrix:

Bij = 1 if vertex j links to group i
0 otherwise


One can create a one-mode project from the
two-mode partite form (but with info loss)
The projection to one-mode can be written in
terms of the incidence matrix B is follows
Tom
SIGMOD
Mary
VLDB
Alice
EDBT
Bob
KDD
Cindy
ICDM
Tracy
SDM
Jack
AAAI
ICML
Mike
Lucy
Jim
Degree and Network Density

Degree of a vertex i:
# of edges m = 1/2 of sum of degrees of all the vertices:

The mean degree c of a vertex in an undirected graph:

Density ρ of a graph:
A network is dense if density ρ tends to be a constant as n → ∞
A network is sparse if density ρ → 0 as n → ∞. The fraction of
nonzero element in the adjacency matrix tends to zero
Internet, WWW and friendship networks are usually regarded as
sparse




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Social Network Analysis

Social Networks: An Introduction

Primitives for Network Analysis

Different Network Distributions

Models of Social Network Generation

Mining on Social Network

Summary
5/9/2017
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Some Interesting Network Quantities




Connected components:
 how many, and how large?
Network diameter:
 maximum (worst-case) or average?
 exclude infinite distances? (disconnected components)
 the small-world phenomenon
Clustering:
 to what extent that links tend to cluster “locally”?
 what is the balance between local and long-distance
connections?
 what roles do the two types of links play?
Degree distribution:
 what is the typical degree in the network?
 what is the overall distribution?
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A “Canonical” Natural Network has…




Few connected components:
 often only 1 or a small number, indep. of network size
Small diameter:
 often a constant independent of network size (like 6)
 or perhaps growing only logarithmically with network size
or even shrink?
 typically exclude infinite distances
A high degree of clustering:
 considerably more so than for a random network
 in tension with small diameter
A heavy-tailed degree distribution:
 a small but reliable number of high-degree vertices
 often of power law form
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Probabilistic Models of Networks






All of the network generation models we will study are
probabilistic or statistical in nature
They can generate networks of any size
They often have various parameters that can be set:
 size of network generated
 average degree of a vertex
 fraction of long-distance connections
The models generate a distribution over networks
Statements are always statistical in nature:
 with high probability, diameter is small
 on average, degree distribution has heavy tail
Thus, we’re going to need some basic statistics and
probability theory
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Probability and Random Variables


A random variable X is simply a variable that probabilistically assumes values in
some set
 set of possible values sometimes called the sample space S of X
 sample space may be small and simple or large and complex
 S = {Heads, Tails}, X is outcome of a coin flip
 S = {0,1,…,U.S. population size}, X is number voting democratic
 S = all networks of size N, X is generated by preferential attachment
Behavior of X determined by its distribution (or density)
 for each value x in S, specify Pr[X = x]
 these probabilities sum to exactly 1 (mutually exclusive outcomes)
 complex sample spaces (such as large networks):
 distribution often defined implicitly by simpler components
 might specify the probability that each edge appears independently
 this induces a probability distribution over networks
 may be difficult to compute induced distribution
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Some Basic Notions and Laws




Independence:
 let X and Y be random variables
 independence: for any x and y, Pr[X = x & Y = y] = Pr[X=x]Pr[Y=y]
 intuition: value of X does not influence value of Y, vice-versa
 dependence: e.g. X, Y coin flips, but Y is always opposite of X
Expected (mean) value of X:
 only makes sense for numeric random variables
 “average” value of X according to its distribution
 formally, E[X] = S (Pr[X = x] X), sum is over all x in S
 often denoted by m
 always true: E[X + Y] = E[X] + E[Y]
 true only for independent random variables: E[XY] = E[X]E[Y]
Variance of X:
 Var(X) = E[(X – m)^2]; often denoted by s^2
 standard deviation is sqrt(Var(X)) = s
Union bound:
 for any X, Y, Pr[X=x & Y=y] <= Pr[X=x] + Pr[Y=y]
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Convergence to Expectations


Let X1, X2,…, Xn be:

independent random variables

with the same distribution Pr[X=x]

expectation m = E[X] and variance s2

independent and identically distributed (i.i.d.)

essentially n repeated “trials” of the same experiment

natural to examine r.v. Z = (1/n) S Xi, where sum is over i=1,…,n

example: number of heads in a sequence of coin flips

example: degree of a vertex in the random graph model

E[Z] = E[X]; what can we say about the distribution of Z?
Central Limit Theorem:

as n becomes large, Z becomes normally distributed

with expectation m and variance s2/n
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The Normal Distribution







The normal or Gaussian density:
applies to continuous, real-valued random
variables
characterized by mean m and standard
deviation s
density at x is defined as
2
2
 (1/(s sqrt(2p))) exp(-(x-m) /2s )
2
 special case m = 0, s = 1: a exp(-x /b) for
some constants a,b > 0
peaks at x = m, then dies off exponentially
rapidly
the classic “bell-shaped curve”
 exam scores, human body temperature,
remarks:
 can control mean and standard deviation
independently
 can make as “broad” as we like, but always
have finite variance
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The Binomial Distribution

Coin with Pr[heads] = p, flip n
times, probability of getting exactly
k heads:


choose (n, k) = pk(1-p)n-k
For large n and p fixed:

approximated well by a normal
with
m = np, s = sqrt(np(1-p))


s/m  0 as n grows
leads to strong large deviation
www.professionalgambler.com/
binomial.html
bounds
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The Poisson Distribution

Like binomial, applies to variables taken on
integer values > 0

Often used to model counts of events



number of phone calls placed in a given
time period

number of times a neuron fires in a given
time period
Single free parameter l, probability of exactly
x events:

exp(-l) lx/x!

mean and variance are both l
single photoelectron distribution
Binomial distribution with n large, p = l/n (l
fixed)

converges to Poisson with mean l
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Power Law (or Pareto) Distributions




Heavy-tailed, pareto, or power law
distributions:
 For variables assuming integer values > 0
a
 probability of value x ~ 1/x
 Typically 0 < a < 2; smaller a gives
heavier tail
 sometimes also referred to as being
scale-free
For binomial, normal, and Poisson
distributions the tail probabilities approach
0 exponentially fast
What kind of phenomena does this
distribution model?
What kind of process would generate it?
27
Distinguishing Distributions in Data






All these distributions are idealized models
In practice, we do not see distributions, but data
Typical procedure to distinguish between Poisson, power law, …
 might restrict our attention to a range of values of interest
 accumulate counts of observed data into equal-sized bins
 look at counts on a log-log plot
power law:
a
 log(Pr[X = x]) = log(1/x ) = -a log(x)
 linear, slope –a
Normal:
2
2
 log(Pr[X = x]) = log(a exp(-x /b)) = log(a) – x /b
 non-linear, concave near mean
Poisson:
x
 log(Pr[X = x]) = log(exp(-l) l /x!)
 also non-linear
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Zipf’s Law




Pareto distribution vs. Zipf’s Law
 Pareto distributions are continuous probability distributions
 Zipf's law: a discrete counterpart of the Pareto distribution
Zipf's law:
 Given some corpus of natural language utterances, the frequency of any
word is inversely proportional to its rank in the frequency table
 Thus the most frequent word will occur approximately twice as often as
the second most frequent word, which occurs twice as often as the
fourth most frequent word, etc.
General theme:
 rank events by their frequency of occurrence
 resulting distribution often is a power law!
Other examples:
 North America city sizes
 personal income
 file sizes
 genus sizes (number of species)
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Zipf Distribution
The same data plotted on linear and logarithmic scales.
Both plots show a Zipf distribution with 300 data points
Linear scales on both axes
5/9/2017
Logarithmic scales on both axes
30
Social Network Analysis

Social Networks: An Introduction

Primitives for Network Analysis

Different Network Distributions

Models of Social Network Generation

Mining on Social Network

Summary
31
Some Models of Network Generation





Random graphs (Erdös-Rényi models):
 gives few components and small diameter
 does not give high clustering and heavy-tailed degree distributions
 is the mathematically most well-studied and understood model
Watts-Strogatz models:
 give few components, small diameter and high clustering
 does not give heavy-tailed degree distributions
Scale-free Networks:
 gives few components, small diameter and heavy-tailed distribution
 does not give high clustering
Hierarchical networks:
 few components, small diameter, high clustering, heavy-tailed
Affiliation networks:
 models group-actor formation
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Models of Social Network Generation

Random Graphs (Erdös-Rényi models)

Watts-Strogatz models

Scale-free Networks
5/9/2017
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Basic Network Measures: Degrees
and Clustering Coefficients

Let a network G = (V, E), degree of a vertex

Undirected network: d(vi):

Directed network


In-degree of a vertex din(vi):

Out-degree of a vertex dout(vi):
Clustering coefficients

Let Nv be the set of adjacent vertices of v, kv be the number of adjacent
vertices to node v

Local clustering coefficient for directed network

Local clustering coefficient for undirected network

For the whole network: Averaging the local clustering coefficient of all
the vertices (Watts & Strogatz):
34
The Erdös-Rényi (ER) Model:
A Random Graph Model

A random graph is obtained by starting with a set of N vertices and adding
edges between them at random

Different random graph models produce different probability distributions
on graphs

Most commonly studied is the Erdős–Rényi model, denoted G(N,p), in which
every possible edge occurs independently with probability p

Random graphs were first defined by Paul Erdős and Alfréd Rényi in their
1959 paper "On Random Graphs”

The usual regime of interest is when p ~ 1/N, N is large

e.g., p = 1/2N, p = 1/N, p = 2/N, p=10/N, p = log(N)/N, etc.

in expectation, each vertex will have a “small” number of neighbors

will then examine what happens when N  infinity

can thus study properties of large networks with bounded degree

Sharply concentrated; not heavy-tailed
35
Erdös-Rényi Model (1959)
Connect with
probability p
Pál Erdös
p=1/6
N=10
k~1.5
Poisson distribution
(1913-1996)
- Democratic
- Random
36
The a-model





The a-model has the following parameters or “knobs”:

N: size of the network to be generated

k: the average degree of a vertex in the network to be generated

p: the default probability two vertices are connected

a: adjustable parameter dictating bias towards local connections
For any vertices u and v:
 define m(u,v) to be the number of common neighbors (so far)
Key quantity: the propensity R(u,v) of u to connect to v
 if m(u,v) >= k, R(u,v) = 1 (share too many friends not to connect)
 if m(u,v) = 0, R(u,v) = p (no mutual friends  no bias to connect)
 else, R(u,v) = p + (m(u,v)/k)^a (1-p)
Generate new edges incrementally
 using R(u,v) as the edge probability; details omitted
Note: a = infinity is “like” Erdos-Renyi (but not exactly)
37
The Watts and Strogatz Model

Proposed by Duncan J. Watts and Steven Strogatz in their joint 1998 Nature
paper

A random graph generation model that produces graphs with small-world
properties, including short average path lengths and high clustering

The model also became known as the (Watts) beta model after Watts used
β to formulate it in his popular science book Six Degrees

The ER graphs fail to explain two important properties observed in realworld networks:

By assuming a constant and independent probability of two nodes
being connected, they do not account for local clustering, i.e., having a
low clustering coefficient

Do not account for the formation of hubs. Formally, the degree
distribution of ER graphs converges to a Poisson distribution, rather
than a power law observed in most real-world, scale-free networks
Watts-Strogatz Model
C(p) : clustering coeff.
L(p) : average path length
(Watts and Strogatz, Nature 393, 440 (1998))
39
Small Worlds and Occam’s Razor




For small a, should generate large clustering coefficients

we “programmed” the model to do so

Watts claims that proving precise statements is hard…
But we do not want a new model for every little property

Erdos-Renyi  small diameter

a-model  high clustering coefficient
In the interests of Occam’s Razor, we would like to find

a single, simple model of network generation…

… that simultaneously captures many properties
Watt’s small world: small diameter and high clustering
40
Discovered by Examining the Real World…



Watts examines three real networks as case studies:
 the Kevin Bacon graph
 the Western states power grid
 the C. elegans nervous system
For each of these networks, he:
 computes its size, diameter, and clustering coefficient
 compares diameter and clustering to best Erdos-Renyi approx.
 shows that the best a-model approximation is better
 important to be “fair” to each model by finding best fit
Overall,
 if we care only about diameter and clustering, a is better than
p
41
Case 1: Kevin Bacon Graph


Vertices: actors and actresses
Edge between u and v if they appeared in a film together
Kevin Bacon
No. of movies : 46
No. of actors : 1811
Average separation: 2.79
Is Kevin Bacon
the most
connected actor?
NO!
5/9/2017
Rod Steiger
Donald Pleasence
Martin Sheen
Christopher Lee
Robert Mitchum
Charlton Heston
Eddie Albert
Robert Vaughn
Donald Sutherland
John Gielgud
Anthony Quinn
James Earl Jones
Average
distance
2.537527
2.542376
2.551210
2.552497
2.557181
2.566284
2.567036
2.570193
2.577880
2.578980
2.579750
2.584440
# of
movies
112
180
136
201
136
104
112
126
107
122
146
112
# of
links
2562
2874
3501
2993
2905
2552
3333
2761
2865
2942
2978
3787
KevinBacon
Bacon
Kevin
2.786981
2.786981
46
46
1811
1811
Rank
Name
1
2
3
4
5
6
7
8
9
10
11
12
…
876
876
…
42
Bacon
-map
#1 Rod Steiger
#876
Kevin Bacon
Donald
#2
Pleasence
#3 Martin Sheen
43
Case 2: New York State Power Grid



Vertices: generators and substations
Edges: high-voltage power transmission lines and transformers
Line thickness and color indicate the voltage level
 Red 765 kV, 500 kV; brown 345 kV; green 230 kV; grey 138 kV
44
Case 3: C. Elegans Nervous System


Vertices: neurons in the C. elegans worm
Edges: axons/synapses between neurons
45
Two More Examples


M. Newman on scientific collaboration networks
 coauthorship networks in several distinct communities
 differences in degrees (papers per author)
 empirical verification of
 giant components
 small diameter (mean distance)
 high clustering coefficient
Alberich et al. on the Marvel Universe
 purely fictional social network
 two characters linked if they appeared together in an issue
 “empirical” verification of
 heavy-tailed distribution of degrees (issues and characters)
 giant component
 rather small clustering coefficient
46
Towards Scale-Free Networks


Major limitation of the Watts-Strogatz model

It produces graphs that are homogeneous in degree

In contrast, real networks are often scale-free networks
inhomogeneous in degree, having hubs and a scale-free
degree distribution. Such networks are better described by
the preferential attachment family of models, such as the
Barabási–Albert (BA) model

The Watts-Strogatz model also implies a fixed number of
nodes and thus cannot be used to model network growth
The leads to the proposal of a new model: scale-free network,
a network whose degree distribution follows a power law, at
least asymptotically
47
Scale-free Networks: World Wide Web
Nodes: WWW documents
Links: URL links
800 million documents
(S. Lawrence, 1999)
ROBOT: collects all URL’s
found in a document and
follows them recursively
R. Albert, H. Jeong, A-L Barabasi, Nature, 401 130 (1999)
48
World Wide Web
Expected Result
Real Result
out= 2.45
 in = 2.1
k ~ 6
P(k=500) ~
10-99
NWWW ~ 109
 N(k=500)~10-90
Pout(k) ~
k-out
P(k=500) ~
10-6
Pin(k) ~ k- in
NWWW ~ 109
 N(k=500) ~ 103
J. Kleinberg, et. al, Proceedings of the ICCC (1999)
49
World Wide Web
3
l15=2 [125]
6
1
l17=4 [1346  7]
4
5
2
7
… < l > = ??
Finite size scaling: create a network with N nodes with Pin(k) and Pout(k)
< l > = 0.35 + 2.06 log(N)
19 degrees of separation
R. Albert et al, Nature (99)
<l>
nd.edu
based on 800 million webpages
[S. Lawrence et al Nature (99)]
IBM
A. Broder et al WWW9 (00)
50
What Does that Mean?
Poisson distribution
Exponential Network
Power-law distribution
Scale-free Network
51
Scale-Free Networks

The number of nodes (N) is not fixed


Networks continuously expand by additional new nodes

WWW: addition of new nodes

Citation: publication of new papers
The attachment is not uniform

A node is linked with higher probability to a node that
already has a large number of links


WWW: new documents link to well known sites (CNN,
Yahoo, Google)
Citation: Well cited papers are more likely to be cited
again
52
Scale-Free Networks







Start with (say) two vertices connected by an edge
For i = 3 to N:
 for each 1 <= j < i, d(j) = degree of vertex j so far
 let Z = S d(j) (sum of all degrees so far)
 add new vertex i with k edges back to {1, …, i-1}:
 i is connected back to j with probability d(j)/Z
Vertices j with high degree are likely to get more links! —“Rich get richer”
Natural model for many processes:
 hyperlinks on the web
 new business and social contacts
 transportation networks
Generates a power law distribution of degrees
 exponent depends on value of k
Preferential attachment explains
 heavy-tailed degree distributions
 small diameter (~log(N), via “hubs”)
Will not generate high clustering coefficient
 no bias towards local connectivity, but towards hubs
53
Case1: Internet Backbone
Nodes: computers, routers
Links: physical lines
(Faloutsos, Faloutsos and Faloutsos, 1999)
54
Internet-Map
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55
Case2: Actor Connectivity
Days of Thunder (1990)
Far and Away
(1992)
Eyes Wide Shut (1999)
Nodes: actors
Links: cast jointly
N = 212,250 actors
k = 28.78
P(k) ~k-
=2.3
56
Case 3: Science Citation Index
25
Nodes: papers
Links: citations
Witten-Sander
PRL 1981
1736 PRL papers (1988)
2212
P(k) ~k-
( = 3)
(S. Redner, 1998)
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57
Case 4: Science Coauthorship
Nodes: scientist (authors)
Links: write paper together
(Newman, 2000, H. Jeong et al 2001)
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58
Case 5: Food Web
Nodes: trophic species
Links: trophic interactions
R. Sole (cond-mat/0011195)
R.J. Williams, N.D. Martinez Nature (2000)
59
Robustness of
Random vs. Scale-Free Networks
5/9/2017

The accidental failure
of a number of nodes
in a random network
can fracture the
system into noncommunicating islands.

Scale-free networks
are more robust in the
face of such failures

Scale-free networks
are highly vulnerable
to a coordinated
attack against their
hubs
60
Bio-Map
GENOME
protein-gene
interactions
PROTEOME
protein-protein
interactions
METABOLISM
Bio-chemical
reactions
Citrate Cycle
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61
Boehring-Mennheim
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62
Prot Interaction Map: Yeast Protein
Network
Nodes: proteins
Links: physical interactions (binding)
P. Uetz, et al., Nature 403, 623-7 (2000)
63
Social Network Analysis

Social Networks: An Introduction

Primitives for Network Analysis

Different Network Distributions

Models of Social Network Generation

Mining on Social Network

Summary
64
Information on Social Network


Heterogeneous, multi-relational data represented as a graph
or network
 Nodes are objects
 May have different kinds of objects
 Objects have attributes
 Objects may have labels or classes
 Edges are links
 May have different kinds of links
 Links may have attributes
 Links may be directed, are not required to be binary
Links represent relationships and interactions between objects
- rich content for mining
65
Metrics (Measures) in Social Network Analysis (I)

Betweenness: The extent to which a node lies between other nodes in the
network. This measure takes into account the connectivity of the node's
neighbors, giving a higher value for nodes which bridge clusters. The
measure reflects the number of people who a person is connecting
indirectly through their direct links

Bridge: An edge is a bridge if deleting it would cause its endpoints to lie in
different components of a graph.

Centrality: This measure gives a rough indication of the social power of a
node based on how well they "connect" the network. "Betweenness",
"Closeness", and "Degree" are all measures of centrality.

Centralization: The difference between the number of links for each node
divided by maximum possible sum of differences. A centralized network
will have many of its links dispersed around one or a few nodes, while a
decentralized network is one in which there is little variation between the
number of links each node possesses.
66
Metrics (Measures) in Social Network Analysis (II)

Closeness: The degree an individual is near all other individuals in a
network (directly or indirectly). It reflects the ability to access information
through the "grapevine" of network members. Thus, closeness is the
inverse of the sum of the shortest distances between each individual and
every other person in the network

Clustering coefficient: A measure of the likelihood that two associates of a
node are associates themselves. A higher clustering coefficient indicates a
greater 'cliquishness'.

Cohesion: The degree to which actors are connected directly to each other
by cohesive bonds. Groups are identified as ‘cliques’ if every individual is
directly tied to every other individual, ‘social circles’ if there is less
stringency of direct contact, which is imprecise, or as structurally cohesive
blocks if precision is wanted.

Degree (or geodesic distance): The count of the number of ties to other
actors in the network.
67
Metrics (Measures) in Social Network Analysis (III)

(Individual-level) Density: The degree a respondent's ties know one another/
proportion of ties among an individual's nominees. Network or global-level
density is the proportion of ties in a network relative to the total number
possible (sparse versus dense networks).

Flow betweenness centrality: The degree that a node contributes to sum of
maximum flow between all pairs of nodes (not that node).

Eigenvector centrality: A measure of the importance of a node in a network. It
assigns relative scores to all nodes in the network based on the principle that
connections to nodes having a high score contribute more to the score of the
node in question.

Local Bridge: An edge is a local bridge if its endpoints share no common
neighbors. Unlike a bridge, a local bridge is contained in a cycle.

Path Length: The distances between pairs of nodes in the network. Average
path-length is the average of these distances between all pairs of nodes.
68
Metrics (Measures) in Social Network Analysis (IV)

Prestige: In a directed graph prestige is the term used to describe a node's
centrality. "Degree Prestige", "Proximity Prestige", and "Status Prestige" are all
measures of Prestige.

Radiality Degree: an individual’s network reaches out into the network and
provides novel information and influence.

Reach: The degree any member of a network can reach other members of the
network.

Structural cohesion: The minimum number of members who, if removed from
a group, would disconnect the group

Structural equivalence: Refers to the extent to which nodes have a common
set of linkages to other nodes in the system. The nodes don’t need to have any
ties to each other to be structurally equivalent.

Structural hole: Static holes that can be strategically filled by connecting one or
more links to link together other points. Linked to ideas of social capital: if you
link to two people who are not linked you can control their communication
69
A Taxonomy of Common Link Mining Tasks



Object-Related Tasks
 Link-based object ranking
 Link-based object classification
 Object clustering (group detection)
 Object identification (entity resolution)
Link-Related Tasks
 Link prediction
Graph-Related Tasks
 Subgraph discovery
 Graph classification
 Generative model for graphs
70
Link-Based Object Ranking (LBR)




Exploit the link structure of a graph to order or prioritize the set
of objects within the graph
 Focused on graphs with single object type and single link type
A primary focus of link analysis community
Web information analysis
 PageRank and Hits are typical LBR approaches
In social network analysis (SNA), LBR is a core analysis task
 Objective: rank individuals in terms of “centrality”
 Degree centrality vs. eigen vector/power centrality
 Rank objects relative to one or more relevant objects in the
graph vs. ranks object over time in dynamic graphs
71
PageRank: Capturing Page Popularity (Brin & Page’98)



Intuitions
 Links are like citations in literature
 A page that is cited often can be expected to be more
useful in general
PageRank is essentially “citation counting”, but improves over
simple counting
 Consider “indirect citations” (being cited by a highly cited
paper counts a lot…)
 Smoothing of citations (every page is assumed to have a
non-zero citation count)
PageRank can also be interpreted as random surfing (thus
capturing popularity)
72
The PageRank Algorithm (Brin & Page’98)
Random surfing model:
At any page,
With prob. a, randomly jumping to a page
With prob. (1 – a), randomly picking a link to follow
d1
d3
d2
0
1
M 
0

1/ 2
0
0
1
1/ 2
1/ 2 1/ 2 
0
0 
0
0 

0
0 
pt 1 (di )  (1  a )
d4
d j IN ( di )
p(di )   [
k

m ji pt (d j )  a 
k
Same as
a/N (why?)
1
pt (d k )
N
1
a  (1  a )mki ] p (d k )
N
p  (a I  (1  a ) M )T p
Initial value p(d)=1/N
“Transition matrix”
Iij = 1/N
Stationary (“stable”)
distribution, so we
ignore time
Iterate until converge
Essentially an eigenvector problem….
73
HITS: Capturing Authorities & Hubs (Kleinberg’98)


Intuitions

Pages that are widely cited are good authorities

Pages that cite many other pages are good hubs
The key idea of HITS

Good authorities are cited by good hubs

Good hubs point to good authorities

Iterative reinforcement …
74
The HITS Algorithm (Kleinberg 98)
d1
d3
d2
d4
0
1
A
0

1
h( d i ) 
a(di ) 
0
0
1
1
1
0
0
0

1
0 
0

0
d j OUT ( di )

d j IN ( di )
h  Aa ;
“Adjacency matrix”
Initial values: a=h=1
a(d j )
h( d j )
a  AT h
h  AAT h ; a  AT Aa
Iterate
Normalize:
 a(di )   h(di )  1
2
i
2
i
Again eigenvector problems…
5/9/2017
75
Block-level Link Analysis (Cai et al. 04)





Most of the existing link analysis algorithms, e.g.
PageRank and HITS, treat a web page as a single node
in the web graph
However, in most cases, a web page contains multiple
semantics and hence it might not be considered as an
atomic and homogeneous node
Web page is partitioned into blocks using the visionbased page segmentation algorithm
extract page-to-block, block-to-page relationships
Block-level PageRank and Block-level HITS
76
Link-Based Object Classification (LBC)

Predicting the category of an object based on its attributes, its
links and the attributes of linked objects

Web: Predict the category of a web page, based on words that
occur on the page, links between pages, anchor text, html tags,
etc.

Citation: Predict the topic of a paper, based on word
occurrence, citations, co-citations

Epidemics: Predict disease type based on characteristics of the
patients infected by the disease

Communication: Predict whether a communication contact is
by email, phone call or mail
77
Challenges in Link-Based Classification

Labels of related objects tend to be correlated

Collective classification: Explore such correlations and jointly
infer the categorical values associated with the objects in the
graph

Ex: Classify related news items in Reuter data sets (Chak’98)


Simply incorp. words from neighboring documents: not
helpful
Multi-relational classification is another solution for link-based
classification
78
Group Detection


Cluster the nodes in the graph into groups that share
common characteristics
 Web: identifying communities
 Citation: identifying research communities
Methods
 Hierarchical clustering
 Blockmodeling of SNA
 Spectral graph partitioning
 Stochastic blockmodeling
 Multi-relational clustering
79
Entity Resolution



Predicting when two objects are the same, based on their
attributes and their links
Also known as: deduplication, reference reconciliation, coreference resolution, object consolidation
Applications
 Web: predict when two sites are mirrors of each other
 Citation: predicting when two citations are referring to the
same paper
 Epidemics: predicting when two disease strains are the
same
 Biology: learning when two names refer to the same
protein
80
Entity Resolution Methods



Earlier viewed as pair-wise resolution problem: resolved based
on the similarity of their attributes
Importance at considering links
 Coauthor links in bib data, hierarchical links between spatial
references, co-occurrence links between name references
in documents
Use of links in resolution
 Collective entity resolution: one resolution decision affects
another if they are linked
 Propagating evidence over links in a depen. graph
 Probabilistic models interact with different entity
recognition decisions
81
Link Prediction



Predict whether a link exists between two entities, based on
attributes and other observed links
Applications
 Web: predict if there will be a link between two pages
 Citation: predicting if a paper will cite another paper
 Epidemics: predicting who a patient’s contacts are
Methods
 Often viewed as a binary classification problem
 Local conditional probability model, based on structural and
attribute features
 Difficulty: sparseness of existing links
 Collective prediction, e.g., Markov random field model
82
Link Cardinality Estimation


Predicting the number of links to an object
 Web: predict the authority of a page based on the number
of in-links; identifying hubs based on the number of outlinks
 Citation: predicting the impact of a paper based on the
number of citations
 Epidemics: predicting the number of people that will be
infected based on the infectiousness of a disease
Predicting the number of objects reached along a path from an
object
 Web: predicting number of pages retrieved by crawling a
site
 Citation: predicting the number of citations of a particular
author in a specific journal
83
Subgraph Discovery




Find characteristic subgraphs
 Focus of graph-based data mining
Applications
 Biology: protein structure discovery
 Communications: legitimate vs. illegitimate groups
 Chemistry: chemical substructure discovery
Methods
 Subgraph pattern mining
Graph classification
 Classification based on subgraph pattern analysis
84
Metadata Mining




Schema mapping, schema discovery, schema
reformulation
cite – matching between two bibliographic sources
web - discovering schema from unstructured or semistructured data
bio – mapping between two medical ontologies
85
Link Mining Challenges







Logical vs. statistical dependencies
Feature construction: Aggregation vs. selection
Instances vs. classes
Collective classification and collective consolidation
Effective use of labeled & unlabeled data
Link prediction
Closed vs. open world
Challenges common to any link-based statistical model (Bayesian Logic
Programs, Conditional Random Fields, Probabilistic Relational Models,
Relational Markov Networks, Relational Probability Trees, Stochastic Logic
Programming to name a few)
86
Social Network Analysis

Social Networks: An Introduction

Primitives for Network Analysis

Different Network Distributions

Models of Social Network Generation

Mining on Social Network

Summary
87
Ref: Mining on Social Networks









D. Liben-Nowell and J. Kleinberg. The Link Prediction Problem for Social
Networks. CIKM’03
P. Domingos and M. Richardson, Mining the Network Value of Customers.
KDD’01
M. Richardson and P. Domingos, Mining Knowledge-Sharing Sites for Viral
Marketing. KDD’02
D. Kempe, J. Kleinberg, and E. Tardos, Maximizing the Spread of Influence
through a Social Network. KDD’03.
P. Domingos, Mining Social Networks for Viral Marketing. IEEE Intelligent
Systems, 20(1), 80-82, 2005.
S. Brin and L. Page, The anatomy of a large scale hypertextual Web search
engine. WWW7.
S. Chakrabarti, B. Dom, D. Gibson, J. Kleinberg, S.R. Kumar, P. Raghavan, S.
Rajagopalan, and A. Tomkins, Mining the link structure of the World Wide
Web. IEEE Computer’99
D. Cai, X. He, J. Wen, and W. Ma, Block-level Link Analysis. SIGIR'2004.
Lecture notes from Lise Getoor’s website: www.cs.umd.edu/~getoor/
88
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