comprehensive temporal diffusion modeling with correlated text component xie yiran 2014.5.13

COMPREHENSIVE TEMPORAL DIFFUSION MODELING WITH CORRELATED TEXT COMPONENT

Xie Yiran

2014.5.13

CONTENTS

• Introduction

• Related Work

• Basic Model

• Joint Model

• Experimental Evaluation

CONTENTS

• Introduction

• Related Work

• Basic Model

• Joint Model

• Experimental Evaluation

INTRODUCTION

Lots of messages are published on the Social medias

Including Correlated texts

CORRELATED TEXTS

Re-sharing texts (71%)

Re-creating texts (29%)

CORRELATED TEXTS

Re-sharing texts (78%)

Re-creating texts (22%)

CORRELATED TEXTS

• The volume of messages changes with the time goes by

CORRELATED TEXTS

• How to model temporal diffusion with the correlated texts?

• And help prediction /recommendation /ad … on micro-blog platform

CONTENTS

• Introduction

• Related Work

• Basic Model

• Joint Model

• Experimental Evaluation

RELATED WORK

• LIM: Modeling information diffusion in implicit network

• SPIKEM: Rise and fall patterns of information diffusion

• SSM: modeling and predicting behavioral dynamics on the web

• Analytical model for temporal variation

• Complicated implicit information

• Cannot make good use of correlated texts

• Meme-tracker: Meme-tracking and the dynamics of the news cycle

• Global model for temporal variation

• Theoretical

RELATED WORK

• Most current work

• employ the simple collective counting methods

• ignore the essential characteristics of temporal variations

• cannot make good use of correlated texts

CONTENTS

• Introduction

• Related Work

• Basic Model

• Joint Model

• Experimental Evaluation

BASIC MODEL

• Social media : scale-free network

• Growth : start with m nodes and add new nodes

• Preferential attachment : new nodes prefer to attach to big nodes

• ∏(k)~kγ

• Initial attractiveness: a new node attaches to a isolated node

• ∏(k)~(A+k)γ

• Growth constraints: real network has finite lifetime or finite edge capacity

• Gradual aging : ∏(k)~(A+k)γ *t-β

BASIC MODEL

• xt~ (A+xt-1)γ *t-β

• γ ~ 2?

• β ~ 1.2

JOINT MODEL

• Growth: start with re-creating nodes, add re-sharing nodes.

• Re-creating action: xt~ (A+xt-1)γ* B * t-α

• Re-sharing action: xt~ (A+xt-1)γ *t-β

JOINT MODEL

• Periodicity p(t)

• xt~ [(A+xt-1)γ *(t-α+B*t-β)] * p(t)

CONTENTS

• Introduction

• Related Work

• Micro-blog Data Characteristics

• Basic Model

• Joint Model

• Experimental Evaluation

EXPERIMENTAL EVALUATION

• Matching data

EXPERIMENTAL EVALUATION

• Matching patterns

EXPERIMENTAL EVALUATION

• Prediction

TO BE CONTINUED

• Motivation and Meaning

• Deduction of model

• Comparison in experiment part