Jeffrey xu yu large graph processing

Large Graph Processing
Jeffrey Xu Yu (于旭)
Department of Systems Engineering and
Engineering Management
The Chinese University of Hong Kong
yu@se.cuhk.edu.hk, http://www.se.cuhk.edu.hk/~yu

Facebook Social Network
 In 2011, 721 million users, 69 billion friendship links. The
degree of separation is 4. (Four Degrees of Separation by
Backstrom, Boldi, Rosa, Ugander, and Vigna, 2012)
5

The Scale/Growth of Social Networks
 Facebook statistics
 829 million daily active users on average in June 2014
 1.32 billion monthly active users as of June 30, 2014
 81.7% of daily active users are outside the U.S. and
Canada
 22% increase in Facebook users from 2012 to 2013
 Facebook activities (every 20 minutes on Facebook)
 1 million links shared
 2 million friends requested
 3 million messages sent
http://newsroom.fb.com/company-info/
http://www.statisticbrain.com/facebook-statistics/
6

The Scale/Growth of Social Networks
 Twitter statistics
 271 million monthly active users in 2014
 135,000 new users signing up every day
 78% of Twitter active users are on mobile
 77% of accounts are outside the U.S.
 Twitter activities
 500 million Tweets are sent per day
 9,100 Tweets are sent per second
https://about.twitter.com/company
http://www.statisticbrain.com/twitter-statistics/
7

Location Based Social Networks
8

Financial Networks
 We borrow £1.7 trillion, but
we're lending £1.8 trillion.
Confused? Yes, inter-nation
finance is complicated..." 9

US Social Commerce --
Statistics and Trends
10

Activities on Social Networks
 When all functions are integrated ….
11

Graph Mining/Querying/Searching
 We have been working on many graph problems.
 Keyword search in databases
 Reachability query over large graphs
 Shortest path query over large graphs
 Large graph pattern matching
 Graph clustering
 Graph processing on Cloud
 ……
12

Some Topics
 Ranking over trust networks
 Influence on social networks
 Influenceability estimation in Social Networks
 Random-walk domination
 Diversified ranking
 Top-k structural diversity search
14

Ranking over Trust Networks
15

 Real rating systems (users and objects)
 Online shopping websites (Amazon) www.amazon.com
 Online product review websites (Epinions) www.epinions.com
 Paper review system (Microsoft CMT)
 Movie rating (IMDB)
 Video rating (Youtube)
Reputation-based Ranking
16

The Bipartite Rating Network
 Two entities: users and objects
 Users can give rating to objects
 If we take the average as the ranking score of an object,
o1 and o3 are the top.
 If we consider the user’s reputation, e.g., u4, …
Objects
Users
Ratings
17

Reputation-based Ranking
 Two fundamental problems
 How to rank objects using the ratings?
 How to evaluate users’ rating reputation?
 Algorithmic challenges
 Robustness
 Robust to the spamming users
 Scalability
 Scalable to large networks
 Convergence
 Convergent to a unique and fixed ranking vector
18

Signed/Unsigned Trust Networks
 Signed Trust Social Networks (users): A user can
express their trust/distrust to others by positive/negative
trust score.
 Epinions (www.epinions.com)
 Slashdot (www.slashdot.org)
 Unsigned Trust Social Networks (users): A user can only
express their trust.
 Advogato (www.advogato.org)
 Kaitiaki (www.kaitiaki.org.nz)
 Unsigned Rating Networks (users and objects)
 Question-Answer systems
 Movie-rating systems (IMDB)
 Video rating systems in Youtube
19

The Trustworthiness of a User
 The final trustworthiness of a user is determined by how
users trust each other in a global context and is
measured by bias.
 The bias of a user reflects the extend up to which his/her
opinions differ from others.
 If a user has a zero bias, then his/her opinions are 100%
unbaised and 100% taken.
 Such a user has high trustworthiness.
 The trustworthiness, the trust score, of a user is
1 – his/her bias score.
20

An Existing Approach
 MB [Mishra and Bhattacharya, WWW’11]
 The trustworthiness of a user cannot be trusted,
because MB treats the bias of a user by relative
differences between itself and others.
 If a user gives all his/her friends a much higher trust
score than the average of others, and gives all his/her
foes a much lower trust score than the average of
others, such differences cancel out. This user has
zero bias and can be 100% trusted.
21

An Example
 Node 5 gives a trust score
𝑊51 = 0.1 to node 1. Node 2
and node 3 give a high trust
score 𝑊21 = 𝑊31 = 0.8 to
node 1.
 Node 5 is different from
others (biased), 0.1 – 0.8.
22

MB Approach
 The bias of a node 𝑖 is 𝑏𝑖.
 The prestige score of node 𝑖 is 𝑟𝑖.
 The iterative system is
23

An Example
 Consider 51, 21, 31.
 A trust score = 0.1 – 0.8 = -0.7.
 Consider 23, 43, 53.
 A trust score = 0.9 – 0.2 = 0.7
 Node 5 has zero bias.
 The bias scores by MB.
24

Our Approach
 To address it, consider a contraction mapping.
 Given a metric space 𝑋 with a distance function 𝑑().
 A mapping 𝑇 from 𝑋 to 𝑋 is a contraction mapping if
there exists a constant c where 0 ≤ 𝑐 < 1 such that
𝑑(𝑇(𝑥), 𝑇(𝑦)) ≤ 𝑐 × 𝑑(𝑥, 𝑦).
 The 𝑇 has a unique fixed point.
25

Our Approach
 We use two vectors, 𝑏 and 𝑟, for bias and prestige.
 The 𝑏𝑗 = (𝑓(𝑟)) 𝑗 denotes the bias of node 𝑗, where 𝑟 is
the prestige vector of the nodes, and 𝑓(𝑟) is a vector-
valued contractive function. (𝑓 𝑟 ) 𝑗 denotes the 𝑗-th
element of vector 𝑓(𝑟).
 Let 0 ≤ 𝑓(𝑟) ≤ 𝑒, and 𝑒 = [1, 1, … , 1] 𝑇
 For any 𝑥, 𝑦 ∈ 𝑅 𝑛, the function 𝑓: 𝑅 𝑛 → 𝑅 𝑛 is a vector-
valued contractive function if the following condition
holds,
𝑓 𝑥 – 𝑓 𝑦 ≤ 𝜆 ∥ 𝑥 − 𝑦 ∥∞ 𝑒
where 𝜆 ∈ [0,1) and ∥∙∥∞ denotes the infinity norm.
26

The Framework
 Use a vector-valued contractive function, which is a
generalization of the contracting mapping in the fixed
point theory.
 MB is a special case in our framework.
 The iterative system can converges into a unique fixed
prestige and bias vector in an exponential rate of
convergence.
 We can handle both unsigned and singed trust social
networks.
27

Influence on Social Networks
28

Diffusion in Networks
 We care about the decisions made by friends and
colleagues.
 Why imitating the behavior of others
 Informational effects: the choices made by others can
provide indirect information about what they know.
 Direct-benefit effects: there are direct payoffs from
copying the decisions of others.
 Diffusion: how new behaviors, practices, opinions,
conventions, and technologies spread through a social
network.
29

A Real World Example
 Hotmail’s viral climb to
the top spot (90’s):
8 million users in
18 months!
 Far more effective than
conventional advertising
by rivals and far cheaper
too!
30

Stochastic Diffusion Model
 Consider a directed graph 𝐺 = (𝑉, 𝐸).
 The diffusion of information (or influence) proceeds in
discrete time steps, with time 𝑡 = 0, 1, …. Each node 𝑣
has two possible states, inactive and active.
 Let 𝑆𝑡 ⊆ 𝑉 be the set of active nodes at time 𝑡 (active set
at time 𝑡). 𝑆0 is the seed set (the seeds of influence
diffusion).
 A stochastic diffusion model (with discrete time steps) for
a social graph 𝐺 specifies the randomized process of
generating active sets 𝑆𝑡 for all 𝑡 ≥ 1 given the initial 𝑆0.
 A progressive model is a model 𝑆𝑡−1 ⊆ 𝑆𝑡 for 𝑡 > 1.
31

Influence Spread
 Let Φ(𝑆0) be the final active set (eventually stable
active set) where 𝑆0 is the initial seed set.
 Φ(𝑆0) is a random set determined by the stochastic
process of the diffusion model.
 To maximize the expected size of the final active set.
 Let 𝔼(𝑋) denote the expected value of a random
variable 𝑋.
 The influence spreed of seed set 𝑆0 is defined as
𝜎 𝑆0 = 𝔼(|Φ(𝑆0)|). Here the expectation is taken
among all random events leading to Φ(𝑆0).
32

Independent Cascade Model (IC)
 IC takes 𝐺 = (𝑉, 𝐸), the influence probability 𝑝 on all
edges, and initial seed set 𝑆0 as the input, and generates
the active sets 𝑆𝑡 for all 𝑡 ≥ 1.
 At every time step 𝑡 ≥ 1, first set 𝑆𝑡 = 𝑆𝑡−1.
 Next for every inactive node 𝑣 ∉ 𝑆𝑡−1, for node 𝑢 ∈
𝑁𝑖𝑛 𝑣 ∩ 𝑆𝑡−1𝑆𝑡−2 , 𝑢 executes an activation attempt
with success probability 𝑝(𝑢, 𝑣). If successful, 𝑣 is
added into 𝑆𝑡 and it is said 𝑢 activates 𝑣 at time 𝑡. If
multiple nodes active 𝑣 at time 𝑡, the end effect is the
same.
33

Influenceability Estimation in Social Networks
 Applications
 Influence maximization for viral marketing
 Influential nodes discovery
 Online advertisement
 The fundamental issue
 How to evaluate the influenceability for a give node
in a social network?
36

 The independent cascade model.
 Each node has an independent probability to
influence his neighbors.
 Can be modeled by a probabilistic graph, called
influence network, 𝐺 = (𝑉, 𝐸, 𝑃).
 A possible graph 𝐺 𝑃 = (𝑉𝑃, 𝐸 𝑃) has probability
Pr 𝐺 𝑃 = 𝑒∈𝐸 𝑃
𝑝 𝑒 𝑒∈𝐸 𝐸 𝑃
(1 − 𝑝 𝑒)
 There are 2|𝐸| possible graphs (Ω).
Reconsider IC Model
37

 Independent cascade model.
 Given a probabilistic graph 𝐺 𝑃 = (𝑉𝑃, 𝑉𝑃)
 Pr 𝐺 𝑃 = 𝑒∈𝐸 𝑃
𝑝 𝑒 𝑒∈𝐸 𝐸 𝑃
(1 − 𝑝 𝑒)
 Given a graph 𝐺 = (𝑉, 𝐸, 𝑃), and a node 𝑠, estimate the
expected number of nodes that are reachable from 𝑠.
 𝐹𝑠(𝐺) = 𝐺 𝑃∈Ω
Pr 𝐺 𝑃 𝑓𝑠(𝐺 𝑃) where 𝑓𝑠(𝐺 𝑃) is the
number of nodes that are reachable from the seed
node 𝑠.
The Problem
39

Reduce the Variance
 The accuracy of an approximate algorithm is measured
by the mean squared error 𝔼 ( 𝐹𝑠 𝐺 − 𝐹𝑠 𝐺 )2
 By the variance-bias decomposition
𝔼 ( 𝐹𝑠 𝐺 − 𝐹𝑠 𝐺 )2 = Var 𝐹𝑠 𝐺 + 𝔼( 𝐹𝑠 𝐺 − 𝐹𝑠 𝐺 )
2
 Make an estimator unbiased  the 2nd term will be
cancelled out.
 Make the variance as small as possible.
40

Naïve Monte-Carlo (NMC)
 Sampling 𝑁 possible graphs 𝐺1, 𝐺2, … , 𝐺 𝑁.
 For each sampled possible graph 𝐺𝑖, compute the
number of nodes that are reachable from 𝑠.
 𝑁𝑀𝐶 Estimator: Average of the number of reachable
nodes over 𝑁 possible graphs. 𝐹 𝑁𝑀𝐶 = 𝑖=1
𝑁
𝑓𝑠(𝐺 𝑖)
𝑁
 𝐹 𝑁𝑀𝐶 is an unbiased estimator of 𝐹𝑠(𝐺) since
𝔼 𝐹 𝑁𝑀𝐶 = 𝐹𝑠 𝐺 .
 𝑁𝑀𝐶 is the only existing algorithm used in the influence
maximization literature.
41

 𝑁𝑀𝐶 Estimator: Average of the number of reachable
nodes over 𝑁 possible graphs. 𝐹 𝑁𝑀𝐶 = 𝑖=1
𝑁
𝑓𝑠(𝐺 𝑖)
𝑁
 𝐹 𝑁𝑀𝐶 is an unbiased estimator of 𝐹𝑠(𝐺)
since 𝔼 𝐹 𝑁𝑀𝐶 = 𝐹𝑠(𝐺).
 The variance of 𝑁𝑀𝐶 is
𝑉𝑎𝑟 𝐹 𝑁𝑀𝐶 =
𝔼 𝑓𝑠(𝐺)2 − (𝔼 𝑓𝑠(𝐺) )2
𝑁
=
𝐺 𝑃∈Ω 𝑃𝑟 𝐺 𝑝 𝑓𝑠(𝐺)2−𝐹𝑠(𝐺)2
𝑁
 Computing the variance is extreme expensive, because
it needs to enumerate all the possible graphs.
42

 In practice, it resorts to an unbiased estimator of 𝑉𝑎𝑟( 𝐹 𝑁𝑀𝐶).
 The variance of 𝑁𝑀𝐶 is
𝑉𝑎𝑟 𝐹 𝑁𝑀𝐶 =
𝑖=1
𝑁
(𝑓𝑠 𝐺𝑖 − 𝐹 𝑁𝑀𝐶)2
𝑁 − 1
 But, 𝑉𝑎𝑟 𝐹 𝑁𝑀𝐶 may be very large, because 𝑓𝑠 𝐺𝑖 fall into
the interval [0, 𝑛 − 1].
 The variance can be up to 𝑂(𝑛2).
43

Stratified Sampling
 Stratified is to divide a set of data items into subsets
before sampling.
 A stratum is a subset.
 The strata should be mutually exclusive, and should
include all data items in the set.
 Stratified sampling can be used to reduce variance.
44

A Recursive Estimator [Jin et al. VLDB’11]
 Randomly select 1 edge to partition the probability
space (the set of all possible graphs) into 2 strata
(2 subsets)
 The possible graphs in the first subset include
the selected edge.
 The possible graphs in the second subset do
not include the selected edge.
 Sample possible graphs in each stratum 𝑖 with a
sample size 𝑁𝑖 proportioning to the probability of
that stratum.
 Recursively apply the same idea in each stratum.
45

A Recursive Estimator [Jin et al. VLDB’11]
 Advantages:
 unbiased estimator with a smaller variance.
 Limitations:
 Select only one edge for stratification, which is not
enough to significantly reduce the variance.
 Randomly select edges, which results in a possible
large variance.
46

More Effective Estimators
 Four Stratified Sampling (SS) Estimators
 Type-I basic SS estimator (BSS-I)
 Type-I recursive SS estimator (RSS-I)
 Type-II basic SS estimator (BSS-II)
 Type-II recursive SS estimator (RSS-II)
 All are unbiased and their variances are significantly
smaller than the variance of NMC.
 Time and space complexity of all are the same as
NMC.
47

Type-I Basic Estimator (BSS-I)
 Select 𝑟 edges to partition the probability space (all the
possible graphs) into 2 𝑟
strata.
 Each stratum corresponds to a probability subspace
(a set of possible graphs).
 Let 𝜋𝑖 = Pr[𝐺 𝑃 ∈ Ω𝑖].
 How to select 𝑟 edges: BFS or random
48

Type-I BSS-I Estimator
Sample size = 𝑁
2 𝑟
𝑁 = 𝑁𝜋1
BSS-I
49

Type-I Recursive Estimator (RSS-I)
 Recursively apply the BSS-I into each stratum, until the sample
size reaches a given threshold.
 RSS-I is unbiased and its variance is smaller than BSS-I
 Time and space complexity are the same as NMC.
Sample size = 𝑁
BSS-I
RSS-I
𝑁 = 𝑁𝜋1
50

Type-II Basic Estimator (BSS-II)
 Select 𝑟 edges to partition the probability space (all the
possible graphs) into 𝑟 + 1 strata.
 Similarly, each stratum corresponds to a probability
subspace (a set of possible graphs).
 How to select 𝑟 edges: BFS or random
51

Type-II Estimators
Sample size = 𝑁
𝑟 + 1
BSS-II
RSS-II
𝑁 = 𝑁𝜋1
52

 Social browsing: a process that users in a social network find
information along their social ties.
 photo-sharing Flickr, online advertisements
 Two issues:
 Problem-I: How to place items on 𝑘 users in a social
network so that the other users can easily discover by
social browsing?
 To minimize the expected number of hops that every
node hits the target set.
 Problem-II: How to place items on 𝑘 users so that as many
users as possible can discover by social browsing?
 To maximize the expected number of nodes that hit the
target set.
Social Browsing
54

 The two problems are a random walk problem.
 𝐿-length random walk model where the path length of
random walks is bounded by a nonnegative number 𝐿.
 A random walk in general can be considered as 𝐿 = ∞.
 Let 𝑍 𝑢
𝑡
be the position of an 𝐿-length random walk,
starting from node 𝑢, at discrete time 𝑡.
 Let 𝑇𝑢𝑣
𝐿 be a random walk variable.
 𝑇𝑢𝑣
𝐿
≜ min{min 𝑡: 𝑍 𝑢
𝑡
= v, t ≥ 0}, 𝐿
 The hitting time ℎ 𝑢𝑣
𝐿 can be defined as the expectation
of 𝑇𝑢𝑣
𝐿 .
 ℎ 𝑢𝑣
𝐿
= 𝔼[𝑇𝑢𝑣
𝐿
]
The Random Walk
55

The Hitting Time
 Sarkar and Moore in UAI’07 define the hitting time of the
𝐿-length random walk in a recursive manner.
ℎ 𝑢𝑣
𝐿 =
0, 𝑢 = 𝑣
1 +
𝑤∈𝑉
𝑝 𝑢𝑤ℎ 𝑤𝑣
𝐿−1, 𝑢 ≠ 𝑣
 Our hitting time can be computed by the recursive
procedure.
 Let 𝑑 𝑢 be the degree of node 𝑢 and 𝑁(𝑢) be the set of
neighbor nodes of 𝑢.
 𝑝 𝑢𝑤 = 1/𝑑 𝑢 be the transition probability for 𝑤 ∈
𝑁(𝑢) and 𝑝 𝑢𝑤 = 0 otherwise.
56

The Random-Walk Domination
 Consider a set of nodes 𝑆. If a random walk from 𝑢
reaches 𝑆 by an 𝐿-length random walk, we say
𝑆 dominates 𝑢 by an 𝐿-length random walk.
 Generalized hitting time over a set of nodes, 𝑆. The
hitting time ℎ 𝑢𝑆
𝐿
can be defined as the expectation of a
random walk variable 𝑇𝑢𝑆
𝐿
.
 𝑇𝑢𝑆
𝐿
≜ min{min 𝑡: 𝑍 𝑢
𝑡
∈ 𝑆, t ≥ 0}, 𝐿
 ℎ 𝑢𝑆
𝐿
= 𝔼[𝑇𝑢𝑆
𝐿
]
 It can be computed recursively.
 ℎ 𝑢𝑆
𝐿
=
0, 𝑢 ∈ 𝑆
1 + 𝑤∈𝑉 𝑝 𝑢𝑤ℎ 𝑤𝑆
𝐿−1
, 𝑢 ∉ 𝑆
57

 How to place items on 𝑘 users in a social network so that
the other users can easily discover by social browsing?
 To minimize the total expected number of hops of which
every node hits the target set.
Problem-I
or
58

 How to place items on 𝑘 users so that as many users as
possible can discover by social browsing? To maximize the
expected number of nodes that hit the target set.
 Let 𝑋 𝑢𝑆
𝐿
be an indicator random variable such that if 𝑢 hits
any one node in 𝑆, then 𝑋 𝑢𝑆
𝐿
= 1, and 𝑋 𝑢𝑆
𝐿
= 0 otherwise by an
𝐿-length random walk.
 Let 𝑝 𝑢𝑆
𝐿
be the probability of an event that an 𝐿-length random
walk starting from 𝑢 hits a node in 𝑆.
 Then, 𝔼 𝑋 𝑢𝑆
𝐿
= 𝑝 𝑢𝑆
𝐿
.
 𝑝 𝑢𝑆
𝐿
=
1, 𝑢 ∈ 𝑆
𝑤∈𝑉 𝑝 𝑢𝑤 𝑝 𝑤𝑆
𝐿−1
, 𝑢 ∉ 𝑆
Problem-II
59

Influence Maximization vs Problem II
 Influence maximization is to select 𝑘 nodes to maximize
the expected number of nodes that are reachable from
the nodes selected.
 Independent cascade model
 Probability associated with the edges are independent
 A target node can influence multiple immediate neighbors
at a time.
 Problem II is to select 𝑘 nodes to maximize the
expected number of nodes that reach a node in the
nodes selected.
 𝐿-length random walk model
60

 The submodular set function maximization subject to
cardinality constraint is 𝑁𝑃-hard.

𝑎𝑟𝑔 max
𝑆⊆𝑉
𝐹(𝑆)
𝑠. 𝑡. 𝑆 = 𝐾
 The greedy algorithm
 There is a 1 −
1
𝑒
approximation algorithm.
 Linear time and space complexity w.r.t. the size of the
graph.
 Submodularity: 𝐹(𝑆) is submodular and non-decreasing.
 Non-decreasing: 𝑓(𝑆) ≤ 𝑓(𝑇) for 𝑆 ⊆ 𝑇 ⊆ 𝑉.
 Submodular: Let 𝑔𝑗(𝑆) = 𝑓(𝑆 ∪ {𝑗}) – 𝑓(𝑆) be the marginal gain. Then,
𝑔𝑗(𝑆) ≥ 𝑔𝑗(𝑇), for j ∈ V T and 𝑆 ⊆ 𝑇 ⊆ 𝑉.
Submodular Function Maximization
61


𝑎𝑟𝑔 max
𝑆⊆𝑉
𝐹(𝑆)
𝑠. 𝑡. 𝑆 = 𝐾
 Both Problem I and Problem II use a submodular set
function.
 Problem-I: 𝐹1 S = nL − 𝑢∈𝑉𝑆 ℎ 𝑢𝑆
𝐿
 Problem-II: 𝐹2(𝑆) = 𝑤∈𝑉 𝔼[𝑋 𝑤𝑆
𝐿
] = 𝑤∈𝑉 𝑝 𝑤𝑆
𝐿
62

The Algorithm
 Let 𝜎 𝑢 S = F(𝑆 ∪ {𝑢}) − 𝐹(𝑆)
 It implies dynamic programming (DP) is needed to
compute the marginal gain.
Marginal gain
63

Diversified Ranking [Li et al, TKDE’13]
 Why diversified ranking?
 Information requirements diversity
 Query incomplete
PAKDD09-65

Problem Statement
 The goal is to find K nodes in a graph that are relevant to
the query node, and also they are dissimilar to each
other.
 Main applications
 Ranking nodes in social network, ranking papers, etc.
66

Challenges
 Diversity measures
 No wildly accepted diversity measures on graph in the
literature.
 Scalability
 Most existing methods cannot be scalable to large
graphs.
 Lack of intuitive interpretation.
67

Grasshopper/ManiRank
 The main idea
 Work in an iterative manner.
 Select a node at one iteration by random walk.
 Set the selected node to be an absorbing node, and
perform random walk again to select the second node.
 Perform the same process 𝐾 iterations to get 𝐾 nodes.
 No diversity measure
 Achieving diversity only by intuition and experiments.
 Cannot scale to large graph (time complexity O(𝐾𝑛2
))
68

Grasshopper/ManiRank
 Initial random walk with no absorbing states
 Absorbing random walk after ranking the first item
69

Our Approach
 The main idea
 Relevance of the top-K nodes (denoted by a set S) is achieved by
the large (Personalized) PageRank scores.
 Diversity of the top-K nodes is achieved by large expansion ratio.
 Expansion ratio of a set nodes 𝑆: 𝜎 𝑆 = 𝑁 𝑆 /𝑛.
 Larger expansion ratio implies better diversity
70


𝑎𝑟𝑔 max
𝑆⊆𝑉
𝐹(𝑆)
𝑠. 𝑡. 𝑆 = 𝐾
 The greedy algorithm
 There is a 1 −
1
𝑒
approximation algorithm.
 Linear time and space complexity w.r.t. the size of the
graph.
 Submodularity: 𝐹(𝑆) is submodular and non-decreasing.
 Non-decreasing: 𝑓(𝑆) ≤ 𝑓(𝑇) for 𝑆 ⊆ 𝑇 ⊆ 𝑉.
 Submodular: Let 𝑔𝑗(𝑆) = 𝑓(𝑆 ∪ {𝑗}) – 𝑓(𝑆) be the marginal gain. Then,
𝑔𝑗(𝑆) ≥ 𝑔𝑗(𝑇), for j ∈ V T and 𝑆 ⊆ 𝑇 ⊆ 𝑉.
71

Top-k Structural Diversity
Search
72

 Social contagion is a process of information (e.g. fads,
news, opinions) diffusion in the online social networks
 Traditional biological contagion model, the affected
probability depends on degree.
MarketingOpinions Diffusion Social Network
Social Contagion
73

Facebook Study [Ugander et al., PNAS’12]
 Case study: The process of a user joins Facebook in
response to an invitation email from an existing
Facebook user.
 Social contagion is not like biological contagion.
74

 Structural diversity of an
individual is the number of
connected components in
one’s neighborhood.
 The problem: Find 𝑘
individuals
with highest structural
diversity. Connected components in the
neighborhood of “white center”
Structural Diversity
75

Big Data: The Volume
 Consider a dataset 𝐷 of 1 PetaByte (1015 bytes).
A linear scan of 𝐷 takes 46 hours with a fastest
Solid State Drive (SSD) of speed of 6GB/s.
 PTIME queries do not always serve as a good yardstick
for tractability in “Big Data with Preprocessing” by Fan.
et al., PVLDB”13.
 Consider a function 𝑓 𝐺 . One possible way is to
make 𝐺 small to be 𝐺’, and find the answers from
𝐺’ as it can be answered by 𝐺, 𝑓’(𝐺’) ≈ 𝑓(𝐺).
 There are many ways we can explore.
77

Big Data: The Volume
 Consider a function 𝑓 𝐺 . One possible way is to make 𝐺
small to be 𝐺’, and find the answers from 𝐺’ as it can be
answered by 𝐺, 𝑓’(𝐺’) ≈ 𝑓(𝐺).
 There are many ways we can explore.
 Make data simple and small
 Graph sampling, Graph compression
 Graph sparsification, Graph simplification
 Graph summary
 Graph clustering
 Graph views
78

More Work on Big Data
 We also believe that there are many things we need to
do on Big Data.
 We are planning explore many directions.
 Make data simple and small
 Graph sampling, graph simplification, graph
summary, graph clustering, graph views.
 Explore different computing approaches
 Parallel computing, distributed computing,
streaming computing, semi-external/external
computing.
79

I/O Efficient Graph Computing
 I/O Efficient: Computing SCCs in Massive Graphs by
Zhiwei Zhang, Jeffrey Xu Yu, Lu Qin, Lijun Chang, and
Xuemin Lin, SIGMOD’13.
 Contract & Expand: I/O Efficient SCCs Computing by
Zhiwei Zhang, Lu Qin, and Jeffrey Xu Yu.
 Divide & Conquer: I/O Efficient Depth-First Search,
Zhiwei Zhang, Jeffrey Xu Yu, Lu Qin, and Zechao
Shang.
80

Reachability Query
 Two possible but infeasible solutions:
 Traverse 𝐺(𝑉, 𝐸) to answer a reachability query
 Low query performance: 𝑂(|𝐸|) query time
 Precompute and store the transitive closure
 Fast query processing
 Large storage requirement: 𝑂(|𝑉|2)
 The labeling approaches:
 Assign labels to nodes in a
preprocessing step offline.
 Answer a query using the labels
assigned online.
81

A
B
D
C
F
G
A
B
C
G
F D
Make a Graph Small and Simple
 Any directed graph 𝐺 can be represented as a DAG (Directed
Acyclic Graph), 𝐺 𝐷, by taking every SCC (Strongly
Connected Component) in 𝐺 as a node in 𝐺 𝐷.
 An SCC of a directed graph 𝐺 = (𝑉, 𝐸) is a maximal set of
nodes 𝐶 ⊆ 𝑉 such that for every pair of nodes 𝑢 and 𝑣 in 𝐶, 𝑢
and 𝑣 are reachable from each other.
82

A
B
D
C
I
E
F
G
H
A
B
C
G
F
D
E
H
I
The Reachability Queries
 Reachability queries can be answered by DAG.
83

The Issue and the Challenge
 It needs to convert a
massive directed graph 𝐺
into a DAG 𝐺 𝐷 in order to
process it efficiently
because
 𝐺 cannot be held in main
memory, and 𝐺 𝐷 can be
much smaller.
 It is assumed that it can be
done in the existing works.
 But, it needs a large
main memory to convert.
84

The Issue and the Challenge
 The Dataset uk-2007
 Nodes: 105,896,555
 Edges: 3,738,733,648
 Average degree: 35
 Memory:
 400 MB for nodes, and
 28 GB for edges.
85

In Memory Algorithm?
 In Memory Algorithm: Scan 𝐺 twice
 DFS(G) to obtain a decreasing order for each node of 𝐺
 Reverse every edge to obtain 𝐺 𝑇, and
 DFS(𝐺 𝑇) according to the same decreasing order to find all
SCCs.
86

4
7
2 3
51
9
68
 DFS(G) to obtain a decreasing order for each node of 𝐺
87

4
7
2 3
51
9
68
4
7
2 3
51
9
68
 Reverse every edge to obtain 𝐺 𝑇
.
88

4
7
2 3
51
9
68
 DFS(𝐺 𝑇
) according to the same decreasing order to find all
SCCs. (A subtree (in black edges) form an SCC.)
89

(Semi)-External Algorithms
 In Memory Algorithm: Scan 𝐺 twice
 The in memory algorithm cannot handle a large graph that
cannot be held in memory.
 Why? No locality. A large number of random I/Os.
 Consider external algorithms and/or semi-external
algorithms. Let 𝑀 be the size of main memory.
 External algorithm: 𝑀 < |𝐺|
 Semi-external algorithm: 𝑘 ∙ |𝑉| ≤ 𝑀 < |𝐺|
 It assumes that a tree can be held in memory.
90

A
B
D
C
I
E
F
G
H
A
B
C
G
F
Main Memory
Contraction Based External Algorithm (1)
 Load in a subgraph and merge SCCs
in it in main memory in every iteration
[Cosgaya-Lozano et al. SEA'09]
91

A
B
D
C
I
E
F
G
H
A
B
C
G
F
A B
C
G
D
Main Memory
92

A
B
D
C
I
E
F
G
H
A
B
C
G
F
A B
C GD FD
H
E
Cannot Find All SCCs Always!
Main Memory
DAG! And memory is full!Cannot load in “I” into memory!
93

2
8
3
4
7
5
1
9
6
Tree-Edge
Forward-Cross-Edge
Backward-Edge
Forward-Edge
Backward-Cross-Edgedelete old tree edge
New tree edge
DFS Based Semi-External Algorithm
 Find a DFS-tree without
forward-cross-edges
[Sibeyn et al. SPAA’02].
 For a forward-cross-
edge (𝑢, 𝑣), delete tree
edge to 𝑣, and (𝑢, 𝑣) as
a new tree edge.
94

DFS Based Approaches: Cost-1
 DFS-SCC uses sequential I/Os.
 DFS-SCC needs to traverse a graph 𝐺 twice using DFS
to compute all SCCs.
 In each DFS, in the worst case it needs the number of
𝑑𝑒𝑝𝑡ℎ(𝐺) ∙ |𝐸(𝑉)|/𝐵 I/Os, where 𝐵 is the block size.
95

 Partial SCCs cannot be contracted to save space while
constructing a DFS tree.
 Why?
 DFS-SCC needs to traverse a graph 𝐺 twice using DFS to
compute all SCCs.
 DFS-SCC uses a total order of nodes (decreasing postorder)
in the second DFS, which is computed in the first DFS.
 SCCs cannot be partially contracted in the first DFS.
 SCCs can be partially contracted in the second DFS, but we
have to remember which nodes belongs to which SCCs with
extra space. Not free!
96

 High CPU cost for reshaping a DFS-tree, when it attempts
to reduce the number of forward-cross-edges.
97

Our New Approach [SIGMOD’13]
 We propose a new two phase algorithm, 2P-SCC:
 Tree-Construction and Tree-Search.
 In Tree-Construction phase, we construct a tree-like
structure.
 In Tree-Search phase, we scan the graph only once.
 We further propose a new algorithm, 1P-SCC, to
combine Tree-Construction and Tree-Search with new
optimization techniques, using a tree.
 Early-Acceptance
 Early-Rejection
 Batch Edge Reduction
A joint work by Zhiwei Zhang, Jeffrey Yu, Qin Lu, Lijun Chang, and Xuemin Lin
98

A New Weak Order
 The total order used in DFS-SCC is too strong and there
is no obvious relationship between the total order and
the SCCs per se, in order to reduce I/Os.
 The total order cannot help to reduce I/O costs.
 We introduce a new weak order.
 For an SCC, there must exist at least one cycle.
 While constructing a tree 𝑇 for 𝐺, a cycle will appear to
contain at least one edge (𝑢, 𝑣) that links to a higher
level node in 𝑇. 𝑑𝑒𝑝𝑡ℎ(𝑢) > 𝑑𝑒𝑝𝑡ℎ(𝑣).
 There are two cases when 𝑑𝑒𝑝𝑡ℎ(𝑢) > 𝑑𝑒𝑝𝑡ℎ(𝑣).
 A cycle: 𝑣 is an ancestor of 𝑢 in 𝑇
 Not a cycle (up-edge): 𝑣 is not an ancestor of 𝑢 in 𝑇.
 We reduce the number of up-edges iteratively.
99

 Let 𝑅𝑠𝑒𝑡(𝑢, 𝐺, 𝑇) be the set of nodes including 𝑢 and nodes that
𝑣 can reach by a tree 𝑇 of 𝐺.
 𝑑𝑒𝑝𝑡ℎ(𝑢, 𝑇): The length of the longest simple
path from root to 𝑢.
 𝑑𝑟𝑎𝑛𝑘(𝑢, 𝑇) = min{𝑑𝑒𝑝𝑡ℎ(𝑣, 𝑇) | 𝑣 ∈ 𝑅𝑠𝑒𝑡(𝑢, 𝐺, 𝑇)}
 drank is used as the weak order!
 𝑑𝑙𝑖𝑛𝑘 𝑢, 𝑇 = 𝑎𝑟𝑔𝑚𝑖𝑛 𝑣 𝑑𝑒𝑝𝑡ℎ 𝑣, 𝑇 𝑣 ∈ 𝑅𝑠𝑒𝑡(𝑢, 𝐺, 𝑇)}
 Nodes do not need to have a unique order.
B
H
C
D
G
E
A
IF
 depth(B) = 1
 drank(B) = 1
 dlink(B) = B
 depth(E) = 3
 drank(E) = 1
 dlink(E) = B
The Weak Order: drank
100

2P-SCC
 To reduce Cost-1, we use a BR+-tree to compute all
SCCs in the Tree-Construction phase. We compute all
SCCs by traversing 𝐺 only once using the BR+-tree
constructed in the Tree-Search phase.
 To reduce Cost-3, we have shown that we only need to
update the depth of nodes locally.
101

B
H
C D
G
E
A
IF
 BR-Tree is a spanning
tree of G.
 BR+-Tree is a BR-Tree
plus some additional
edges (𝑢, 𝑣) such that 𝑣
is an ancestor of 𝑢.
BR-Tree and BR+-Tree
 In Memory: Black edges
102

B
H
C D
GE
A
IF
drank(I) = 1
drank(H) = 2
Up-edge
Tree-Construction: Up-edge
 An edge (𝑢, 𝑣) is an up-edge on
the conditions:
 𝑣 is not an ancestor of 𝑢 in 𝑇
 𝑑𝑟𝑎𝑛𝑘(𝑢, 𝑇) ≥ 𝑑𝑟𝑎𝑛𝑘(𝑣, 𝑇)
 Up-edges violate the existing
order
103

 When there is an violate
up-edge, then
 Modify T
 Delete the old tree
edge
 Set the up-edge as a
new tree edge
 Graph Reconstruction
 No I/O cost, low CPU
cost.
B
H
C D
GE
A
IF
Tree-Construction (Push-Down)
104

B
D
E
A
F
drank(E) = 1
dlink(E) = B
drank(F) = 1
Up-edge
Tree-Construction (Graph Reconstruction)
 Tree edges and one
extra edge in BR+-Tree
form a part of an SCC.
 For an up-edge (𝑢, 𝑣), if
𝑑𝑙𝑖𝑛𝑘(𝑣, 𝑇) is an ancestor
of 𝑢 in 𝑇, delete (𝑢, 𝑣) and
add (𝑢, 𝑑𝑙𝑖𝑛𝑘(𝑣)).
 In Tree-Search, scan the
graph only once to find all
SCCs, which reduces I/O
costs.
A new edge
105

Tree-Construction
 When a BR+-tree is completely constructed, there are no
up-edges.
 There are only two kinds of edges in G.
 The BR+-tree edges, and
 The edges (𝑢, 𝑣) where 𝑑𝑟𝑎𝑛𝑘(𝑢, 𝑇) < 𝑑𝑟𝑎𝑛𝑘(𝑣, 𝑇).
 Such edges do not play in any role in determining an
SCC.
106

B
H
C D
G
E
A
IF
In memory for each node u:
 TreeEdge(u)
 dlink(u)
 drank(u)
In total: 3 × |𝑉|
Search Procedure:
 If an edge (𝑢 , 𝑣) points
to an ancestor, merge
all nodes from 𝑣 to 𝑢 in
the tree
Only need to scan the
graph once.
Tree-Search
107

From 2P-SCC To 1P-SCC
 With 2P-SCC:
 In Tree-Construction phase, we construct a tree by an
approach similar to DFS-SCC, and
 In Tree-Search phase, we scan the graph only once.
 The memory used for BR+-tree is 3 × |𝑉|.
 With 1P-SCC: We combine Tree-Construction and Tree-
Search with new optimization techniques to reduce Cost-2
and Cost-3:
 Early-Acceptance
 Early-Rejection
 Batch Edge Reduction
 Only need to use a BR-tree with memory of 2 × |𝑉|.
108

Early-Acceptance and Early Rejection
 Early acceptance: we contract a partial SCC into a
node in an early stage while constructing a BR-tree.
 Early rejection: we identify necessary conditions to
remove nodes that will not participate in any SCCs
while constructing a BR-tree.
109

Early-Acceptance and Early Rejection
 Consider an example.
 The three nodes on the left can be contracted into a node on the right.
 The node “a” and the subtrees, C and D, can be rejected.
110

B
I
C
D
H
E
A
JG
 Memory: 2 × |𝑉|
 Reduce I/O Cost
KF
Up-edge: Modify Tree
Early-Acceptance
Early-Acceptance
Modify Tree + Early Acceptance
111

DFS Based vs Ours Approaches
 I/O cost for DFS is high
 Use a total order
 Cannot merge SCCs
when found earlier
 Total order cannot be
changed. Large # of
I/Os.
 Cannot prune non-SCC
nodes
 Total order cannot be
changed
 Smaller I/O Cost
 Use a weaker order
 Merge SCCs as early as
possible
 Merge nodes with the
same order. Small size,
small # of I/Os.
 prune non-SCC nodes as
early as possible
 Weaker order is flexible
112

Optimization: Batch Edge Reduction
 With 1PC-SCC, CPU cost is still high.
 In order to determine whether (𝑢, 𝑣) is a backward-
edge/up-edge, it needs to check the ancestor
relationships between 𝑢 and 𝑣 over a tree.
 The tree is frequently updated.
 The average depth of nodes in the tree becomes
larger with frequently push-down operation.
113

Optimization: Batch Edge Reduction
 When memory can hold more edges, there is no need to
contract partial SCCs edge by edge.
 Find all SCCs in the main memory at the same time
 Read all edges that can be read into memory.
 Construct a graph with the edges of the tree
constructed in memory already plus the edges newly
read into memory.
 Construct a DAG in memory using the existing memory
algorithm, which finds all SCCs in memory.
 Reconstruct the BR-Tree according to the DAG.
114

Performance Studies
 Implement using visual C++ 2005
 Test on a PC with Intel Core2 Quard 2.66GHz CPU
and 3.43GB memory running Windows XP
 Disk Block Size: 64𝐾𝐵
 Memory Size: 3 × |𝑉(𝐺)| × 4𝐵 + 64 𝐾𝐵
115

|V| |E| Average
Degree
cit-patent 3,774,768 16,518,947 4.70
go-uniprot 6,967,956 34,770,235 4.99
citeseerx 6,540,399 15,011,259 2.30
WEBSPAM-
UK2002
105,896,555 3,738,733,568 35.00
Four Real Data Sets
116

Parameter Range Default
Node Size 30M - 70M 30M
Average Degree 3 - 7 5
Size of Massive SCCs 200K – 600K 400K
Size of Large SCCs 4K - 12K 8K
Size of Small SCCs 20 - 60 40
# of Massive SCCs 1 1
# of Large SCCs 30 - 70 50
# of Small SCCs 6K – 14K 10K
Synthetic Data Sets
 We construct a graph G by (1) randomly selecting all
nodes in SCCs first, (2) adding edges among the
nodes in an SCC until all nodes form an SCC, and (3)
randomly adding nodes/edges to the graph.
117

1PB-SCC 1P-SCC 2P-SCC DFS-SCC
cit-patent(s) 24s 22s 701s 840s
go-uniprot(s) 22s 21s 301s 856s
citeseerx(s) 10s 8s 517s 669s
cit-patent(I/O) 16,031 13,331 133,467 667,530
go-uniprot(I/O) 26,034 47,947 471,540 619,969
citeseerx(I/O) 15,472 13,482 104,611 392,659
Performance Studies
118

WEBSPAM-UK2007: Vary Node Size
119

WEBSPAM-UK2007: Vary Memory
120

Synthetic Data Sets: Vary SCC Sizes
121

Synthetic Data Sets: Vary # of SCCs
122

From Semi-External to External
 Existing semi-external solutions work under the condition
that it can held a tree in main-memory 𝑘∙ |𝑉| ≤ |𝑀|, and
generate a large I/Os.
 We study an external algorithm by removing the
condition of 𝑘 ∙ |𝑉| ≤ |𝑀|.
123
A joint work by Zhiwei Zhang, Qin Lu, and Jeffrey Yu

The New Approach: The Main Idea
 DFS based approaches generate random accesses
 Contraction based semi-external approach
reduces |𝑉| and |𝐸| together at the same time.
 Cannot find all SCCs.
 The main idea of our external algorithm:
 Work on a small graph 𝐺’ of 𝐺 by reducing 𝑉 because 𝑀
can be small.
 Find all SCCs in 𝐺’.
 Add removed nodes back to find SCCs in 𝐺.
124

The New Approach: The Property
 Reducing the given graph
 𝐺 𝑉, 𝐸 → 𝐺′
𝑉′
, 𝐸′
, 𝑉 < |𝑉′
|.
 If 𝑢 can reach 𝑣 in 𝐺, 𝑢 can also reach 𝑣 in 𝐺′.
 Maintaining this property may generate a large
number of random I/O access.
 Reason: several nodes on the path from 𝑢 to 𝑣 may
be removed from 𝐺 in the previous iterations.
125

The New Approach: The Approach
 We introduce a new Contraction & Expansion approach.
 Contraction phase:
 Reduce nodes iteratively, 𝐺1, 𝐺2 … 𝐺𝑙.
 It decreases |𝑉(𝐺𝑖)|, but may increase |𝐸(𝐺𝑖)|.
 Expansion phase:
 In the reverse order in contraction phase,
𝐺𝑙, 𝐺𝑙−1 … 𝐺1.
 Find all SCCs in 𝐺𝑙 using a semi-external
algorithm.
 The semi-external algorithm deals with edges.
 Expand 𝐺𝑖 back to 𝐺𝑖−1.
126

The Contraction
 In Contraction phase, graph 𝐺1, 𝐺2 … 𝐺𝑙 are generated,
 𝐺𝑖+1 is generated by removing a batch of nodes from 𝐺𝑖.
 Stops until 𝑘 ∙ |𝑉| < |𝑀| when semi-external approach
can be applied.
G1 G2 G3
127

The Expansion
 In Expansion phase, removed nodes are added
 Addition is in the reverse order of their removal in
contraction phase.
G1 G2 G3
128

The Contraction Phase
 Compared with 𝐺𝑖, 𝐺𝑖+1should have the following
properties
 Contractable:
 |𝑉(𝐺𝑖+1)| < |𝑉(𝐺𝑖)|
 SCC-Preservable:
 𝑆𝐶𝐶(𝑢, 𝐺𝑖) = 𝑆𝐶𝐶(𝑣, 𝐺𝑖) ⟺ 𝑆𝐶𝐶(𝑢, 𝐺𝑖+1) = 𝑆𝐶𝐶(𝑣, 𝐺𝑖+1)
 Recoverable:
 𝑣 ∈ 𝑉𝑖 − 𝑉𝑖+1 ⟺ 𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑢𝑟 𝑣, 𝐺𝑖 ⊆ 𝐺𝑖+1
129

Contract Vi+1
 Recoverable:
 𝑣 ∈ 𝑉𝑖 − 𝑉𝑖+1 ⟺ 𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑢𝑟(𝑣, 𝐺𝑖) ⊆ 𝐺𝑖+1
 𝐺𝑖+1 is recoverable if and only if 𝑉𝑖+1 is a vertex cover of
𝐺𝑖.
 At this condition, we can determine which SCCs the
nodes in 𝐺𝑖 belong to by scanning 𝐺𝑖 once.
 For each edge, we select the node with a higher degree
or a higher order.
130

Contract Vi+1
c
d
h
a
b
e
f
g
i
ID1 ID2 Deg1 Deg2
a b 3 3
a d 3 4
b c 3 2
c d 2 4
d e 4 4
d g 4 4
e b 4 3
e g 4 4
f g 2 4
g h 4 2
h i 2 2
i f 2 2
DISK
For each edge, we select the node with
a higher degree or a higher order.
131

Construct Ei+1
 SCC-Preservable:
 𝑆𝐶𝐶(𝑢, 𝐺𝑖) = 𝑆𝐶𝐶(𝑣, 𝐺𝑖) ⟺ 𝑆𝐶𝐶(𝑢, 𝐺𝑖+1) = 𝑆𝐶𝐶(𝑣, 𝐺𝑖+1)
 If 𝑣 ∈ 𝑉𝑖 – 𝑉𝑖+1, remove (𝑣𝑖𝑛, 𝑣) and (𝑣, 𝑣 𝑜𝑢𝑡) and add (𝑣𝑖𝑛, 𝑣 𝑜𝑢𝑡).
 Although |𝐸| may be larger, |𝑉| is sure to be smaller.
 Smaller |𝑉| implies semi-external approach can be applied.
132

ID1 ID2
e d
b d
i g
g i
Construct Ei+1
c
d
h
a
b
e
f
g
i
ID1 ID2
d e
d g
e b
e g
DISK
If 𝑣 ∈ 𝑉𝑖 – 𝑉𝑖+1, remove (𝑣, 𝑣𝑖𝑛) and
(𝑣, 𝑣 𝑜𝑢𝑡) and add (𝑣𝑖𝑛, 𝑣 𝑜𝑢𝑡)
Existing Edges
New Edges
133

The Expansion Phase
 𝑆𝐶𝐶(𝑢, 𝐺𝑖) = 𝑆𝐶𝐶(𝑣, 𝐺𝑖) = 𝑆𝐶𝐶(𝑤, 𝐺𝑖) (𝑢, 𝑤 ∈ 𝑉𝑖+1)
⟺ 𝑢 → 𝑣 & 𝑣 → 𝑤 in 𝐺𝑖
 For any node 𝑣 ∈ 𝑉𝑖+1 − 𝑉𝑖, 𝑆𝐶𝐶(𝑣, 𝐺𝑖) can be
computed using
𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑢𝑟𝑖𝑛 (𝑣, 𝐺𝑖) and 𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑢𝑟𝑜𝑢𝑡(𝑣, 𝐺𝑖) only.
a
b
c
134

ID1 ID2
a b
a d
b c
c d
d e
d g
e b
e g
f g
g h
h i
i f
Expansion Phase
c
d
h
a
b
e
f
g
i
DISK
𝑆𝐶𝐶 𝑢, 𝐺𝑖 = 𝑆𝐶𝐶 𝑣, 𝐺𝑖 = 𝑆𝐶𝐶 𝑤, 𝐺𝑖
(𝑢, 𝑤 ∈ 𝑉𝑖+1) ⟺ 𝑢 → 𝑣 & 𝑣 → 𝑤 in 𝐺𝑖
135

Performance Studies
 Test on a PC with Intel Core2 Quard 2.66GHz CPU and 3.5GB
memory running Windows XP
 Disk Block Size: 64KB
 Default memory Size: 400𝑀
136

Data Set
 Real Data set
 Synthetic Data
V E Average
Degree
WEBSPAM-
UK2007
105,896,555 3,738,733,568 35.00
Parameter
Node Size 25M – 100M
Average Degree 2 - 6
Size of SCCs 20 – 600K
Number of SCCs 1 – 14 K
137

Performance Studies
Vary Memory Size
138

DFS [SIGMOD’15]
 Given a graph 𝐺(𝑉, 𝐸), depth-first search is to
search 𝐺 following the depth-first order.
A
B E
D
C
F
IH
J
G
A joint work by Zhiwei Zhang, Jeffrey Yu, Qin Lu, and Zechao Shang
139

The Challenge
 It needs to DFS a
massive directed graph
𝐺, but it is possible that
𝐺 cannot be entirely
held in main memory.
 Our work only keeps
nodes in memory,
which is much smaller.
140

The Issue and the Challenge (1)
 Consider all edges from 𝑢, like 𝑢, 𝑣1 , 𝑢, 𝑣2 ,
… , 𝑢, 𝑣 𝑝 . Suppose DFS searches from 𝑢 to 𝑣1. It
is hard to estimate when it will visit 𝑣𝑖 (2 ≤ 𝑖 ≤ 𝑝).
 It is hard to know when
C/D will be visited even
they are near A and B.
 It is hard to design
the format of graph
on disk.
A
B C
D
E
141

The Issue and the Challenge (2)
 A small part of graph can change DFS a lot.
 Even almost the entire graph can be kept in
memory, it still costs a lot to find the DFS.
 (E,D) will change the
existing DFS significantly.
 A large number of
iterations is needed
even the memory
keeps a large
portion of graph.
A
B C D
E F G
142

Problem Statement
 We study a semi-external algorithm that
computes a DFS-Tree by which DFS can be
obtained.
 The limited memory 𝑘 𝑉 ≤ 𝑀 ≤ |𝐺|
 𝑘 is a small constant number.
 𝐺 = 𝑉 + |𝐸|
143

DFS-Tree & Edge Type
 A DFS of 𝐺 forms a DFS-Tree
 A DFS procedure can be obtained by a DFS-Tree.
A
B E
D
C
F
IH
J
G
144

 Given a spanning tree 𝑇, there exist 4 types of
non-tree edges.
A
B E
D
C
F
IH
J
G
Forward Edge
Forward-cross Edge Backward-cross Edge
Backward Edge
145

 An ordered spanning tree is a DFS-Tree if there
does not have any forward-cross edges.
A
B E
D
C
F
IH
J
G
Forward Edge
Forward-cross Edge Backward-cross Edge
Backward Edge
146

Existing Solutions
 Iteratively remove the forward-cross edges.
 Procedure:
 If there exists a forward-cross edge
 Construct a new 𝑇 by conducting DFS over the
graph in memory
147

Existing Solutions
 Construct a new 𝑇 by conducting DFS over the graph in
memory until no forward-cross edges exist.
A
B E
D
C
F
IH
J
G
Forward-cross Edge
148

The Drawbacks
 D-1: A total order in 𝑉(𝐺) needs to be
maintained in the whole process.
 D-2: A large number of I/Os is produced
 Need to scan all edges in every iteration.
 D-3: A large number of iterations is needed.
 The possibility of grouping the edges near each
other in DFS is not considered.
149

Why Divide & Conquer
 We aim at dividing the graph into several
subgraphs 𝐺1, 𝐺2 , … , 𝐺 𝑝 with possible overlaps
among them.
 Goal: The DFS-Tree for 𝐺 can be computed by
the DFS-Trees for all 𝐺𝑖.
 Divide & Conquer approach can overcome the
existing drawbacks.
150

 To address D-1
 A total order in 𝑉(𝐺) needs to be maintained in
the whole process.
 After dividing the graph 𝐺 into 𝐺0 , 𝐺1 , … , 𝐺 𝑝, we
only need to maintain the total order in 𝑉(𝐺𝑖).
151

 To address D-2
 A large number of I/Os is produced.
 It needs to scan all edges in each iterations.
 After dividing the graph 𝐺 into 𝐺0 , 𝐺1 , … , 𝐺 𝑝, we
only need to scan the edges in 𝐺𝑖 to eliminate
forward-cross edges.
152

 To address D-3
 A large number of iterations is needed.
 It cannot group the edges together that are near
each other in DFS visiting sequence.
 After dividing the graph 𝐺 into 𝐺0 , 𝐺1 , … , 𝐺 𝑝, the
DFS procedure can be applied to 𝐺𝑖 independently.
153

Valid Division
A
B
F
C
D
E
𝐺1
𝐺2
A
B
F
C
D
E
𝐺1
𝐺2
The left is not a DFS-tree The right is a DFS-tree
154

Invalid Division
 An example:
A
B
F
C
D
E
𝐺1
𝐺2
No matter how the DFS-
Trees for 𝐺1 and 𝐺2 are
ordered, the merged tree
cannot be a DFS-Tree for 𝐺.
155

How to Cut: Challenges
 Challenge-1: uneasy to check whether a division
is valid.
 Need to make sure a DFS-Tree for a divided subgraph
will not affect the DFS-Tree of others.
 Challenge-2: finding a good division is non-trivial.
 The edge types between different subgraphs are
complicated.
 Challenge-3: The merge procedure needs to
make sure that the result is the DFS-Tree for 𝐺.
156

Our New Approach
 To address Challenge-1:
 Compute a light-weight summary graph (S-graph)
denoted as 𝐺.
 Check whether a division is valid by searching 𝐺
 Recursively divide & conquer.
 The DFS-Tree for 𝐺 is computed only by 𝑇𝑖 and 𝐺.
157

Four Division Properties
 Node-Coverage: 1 ≤ 𝑖 ≤ 𝑝 𝑉 𝐺𝑖 = 𝐺
 Contractible: 𝑉 𝐺𝑖 < |V(𝐺)|
 Independence: any pair of nodes in
𝑉 𝑇𝑖 ∩ 𝑉(𝑇𝑗) are consistent.
 𝑇𝑖 and 𝑇𝑗 can be dealt with independently
(𝑇𝑖 and 𝑇𝑗 are DFS-Tree for 𝐺𝑖 and 𝐺𝑗)
 DFS-Preservable: there exists a DFS-Tree 𝑇 for
graph 𝐺 such that 𝑉 𝑇 = 1≤𝑖≤𝑝 𝑉(𝑇𝑖) and
E 𝑇 = 1≤𝑖≤𝑝 𝐸(𝑇𝑖)
 DFS-Tree for 𝐺 can be computed by 𝑇𝑖
158

DFS-Preservable Property
 DFS-Tree for 𝐺 can be computed by 𝑇𝑖.
 DFS∗
-Tree: A spanning tree with the same edge
set of a DFS-Tree (without order).
 Suppose the independence property is satisfied,
then the DFS-preservable property is satisfied if
and only if the spanning tree T with 𝑉 𝑇 =
1≤𝑖≤𝑝 𝑉(𝑇𝑖) and 𝐸 𝑇 = 1≤𝑖≤𝑝 𝐸(𝑇𝑖) is a 𝐷𝐹𝑆∗-
Tree.
159

Independence Property
 Any pair of nodes in 𝑉 𝑇𝑖 ∩ 𝑉(𝑇𝑗) are consistent
(𝑇𝑖 and 𝑇𝑗 are DFS-Tree for 𝐺𝑖 and 𝐺𝑗).
 𝑇𝑖 , 𝑇𝑗 can be dealt with independently.
 This may not hold: 𝑢 is an ancestor of 𝑣 in 𝑇𝑖, but
is a sibling in 𝑇𝑗.
 Theorem:
 Given a division 𝐺0, 𝐺1, … , 𝐺 𝑝 of 𝐺, the
independence property is satisfied if and only if for
any subgraphs 𝐺𝑖 and 𝐺𝑗, 𝐸 𝐺𝑖 ∩ 𝐸 𝐺𝑗 = ∅.
160

Independence Property
C
A
ED
B
F
𝐺1
𝐺3
𝐺2
161

DFS-Preservable Example
A
B
D
C
E
F
G
 DFS-preservable
property is not satisfied.
 The DFS-Tree for 𝐺 does
not exist given the DFS-
Tree for each subgraph.
 Forward-cross edges
always exist.
162

Our Approach
 Root based division: independence is satisfied.
 For each 𝐺𝑖, it has a spanning tree 𝑇𝑖.
 For a division 𝐺0, 𝐺1, …, 𝐺 𝑝, 𝐺0 ∩ 𝐺𝑖 = 𝑟𝑖.
 𝑟𝑖 is the root of 𝑇𝑖 and the leaf of 𝑇0
𝐺0
𝐺𝑖 𝐺𝑗
163

Our Approach
 We expand 𝐺0 to capture the relationship between
different 𝐺𝑖 and call it S-graph.
 S-graph is used to check whether the current division
is valid (DFS-preservable property is satisfied)
𝐺0
𝐺𝑖 𝐺𝑗
S-graph
164

S-edge
 S-edge: given a spanning tree 𝑇 of 𝐺, (𝑢′
, 𝑣′
) is the
S-edge of 𝑢, 𝑣 if
 𝑢′ is ancestor of 𝑢 and 𝑣′ is ancestor of 𝑣 in 𝑇,
 Both 𝑢′, 𝑣′ are the children of 𝐿𝐶𝐴(𝑢, 𝑣), where
𝐿𝐶𝐴(𝑢, 𝑣) is the lowest common ancestor of 𝑢, 𝑣 in 𝑇.
165

S-edge Example
A
B
D
H
I
E
K
F
C
J
𝐺0
Cross edge
S-edge
G
166

S-graph
 For a division 𝐺0, 𝐺1, …, 𝐺 𝑝 and 𝑇0 is the DFS-Tree
for 𝐺0, S-graph 𝐺 is constructed in the following:
 Remove all backward and forward edges w.r.t. 𝑇0
 Replace all cross-edges (𝑢, 𝑣) with their
corresponding S-edge if the S-edge is between nodes
in 𝐺0,
 For edge (𝑢, 𝑣), if 𝑢 ∈ 𝐺𝑖 and 𝑣 ∈ 𝐺0, add edge (𝑟𝑖, 𝑣)
and do the same for 𝑣.
167

S-graph Example
A
B
D
H
I
E
K
F
C
J
𝐺0
Cross edge
S-edge
G
link (𝑟𝑖, 𝑣)
168

S-graph Example
A
B
D
H
I
E
K
F
C
J
𝐺0
Cross edge
S-edge
G
link (𝑟𝑖, 𝑣)
S-graph
169

Division Theorem
 Consider a division 𝐺0, 𝐺1, …, 𝐺 𝑝 and suppose 𝑇0 is
the DFS-Tree for 𝐺0, the division is DFS-preservable
if and only if the S-graph 𝐺 is a DAG.
170

Divide-Star Algorithm
 Divide 𝐺 according to the children of the root 𝑅 of 𝐺.
 If the corresponding S-graph 𝐺 is a DAG, each
subgraph can be computed independently.
 Deal with strongly connected component:
 Modify 𝑇: add a virtual node RS representing a SCC S.
 Modify 𝐺:
 For any edge (𝑢, 𝑣) in S-graph 𝐺, if 𝑢 ∉ 𝑆 and 𝑣 ∈ 𝑆,
add edge (𝑢, 𝑅𝑆). Do the same for 𝑣.
 Remove all nodes in S and corresponding edges.
 Modify Division: create a new tree 𝑇′ rooted at the
virtual root RS and connect to the roots in the SCC.
171

A
B
D
H
I
E
K
F
C
J
G
S-graph
SCC
Add a virtual
root DF
172

A
B
D
H
I
E
K
F
C
J
G
S-graph
DF
S-graph is
DAG
173

A
B
D
H
I
E
K
F
J
G
𝐺0
DF
Divide the graph
into 4 parts
B
C
DF
H
𝐺1
𝐺2 𝐺3
174

Divide-TD Algorithm
 Divide-Star algorithm divides the graph according to
the children of the root.
 The depth of 𝑇0 is 1.
 The max number of subgraphs after dividing will not
be larger than the number of children.
 Divide-TD algorithm enlarges 𝑇0 and the
corresponding S-graph.
 It can result in more subgraphs than that Divide-Star
can provide.
175

Divide-TD Algorithm
 Divide-TD algorithm enlarges 𝑇0 to a Cut-Tree.
 Cut-Tree: Given a tree 𝑇 with root 𝑡0, a cut-tree 𝑇𝑐 is
a subtree of 𝑇 which satisfies two conditions.
 The root of 𝑇𝑐 is 𝑡0.
 For any node 𝑣 ∈ 𝑇 with child nodes 𝑣1, 𝑣2, … , 𝑣 𝑘, if 𝑣 ∈
𝑇𝑐, then either 𝑣 is a leaf node or a node in 𝑇𝑐 with all
child nodes 𝑣1, 𝑣2, … , 𝑣 𝑘.
 With such conditions, for any S-edge (𝑢, 𝑣), only two
situations exist.
 𝑢, 𝑣 ∈ 𝑇𝑐
 𝑢, 𝑣 ∉ 𝑇𝑐
176

Cut-Tree Construction
 Given a tree T with root 𝑟0.
 Initially 𝑇𝑐 contains only the root 𝑟0.
 Iteratively pick a leaf node 𝑣 in 𝑇𝑐 and all the child nodes
of 𝑣 in 𝑇.
 The process stops until the memory cannot hold it after
adding the next nodes.
177

Divide-TD Algorithm
A
B
D
H
I
E
K
F
C
J
G
Cut-Tree 𝑇𝑐
178

Divide-TD Algorithm
A
B
D
H
I
E
K
F
C
J
G
Add a virtual
node DF
SCC
Cut-Tree 𝑇𝑐
179

Divide-TD Algorithm
A
B
D
I
E
K
F
J
G
DF
S-Graph is a DAG
Divide the graph
into 5 parts
B
C
DF
H
𝐺1
𝐺2 𝐺3 I K𝐺4
𝐺0
180

Merge Algorithm
 According to the properties, the DFS-Tree for
subgraphs are 𝑇0 , 𝑇1 ,…,𝑇𝑝, there exists a DFS-Tree T
with 𝑉 𝑇 = 1≤𝑖≤𝑝 𝑉(𝑇𝑖) and 𝐸 𝑇 = 1≤𝑖≤𝑝 𝐸(𝑇𝑖).
 Only need to organize 𝑇𝑖 in the merged tree such that
the result tree 𝑇 is a DFS-Tree.
 Since S-graph 𝐺 is a DAG in the division procedure,
we can topological sort 𝐺 and organize 𝑇𝑖 according to
the topological order.
 Remove virtual nodes 𝑣 and add edges from the father
of 𝑣 to the children of 𝑣.
 It can be proven that the result tree is a DFS-Tree.
181

Merge Algorithm
A
B
D
H
I
E
K
F
J
G
𝐺0
DF
B
C
DF
H
𝐺1
𝐺2 𝐺3
Topological sort 𝐺0
Removing S-edges
and find the DFS-
Tree
182

Merge Algorithm
A
B
D
H
I
E
K
F
J
G
𝑇0
DF
B
C
DF
H
𝑇1
𝑇2 𝑇3
Merge trees
according to the
order
183

Merge Algorithm
A
B
D
I
E
K
F
J
G
𝑇0
C
H
𝑇1
𝑇2 𝑇3
184

Performance Studies
 Test on a PC with Intel Core2 Quard 2.66GHz
CPU and 4GB memory running Windows 7
Enterprise
 Disk Block Size: 64KB
185

|V| |E| Average
Degree
Wikilinks 25,942,246 601,038,301 23.16
Arabic-2005 22,744,080 639,999,458 28.14
Twitter-2010 41,652,230 1,468,365,182 35.25
WEBGRAPH-
UK2007
105,895,908 3,738,733,568 35.00
Four Real Data Sets
186

Web-graph Results
 Memory size 2GB
 Varying node size percentage
187

 We study the I/O efficient DFS algorithms for a large
graph.
 We analyze the drawbacks of existing semi-external
DFS algorithm.
 We discuss the challenges and four properties in
order to find a divide & conquer approach.
 Based on the properties, we design two novel graph
division algorithms and a merge algorithm to reduce
the cost to DFS the graph.
 We have conducted extensive performance studies to
confirm the efficiency of our algorithms.
Conclusion
188

 We also believe that there are many things we need
to do on large graphs or big graphs.
 We know what we have known on graph processing.
 We do not know yet what we do not know on graph
processing.
 We need to explore many directions such as
 parallel computing
 distributed computing
 streaming computing
 semi-external/external computing.
Some Conclusion Remarks
189

I/O Cost Minimization
 If there does not exist node 𝑢 for 𝑣 that 𝑆𝐶𝐶(𝑢, 𝐺𝑖) =
𝑆𝐶𝐶(𝑣, 𝐺𝑖), 𝑣 can be removed from 𝐺𝑖+1.
 For a node 𝑣, if 𝑛eigh𝑏𝑜𝑢𝑟(𝑣, 𝐺𝑖) ⊆ 𝑉𝑖+1, 𝑣 can be
removed from 𝑉𝑖+1.
 The I/O complexity is
𝑂(𝑠𝑜𝑟𝑡 𝑉𝑖 + 𝑠𝑜𝑟𝑡 𝐸𝑖 + 𝑠𝑐𝑎𝑛(|𝐸𝑖|))
190

B
H
C D
G
E
A
IF
This edge makes all
nodes in a partial
SCC the same order.
Another Example
 Keep tree structure edges in memory.
 Only concern the depth of nodes
reachable but not the exact positions.
 Early-acceptance: merging SCCs
partially whenever possible does
not affect the order of others.
 Early-rejection: prune non-SCC
nodes when possible.
 Prune the node “A”.
 In Memory: Black edges
 On Disk: Red edges
191

B
I
C
D
H
E
A
JG
 No need to remember
𝑑𝑙𝑖𝑛𝑘(𝑢, 𝑇).
 Merge nodes of the
same order when an
edge (𝑢, 𝑣) is found,
where 𝑣 is an
ancestor of 𝑢 in 𝑇.
 Smaller graph size,
smaller I/O Cost
KF
Memory: 2 × |𝑉|
Early-Acceptance
Early-Acceptance
Optimization: Early Acceptance
192

Performance Studies
Vary Degree
193

Jeffrey xu yu large graph processing

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