Debunking the Myths of Influence Maximizationmitdbg.github.io/nedbday/2017/talks/galhotr.pdf ·...
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Debunking the Myths of InfluenceMaximization
Akhil Arora1, Sainyam Galhotra1, Sayan Ranu
[email protected] of Massachusetts, Amherst
January 27, 2017NEDB, 2017
1The first two authors have contributed equally to this work.Influence Maximization January 27, 2017 NEDB, 2017 1 / 19
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Information Propagation2: Need for Modelling??
Many real-world processes can be interpreted using conceptsfrom information propagationFor example: Spread of Diseases
2Propagation/Flow/Spread/Diffusion, would be used interchangeablyInfluence Maximization January 27, 2017 NEDB, 2017 2 / 19
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Need for Modelling??
Traffic Congestion and its propagation
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Other Applications
Using the word-of-mouth effect for:
Viral Marketing: Product/Topic/Event promotionManaging Celebrity/Political campaigns
Detect and Prevent Outbreaks/Epidemics/RumoursMany more . . .
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Other Applications
Using the word-of-mouth effect for:Viral Marketing: Product/Topic/Event promotionManaging Celebrity/Political campaigns
Detect and Prevent Outbreaks/Epidemics/RumoursMany more . . .
Influence Maximization January 27, 2017 NEDB, 2017 4 / 19
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Other Applications
Using the word-of-mouth effect for:Viral Marketing: Product/Topic/Event promotionManaging Celebrity/Political campaigns
Detect and Prevent Outbreaks/Epidemics/RumoursMany more . . .
Influence Maximization January 27, 2017 NEDB, 2017 4 / 19
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Existing Information Propagation models
Independent Cascade (IC) and Weighted Cascade (WC) ModelsLinear Threshold (LT) ModelOther models – Heat Diffusion etc.
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Existing Information Propagation models
Independent Cascade (IC) and Weighted Cascade (WC) ModelsLinear Threshold (LT) ModelOther models – Heat Diffusion etc.
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Existing Information Propagation models
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Existing Information Propagation models
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Existing Information Propagation models
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The Influence Maximization (IM) Problem
Input: A graph G, an information-diffusion model IConstraints: The budget (k = |S|) defining the size of the seed-set
Task: Identify the set of most-influential nodes in a networkMaximize σ(S) = E[F(S)]: Expected number of nodes active at theend, if set S is targeted for initial activation
Tractability: The IM problem is NP-hard. Need for ApproximateSolutions!The spread function σ is Monotone and Submodular, thus, asimple GREEDY algorithm provides the best possible (1− 1/e)approximation
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The Influence Maximization (IM) Problem
Input: A graph G, an information-diffusion model IConstraints: The budget (k = |S|) defining the size of the seed-setTask: Identify the set of most-influential nodes in a network
Maximize σ(S) = E[F(S)]: Expected number of nodes active at theend, if set S is targeted for initial activation
Tractability: The IM problem is NP-hard. Need for ApproximateSolutions!The spread function σ is Monotone and Submodular, thus, asimple GREEDY algorithm provides the best possible (1− 1/e)approximation
Influence Maximization January 27, 2017 NEDB, 2017 9 / 19
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The Influence Maximization (IM) Problem
Input: A graph G, an information-diffusion model IConstraints: The budget (k = |S|) defining the size of the seed-setTask: Identify the set of most-influential nodes in a network
Maximize σ(S) = E[F(S)]: Expected number of nodes active at theend, if set S is targeted for initial activation
Tractability: The IM problem is NP-hard. Need for ApproximateSolutions!The spread function σ is Monotone and Submodular, thus, asimple GREEDY algorithm provides the best possible (1− 1/e)approximation
Influence Maximization January 27, 2017 NEDB, 2017 9 / 19
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Need for benchmarking? Wide variety of techniques
MC Simulation1. Run MC Simulation from each node to estimate its spread.2. Exploit submodularity to prune out nodes with low spread
SamplingStore a DAG for a sample of nodes and use it to estimate influ-ence
Approximate ScoringEstimate the influence of the nodes using heuristics as exactcomputation is #P - hard
Influence Maximization January 27, 2017 NEDB, 2017 10 / 19
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Need for benchmarking? Wide variety of techniques
MC Simulation1. Run MC Simulation from each node to estimate its spread.2. Exploit submodularity to prune out nodes with low spread
SamplingStore a DAG for a sample of nodes and use it to estimate influ-ence
Approximate ScoringEstimate the influence of the nodes using heuristics as exactcomputation is #P - hard
Influence Maximization January 27, 2017 NEDB, 2017 10 / 19
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Need for benchmarking? Wide variety of techniques
MC Simulation1. Run MC Simulation from each node to estimate its spread.2. Exploit submodularity to prune out nodes with low spread
SamplingStore a DAG for a sample of nodes and use it to estimate influ-ence
Approximate ScoringEstimate the influence of the nodes using heuristics as exactcomputation is #P - hard
Influence Maximization January 27, 2017 NEDB, 2017 10 / 19
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Need for benchmarking? Wide variety of techniques
MC Simulation1. Run MC Simulation from each node to estimate its spread.2. Exploit submodularity to prune out nodes with low spread
SamplingStore a DAG for a sample of nodes and use it to estimate influ-ence
Approximate ScoringEstimate the influence of the nodes using heuristics as exactcomputation is #P - hard
Influence Maximization January 27, 2017 NEDB, 2017 10 / 19
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Need for benchmarking? : Ambiguities
Existing Literature: Use IC, WC interchangeablyActual scenario: Varied behaviour in terms of the spread of seednodes, efficiency and scalability aspects of different techniques.
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Figure: IMM (ε = 0.5) for Orkut dataset
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Need for benchmarking? : Ambiguities
State-of-the-art technique in one aspect behaves the worst inanother aspect of the problem.
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Important Questions
How to choose the most appropriate IM technique in a givenspecific scenario?
What does it really mean to claim to be the state-of-the-art?Are the claims made by the recent papers true?
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Important Questions
How to choose the most appropriate IM technique in a givenspecific scenario?What does it really mean to claim to be the state-of-the-art?
Are the claims made by the recent papers true?
Influence Maximization January 27, 2017 NEDB, 2017 13 / 19
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Important Questions
How to choose the most appropriate IM technique in a givenspecific scenario?What does it really mean to claim to be the state-of-the-art?Are the claims made by the recent papers true?
Influence Maximization January 27, 2017 NEDB, 2017 13 / 19
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Our Framework
Generic framework applicable on all techniques.Unified approach to tune the external parameters.
Setu
p
Algorithms Propagation Models Datasets Configurations
IM F
ram
ew
orkSeed Selection Spread Computation
Monte-Carlo (MC)
Simulations
Insig
hts
Eva
luati
on
Quality
Efficiency Scalability
Node
Ord
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Influen
ce
Estim
atio
n
Convergence
Robustness
Upda
te
Data
Str
uctu
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#Parameters
Convergence calculationSelect the parameters which provide the best quality withouthampering the efficiency and scalability of the technique.
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Our Framework
Generic framework applicable on all techniques.Unified approach to tune the external parameters.
Setu
p
Algorithms Propagation Models Datasets Configurations
IM F
ram
ew
orkSeed Selection Spread Computation
Monte-Carlo (MC)
Simulations
Insig
hts
Eva
luati
on
Quality
Efficiency Scalability
Node
Ord
ering
Influen
ce
Estim
atio
n
Convergence
Robustness
Upda
te
Data
Str
uctu
res
#Parameters
Convergence calculationSelect the parameters which provide the best quality withouthampering the efficiency and scalability of the technique.
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Myths
IMM is always faster than TIM+?
Model ε (TIM+) ε (IMM) Time (TIM+) Time (IMM) GainIC 0.05 0.05 8582.23 829.6 10.3xLT 0.35 0.1 0.79 1.2 0.65x
Table: Comparison of convergence parameter and running time (secs) for IMM and TIM+
over HepPH dataset for 200 seeds
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Myths
IMM is always faster than TIM+?
Model ε (TIM+) ε (IMM) Time (TIM+) Time (IMM) GainIC 0.05 0.05 8582.23 829.6 10.3xLT 0.35 0.1 0.79 1.2 0.65x
Table: Comparison of convergence parameter and running time (secs) for IMM and TIM+
over HepPH dataset for 200 seeds
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Myths
CELF++ is the fastest IM technique in the MC estimationparadigm?
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Myths
CELF++ is the fastest IM technique in the MC estimationparadigm?
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e (
in m
in)
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(e) Nethept (WC)
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Ru
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ing
Tim
e (
in m
in)
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CELFCELF++
(f) Nethept (LT)
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Myths
SIMPATH is faster the LDAG?
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in m
in)
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LDAGSIMPATH
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Myths
SIMPATH is faster the LDAG?
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e (
in m
in)
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#Seeds (k)
LDAGSIMPATH
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Conclusions
No technique is the best on all aspects of IM.
Quality
EfficiencyMemory Footprint
TIM/IMMPMC
CELF/CELF++EaSyIM
ME
IRIEIMRank
StaticGreedy
LDAGSIMPATH
(k) Qualitative catego-rization of IM techniques
(l) Which technique to choose & when?
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Thanks!
For more details, please refer :A. Arora, S. Galhotra, S. Ranu. Debunking the Myths of InfluenceMaximization : An In-Depth Benchmarking Study. SIGMOD 2017
Influence Maximization January 27, 2017 NEDB, 2017 19 / 19