1. Modeling Topic Diffusion in Scientific Collaboration Networks
Graph Data Management Lab, School of Computer Science
Fudan University, Shanghai, China
Which Topic will You Follow? Deqing Yang, Yanghua Xiao, Bo Xu, Hanghang Tong, Wei Wang and Sheng Huang
Introduction
Empirical Study
Motivations
Who are the most appropriate candidates to receive a call-for-paper or call-for-participation?
What session topics should we propose for a conference of next year?
Addressing these objectives, we study author’s topic-following behavior in Scientific Collaboration Network (SCN), i.e., an author follows others to publish papers of a given topic
Basic Idea
Scientific Collaboration Network
It is represented as
a graph where vertices
represent authors and
edges represent coauthor
relationships extracted
from DBLP dataset
It is a temporal graph
Gt, in which vertices and
edges increase as time t
elapses
Author’s topic-following behavior is the process of topic diffusion in social networks, which is driven by two typical ingredients, social influence and homophily
We try to find the variables that can precisely depict social influence and homophily in our scenario and use them to predict one author’s topic-following behavior in future
Challenges
How to distinguish social influence and homophily?
Topic definition and identification
Sample sparseness
Contributions
Uncover the effects of social influence and homophily on topic diffusion
Propose a Multiple Logistic Regression (MLR) model to predict author’s topic-following behavior
Extensive experiments prove our model’s excellent performance
Driving Forces of Topic-Following
U1: users affected by both social influence and homophily
U2: users affected only by social influence
U3: users affected only homophily
U4: users without any impact
Results:
Two forces are mixed to impact topic-following
Impacts are time-sensitive and decrease in an exponential way
Social Influence
An author adopts a topic with more probability when more of his neighbors have followed the topic before
x is affected neighbor number/proportion
p(x) is the probability that an author follows the topic
It is more probable for an author to follow the topics that have been adopted by his neighbors (direct propagation) who have coauthored more papers with him
Modeling Topic Diffusion in Scientific Collaboration Networks
Model
It is a two-category classification to predict whether an author will follow a given topic
Multiple Logistic Regression (MLR) model is feasible for our scenario, where the probability of topic-following is formalized as:
where xi is explanatory variable, αand β are parameters we should estimate by training
Baseline model
where a is the number of neighbors who have followed the topic
Explanatory Variables
Social Influence
An author u’s tendency to follow topic s in year t, is composed from all his neighbor v’s tendency to this topic, as well as considering their coauthor strengths
Homophily
We use topic similarity to depict the homophily among users in the context of topic-following
A 25-dim vector u represents an author’s topic history, each dimension is the number of his papers of a given topic
Then, topic similarity between user u and v can be defined as
W.r.t. those users who have followed topic s before t, i.e., we measure u’s homophily as
Then, the whole MLR model is
Y=π(x)=1, if u follows s or its related topics
Parameter Estimation
By maximum likelyhood against training set
β2 has larger Wald value than β1 indicating FTS (homophily) is more crucial to impact topic-following behavior than FSI
Model Evaluation
Metrics
Recall/sensitivity, specificity, precision, accuracy, AUC
Fβ, we set β=1.1 to favor recall a little
For topic XML
Area under ROC curve (AUC) is vs
For other 4 representative topics, MLR outperforms the baseline in both accuracy and Fβ
yangdeqing, ECML/PKDD2012, Bristol, UK