MAGNET COMMUNITY
IDENTIfiCATION ON
SOCIAL NETWORKS
University of Illinois at Chicago, USA
ABSTRACT




1.

2.

A magnet community is such a community that
attracts significantly more people’s interests and
atten...
INTRODUCTION


Magnet communities are such communities
that draw significantly more attention than
others even if they are...
INTRODUCTION

1.

2.



Importance:
help people understand the trends of their
domains.
help people make decisions when ...
INTRODUCTION
INTRODUCTION

1.

2.

3.


Challenges:
how to extract features from these
heterogeneous sources of impacting factors
of ...
contributions






A new direction on social network analysis,
namely
magnet community identification.
One definition of...
MAGNET COMMUNITY
IDENTIFICATION FRAMEWORK

MAGNET COMMUNITY
IDENTIFICATION FRAMEWORK
MAGNET COMMUNITY
IDENTIFICATION FRAMEWORK



Attractiveness computation framework
M =( m1 , m2 ,..., mk )
M = f ( FV , FE...
MAGNET COMMUNITY
IDENTIFICATION FRAMEWORK


Attractiveness features


Standalone features



Attention migrating matrix...
MAGNET COMMUNITY
IDENTIFICATION FRAMEWORK


Concrete formula of magnet community
ranking framework



At least one of th...
MAGNET COMMUNITY
IDENTIFICATION FRAMEWORK
MAGNET COMMUNITY
IDENTIFICATION FRAMEWORK
MAGNET COMMUNITY
IDENTIFICATION FRAMEWORK
MAGNET COMMUNITY
IDENTIFICATION FRAMEWORK
EVALUATION


Data collection and features extraction

•
•

•

Data collection
www.linkedin.com
Standalone features : a c...
EVALUATION


Feature extraction
•
•
•




industry – count how many people flow into it and
out of it, using company lev...
EVALUATION


Case studies --- IT
EVALUATION


Case studies --- Financial
EVALUATION


Overall Correctness measures
EVALUATION


Parameter sensitivity


two parameters α and μ
CONCLUSION






提出了magnet community identification 的研究
方向
对问题定义和举例、研究意义、挑战、目标、
传统思路的缺陷等有非常充分的说明
算法思路清晰,提出了三个特性,量化成目标
函...
JOINT TOPIC MODELING
FOR EVENT
SUMMARIZATION
ACROSS
NEWS AND SOCIAL
MEDIA STREAMS
Qatar Computing Research Institute

Qata...
ABSTRACT


1.

2.

a novel unsupervised approach based on topic
modeling to summarize trending subjects by
jointly discov...
INTRODUCTION




News -- well-crafted, fact-oriented long stories
written by professionals based on the latest
past even...
INTRODUCTION









contributions
A novel problem of generating complementary
summaries
a principled measure to ass...
PROBLEM DEFINITION
PROBLEM DEFINITION
LEARNING COMPLEMENTARY
RELATION



commonality and difference
general model and media-specific model.
LEARNING COMPLEMENTARY
RELATION



Measuring Commonality and Difference
LEARNING COMPLEMENTARY
RELATION


Cross-collection Topic-Aspect Model (ccTAM)
Inference


Infer the general topic-word distribution φ z
Inference


The collection-specific topic-word distribution
φc z
GENERATE COMPLEMENTARY
SUMMARIES




G =( N ∪ T, E )
N = { n1 ,n2, ··· ,nmn }, T = { t1 ,t2 , ··· ,tnt }
E = { ( p ( ni...
GENERATE COMPLEMENTARY
SUMMARIES


Jumping Probability
GENERATE COMPLEMENTARY
SUMMARIES


Sentences/Tweets Co-ranking



Summary Generation
Summary-level complementarity
 Sen...
EXPERIMENTS AND
RESULTS


Data Collecting
EXPERIMENTS AND
RESULTS


gold-standard summaries
The news summaries: English Wikipedia and
Wikinews
 Tweets summaries
...
EXPERIMENTS AND
RESULTS


Results and Discussions
EXPERIMENTS AND
RESULTS


Results and Discussions
EXPERIMENTS AND
RESULTS


Example of output summaries
CONCLUSIONS






提出了用tweets补充news summarization的想
法
提出了补充度的概念,并介绍了一种度量
tweets补充度的方法。
算法结合了很多已有模型,比如topic-aspect
model,...
Magnet community identification on social networks
Magnet community identification on social networks
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Magnet community identification on social networks

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Magnet community identification on social networks

  1. 1. MAGNET COMMUNITY IDENTIfiCATION ON SOCIAL NETWORKS University of Illinois at Chicago, USA
  2. 2. ABSTRACT   1. 2. A magnet community is such a community that attracts significantly more people’s interests and attentions than other communities of similar topics. the study of magnet community identification problem. We observe several properties of magnet communities. We formalize these properties with the combination of community feature extraction into a graph ranking formulation.
  3. 3. INTRODUCTION  Magnet communities are such communities that draw significantly more attention than others even if they are all about the same topic.
  4. 4. INTRODUCTION  1. 2.  Importance: help people understand the trends of their domains. help people make decisions when joining communities. Our goal : Given communities in a domain, we want to rank them based on their attractiveness to people among the communities of that domain.  In the end, the top ranked communities are the ones people tend to adhere to. 
  5. 5. INTRODUCTION
  6. 6. INTRODUCTION  1. 2. 3.  Challenges: how to extract features from these heterogeneous sources of impacting factors of a community’s attractiveness. how to combine all heterogeneous information into a unified ranking model. Noise handling. common properties: attention flow  Attention quality  persistence of people’s attention 
  7. 7. contributions    A new direction on social network analysis, namely magnet community identification. One definition of magnet communities by identifying their properties. We demonstrate the effectiveness of our framework on a particular domain of magnet community identification, namely company’s employee magnet community identification.
  8. 8. MAGNET COMMUNITY IDENTIFICATION FRAMEWORK 
  9. 9. MAGNET COMMUNITY IDENTIFICATION FRAMEWORK
  10. 10. MAGNET COMMUNITY IDENTIFICATION FRAMEWORK  Attractiveness computation framework M =( m1 , m2 ,..., mk ) M = f ( FV , FE , M ) M∗  Our objective function:   
  11. 11. MAGNET COMMUNITY IDENTIFICATION FRAMEWORK  Attractiveness features  Standalone features  Attention migrating matrix as dependency features • • an attention migrating matrix:D=(dij)k*k The attention vector, A =( ai )k∗1 = D · e • • Dependency features of communities:
  12. 12. MAGNET COMMUNITY IDENTIFICATION FRAMEWORK  Concrete formula of magnet community ranking framework  At least one of the following conditions hold
  13. 13. MAGNET COMMUNITY IDENTIFICATION FRAMEWORK
  14. 14. MAGNET COMMUNITY IDENTIFICATION FRAMEWORK
  15. 15. MAGNET COMMUNITY IDENTIFICATION FRAMEWORK
  16. 16. MAGNET COMMUNITY IDENTIFICATION FRAMEWORK
  17. 17. EVALUATION  Data collection and features extraction  • • • Data collection www.linkedin.com Standalone features : a company’s revenue per employee, industry, location, age 39527 companies’ information in 142 industries
  18. 18. EVALUATION  Feature extraction • • •   industry – count how many people flow into it and out of it, using company level departure and arrival data. Locations -- popularities Founded year feature -- the number of companies founded for each year. Ranking performance Baseline Description • • • PageRank IT and financial The 2011 ideal employer ranking proposed by Universumglobal the 2011 most admired company ranking by Fortune
  19. 19. EVALUATION  Case studies --- IT
  20. 20. EVALUATION  Case studies --- Financial
  21. 21. EVALUATION  Overall Correctness measures
  22. 22. EVALUATION  Parameter sensitivity  two parameters α and μ
  23. 23. CONCLUSION    提出了magnet community identification 的研究 方向 对问题定义和举例、研究意义、挑战、目标、 传统思路的缺陷等有非常充分的说明 算法思路清晰,提出了三个特性,量化成目标 函数
  24. 24. JOINT TOPIC MODELING FOR EVENT SUMMARIZATION ACROSS NEWS AND SOCIAL MEDIA STREAMS Qatar Computing Research Institute Qatar Foundation Doha, Qatar
  25. 25. ABSTRACT  1. 2. a novel unsupervised approach based on topic modeling to summarize trending subjects by jointly discovering the representative and complementary information from news and tweets. topic modeling formalism by combining a twodimensional topic-aspect model and a cross-collection approach in the multidocument summarization literature. co-ranking the news sentences and tweets in both sides.
  26. 26. INTRODUCTION   News -- well-crafted, fact-oriented long stories written by professionals based on the latest past events Tweets – personalized, more opinionated freestyle short messages posted by the average persons in real time.
  27. 27. INTRODUCTION      contributions A novel problem of generating complementary summaries a principled measure to assess the extent of sentence-level complementarity A topic modeling approach called crosscollection topic-aspect model (ccTAM) that combines ccLDA and topic-aspect mixture model for precisely estimating the proposed complementary measure. a gold-standard dataset of complementary
  28. 28. PROBLEM DEFINITION
  29. 29. PROBLEM DEFINITION
  30. 30. LEARNING COMPLEMENTARY RELATION   commonality and difference general model and media-specific model.
  31. 31. LEARNING COMPLEMENTARY RELATION  Measuring Commonality and Difference
  32. 32. LEARNING COMPLEMENTARY RELATION  Cross-collection Topic-Aspect Model (ccTAM)
  33. 33. Inference  Infer the general topic-word distribution φ z
  34. 34. Inference  The collection-specific topic-word distribution φc z
  35. 35. GENERATE COMPLEMENTARY SUMMARIES    G =( N ∪ T, E ) N = { n1 ,n2, ··· ,nmn }, T = { t1 ,t2 , ··· ,tnt } E = { ( p ( ni | tj ) ,p ( tj | ni )) | i =1 , ··· ,mn ; j =1 , ··· ,nt } is the set of directed edges between two sets of nodes whose values are node-to-node jumping probabilities.
  36. 36. GENERATE COMPLEMENTARY SUMMARIES  Jumping Probability
  37. 37. GENERATE COMPLEMENTARY SUMMARIES  Sentences/Tweets Co-ranking  Summary Generation Summary-level complementarity  Sentence-level complementarity 
  38. 38. EXPERIMENTS AND RESULTS  Data Collecting
  39. 39. EXPERIMENTS AND RESULTS  gold-standard summaries The news summaries: English Wikipedia and Wikinews  Tweets summaries   Baseline Methods BL-0:LexRank  BL-1: KL-divergence(KLD)  BL-2: Cosine and language modeling(LM)  BL-3:LexRank+Complementarity(LexComp) 
  40. 40. EXPERIMENTS AND RESULTS  Results and Discussions
  41. 41. EXPERIMENTS AND RESULTS  Results and Discussions
  42. 42. EXPERIMENTS AND RESULTS  Example of output summaries
  43. 43. CONCLUSIONS    提出了用tweets补充news summarization的想 法 提出了补充度的概念,并介绍了一种度量 tweets补充度的方法。 算法结合了很多已有模型,比如topic-aspect model, cross-collection topic model, random walk model

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