1. MAGNET COMMUNITY
IDENTIfiCATION ON
SOCIAL NETWORKS
University of Illinois at Chicago, USA

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. INTRODUCTION

Magnet communities are such communities
that draw significantly more attention than
others even if they are all about the same topic.

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. INTRODUCTION

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. 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. MAGNET COMMUNITY
IDENTIFICATION FRAMEWORK


9. MAGNET COMMUNITY
IDENTIFICATION FRAMEWORK

10. MAGNET COMMUNITY
IDENTIFICATION FRAMEWORK

Attractiveness computation framework
M =( m1 , m2 ,..., mk )
M = f ( FV , FE , M )
M∗

Our objective function：




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. MAGNET COMMUNITY
IDENTIFICATION FRAMEWORK

Concrete formula of magnet community
ranking framework

At least one of the following conditions hold

13. MAGNET COMMUNITY
IDENTIFICATION FRAMEWORK

14. MAGNET COMMUNITY
IDENTIFICATION FRAMEWORK

15. MAGNET COMMUNITY
IDENTIFICATION FRAMEWORK

16. MAGNET COMMUNITY
IDENTIFICATION FRAMEWORK

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. 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. EVALUATION

Case studies --- IT

20. EVALUATION

Case studies --- Financial

21. EVALUATION

Overall Correctness measures

22. EVALUATION

Parameter sensitivity

two parameters α and μ

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. JOINT TOPIC MODELING
FOR EVENT
SUMMARIZATION
ACROSS
NEWS AND SOCIAL
MEDIA STREAMS
Qatar Computing Research Institute
Qatar Foundation Doha, Qatar

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. 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. 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. PROBLEM DEFINITION

29. PROBLEM DEFINITION

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

31. LEARNING COMPLEMENTARY
RELATION

Measuring Commonality and Difference

32. LEARNING COMPLEMENTARY
RELATION

Cross-collection Topic-Aspect Model (ccTAM)

35. Inference

Infer the general topic-word distribution φ z

36. Inference

The collection-specific topic-word distribution
φc z

37. 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.

38. GENERATE COMPLEMENTARY
SUMMARIES

Jumping Probability

39. GENERATE COMPLEMENTARY
SUMMARIES

Sentences/Tweets Co-ranking

Summary Generation
Summary-level complementarity
 Sentence-level complementarity


40. EXPERIMENTS AND
RESULTS

Data Collecting

41. 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)


42. EXPERIMENTS AND
RESULTS

Results and Discussions

43. EXPERIMENTS AND
RESULTS

Results and Discussions

44. EXPERIMENTS AND
RESULTS

Example of output summaries

45. 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