Presented at Machine Learning in Network Science, co-located with NetSci'19, VT. Abstract: We introduce a generative/discriminative mechanism to predict the temporal dynamics of information cascade with the support of probabilistic models and Long-Short Term Memory (LSTM) neural networks. Our approach is to train a machine-learning algorithm to act as a filter for identifying realistic cascades for a particular social platform from a large pool of generated cascades. Our goal is to select the most realistic cascade with an accurate de-construction of user activity time-line. As an example in Twitter, we predict which user performs a retweet, and when she does such, in addition to the underlying cascade structure.

1. A Generative/ Discriminative
Approach to De-construct
Cascading Events
Sameera Horawalavithana, John Skvoretz, and
Adriana Iamnitchi
University of South Florida
Machine Learning in Network Science (MLNS ‘19)

2. Diffusion in Social
Networks
• News, opinions, rumors, fads,
urban legends, …
• Virus, disease propagation
• Change in social priorities:
smoking, recycling
• Saturation news coverage:
topic diffusion among
bloggers
• Internet-energized political
campaigns
• ….
“Viral Marketing” -- Patterns of Influence in a Recommendation Network
2

3. Information
Cascade
• A diffusion process initiates activations through an
underlying network
• Activations leave a trace – cascade
• E.g., re-tweet chain, online conversation
3

4. Information
Cascade
4

5. Cascading Events
• who did what to whom when where
• E.g., @Sameera mentions
@Anda in the Retweet “RT:
TheHackerNews: Biggest…” at
2019:03:27 08:23
5

6. Collective Behavior in
Cascading Events
• “Information do no spread
in isolation, independent of
all other information
currently diffusion in the
network” Myers et al.
• Competing or Cooperative
cascades
• E.g., Reddit conversations on
Bitcoin scaling debate
Introducing Bitcoin Cash(BCH) that splits Bitcoin’s
(BTC) original blockchain via a hard fork — a
consequence of popular Bitcoin scaling debate
“We need off-chain
scaling and on-chain
scaling.Our stupid
politics is hurting
Bitcoin because
we're separating the
two necessary parts
of the overall
solution.”
Discussion on the
advantages of Bitcoin
Cash (BCH) on
scalability — “Bitcoin
Cash (BCH) totally fixes
the quadratic scaling of
sighash operations bug”
..“I miss those
days man and
got tired of
these scaling
topics.”..
“Don't fall for the big
block argument while we
are so close to have the
scaling solution.”
“..but he made
a mistake in
the scaling
debate”
6

7. Probabilistic Cascade
Generation
• Branching Model
• E.g., person transmits
the disease to each
people she meets
independently with a
probability p, an
infected person meets k
(new) people while she
is contagious
• Used to accurately
model cascade subtree
structure, Cheng et al.
7

8. Probabilistic
Generation of a
Cascade Pool
• Cascade pool consists of a set
of cascades
• Each cascade originated from
a given set of initial seeds
(e.g., tweet, or a post in
Reddit),
• Apply branching model for
each seed separately.
• Based on empirically-
benchmarked distributions;
• E.g., conditional degree
distribution by the level
• Branching model generates
only the cascade structure.
8

9. Cascading Events:
Inferring Users
• How do we map users to the
generated cascade trees?
• Overlay the cascade structure on
top of an underlying diffusion
network
• Given a user in an underlying
network, we take a random
weighted sample of user’s
incoming edges
• Underlying Networks:
• Reddit, shared subreddit network
weighted by direct interactions
• Twitter, follower network where
edges are weighted by the
number of previous direct
interactions
ua
ub
uc
ud
ue
uf
0.45
0.35
0.2
1
0.5
0.5
1
post
comment 1
comment 2
comment 3

10. Cascading Events:
Inferring Time
• How do we estimate the
time of adoption
(timestamp of the comment
or retweet)?
• Given a size of the cascade,
we randomly sample a
sequence of long propagation
delays in a previous cascade
of a similar size
10
post
comment 1
comment 2
comment 3
post
comment 1
comment 2
comment 3
2017/08/01 13:00
+ 00:35
+ 00:40
+ 00:55
2018/02/01 23:00
+ 00:35
+ 00:40
+ 00:55
Previously
seen
cascade
Simulated
cascade

11. Problem..
11
The probabilistic-based cascade generation
considers the structure, authorship and
timing as mutually independent of each
other during the evolution of a cascade
Largely constrained by the prior knowledge,
and does not provide any predictive power
for attributing user and timing information.
How to capture the dependence between
user, timing and structure in a cascade?

12. Generative/
Discriminative
Approach
12
How to capture the dependence between
user, timing and structure in a cascade?
Task is to accurately predict a pool of
cascades with user and timing information
• Generate N pools of cascades using probabilistic
models
• Reconstruct a pool of cascades using genetic
algorithm
• We develop a fitness function based on the
output of two trained LSTM models
• The models assess how realistic to observe the
generated cascade in two spatio-temporal
properties (i.e., branching and propagation
delay)?

13. Discriminative
Models
13
Two Prediction Objectives:
• Predict the branch and leaf nodes
• critical factor to accurately predict the cascade
structure
• Predict the early and late adopters
• critical factor to identify the temporal position of a
node in the cascade
• E.g., assume node 1 appeared in timestep x, and node 2
and 3 appeared in timestep x+1 and x+2 respectively:
• node 2 is a branch, and an earlier adopter
(propagation delay = 1) than node 3 (propagation
delay = 2)

14. Cascade
Representation
and Features
14

15. Re-construction of a
Cascade Pool
• How realistic is the generated
cascade with the user and
timing information?
• Terminology:
• “Gene” – individual cascade
• “Individual” – a pool of
cascades
• “Population” – a set of cascade
pools

16. Fitness Score
16
How realistic is the generated cascade with the attached user
and timing information?
seed
Nx
Ny
Nz
1
0
0
1
1
0
Generated Cascade
Branch LSTM
Delay LSTM
0
0
1
0
1
1
T10
T1
T2
Ny T1
Nz T2
Nx T10
Branch vector Delay vector

17. Fitness Score
17
Genetic Test
• The objective is to come up with an individual (a pool of
cascades) which outperforms any existing pool in the
initial search space.
• We feed each generated cascade into two machine-
learning models:
• Predict a binary vector that represents branch/leaf
using the branch discriminator model,
• Predict a binary vector that represents the early/late
adopters using the delay discriminator model.
• We consider these two binary vectors as the inferred
ground truth to assess the generated cascade.
• Report AUC comparing the inferred ground truth with
the simulated cascade output.

18. Performance
(prediction)
• Two classification tasks
• Predict branch or leaf node
• Predict early or late adopter
18
Training Data Test Data time
2015 January 2016 December 2017 August

19. Performance
(simulation)
• Simulation Task
• Given a set of initial seeds (e.g., tweet, or a post in Reddit), predict the full
cascade structure with user and timing information
• E.g., who did what to whom when where
19
Training Data Test Data
2015 January 2017 July 2017 August
Simulation

20. Acknowledgement
20
Funded by DARPA SocialSim Program
and the Air Force Research
Laboratory
Data: Leidos, Netanomics
Evaluation: Pacific Northwest
National Laboratory

21. Thank you.
Email: sameera1@mail.usf.edu
https://samtube405.github.io/_profile
21