Some Computational Challenges in Mining Social Media
- Huan LiuData Mining and Machine Learning Lab, Arizona State University, Tempe, AZ, USA
People of all walks of life use social media for communications and
networking. Their active participation in numerous and diverse online
activities continually generates massive amounts of social media data.
This undoubtedly “big” data presents new challenges to data mining,
including how to select salient features for social media data with
varied relations, how to assess user vulnerability, and how to ensure
that patterns discovered from social media data are valid when no
ground truth is available. We will illustrate the intricacies of
social media data, present original social-computing problems,
deliberate approaches to mining social media data to gain insight from
real-world applications and deepen our understanding, and exploit
unique characteristics of social media data in developing novel
algorithms and computational tools for social media mining.
Dr. Huan Liu is a professor of Computer Science and Engineering at
Arizona State University. He obtained his Ph.D. in Computer Science at
University of Southern California and B.Eng. in EECS at Shanghai
JiaoTong University. He was recognized for excellence in teaching and
research in Computer Science and Engineering at Arizona State
University. His research interests are in data mining, machine
learning, social computing, and artificial intelligence, investigating
problems that arise in real-world applications with high-dimensional
data of disparate forms. His well-cited publications include books,
book chapters, encyclopedia entries as well as conference and journal
papers. He serves on journal editorial/advisory boards and numerous
conference program committees. He is a Fellow of IEEE and a member of
several professional societies.
Unraveling daily human mobility motifs
- Marta GonzalezMIT, Cambridge, MA, USA
Time scales differentiate human mobility. While the mechanism for
longtime scales has been studied, the underlying mechanism on the
daily scale is still unrevealed. Here, we uncover the mechanism
responsible for the daily mobility patterns by analyzing the temporal
and spatial trajectories of thousands of persons as individual
networks. Using the concept of motifs from network theory, we find
only 17 unique networks are present in daily mobility and they follow
simple rules. These networks, called here motifs, are sufficient to
capture up to 90 per cent of the population in surveys and mobile
phone datasets for different countries. Each individual exhibits a
characteristic motif, which seems to be stable over several months.
Consequently, an analytically tractable framework for Markov chains
can reproduce daily human mobility by modeling periods of
high-frequency trips followed by periods of lower activity as the key
Gilbert Winslow Career Development Assistant Professor of Civil and
Environmental Engineering, MIT joint with Engineering Systems (ESD)
and the Operations Research Center (ORC). Before joining MIT, she was
research associate at the Center for Complex Network Research
(Barabási Lab) at Northeastern University, where she joined right
after her PhD at Stuttgart Universität, Germany, in 2006.
Marta’s research interests and direction are mainly aimed at advancing
the understanding of the laws and principles that characterize human
behavior and result in collective social phenomena. More specifically
her work falls into the category of complex networks and statistical
physics applied to social dynamic systems. Current research explores
human mobility patterns using mobile phone communication; data mining
combined with geographic information systems (GIS), and urban
transportation models (see details at: http://web.mit.edu/humnet/index.shtml
Large Graph Mining - Patterns, explanations, and cascade analysis
- Christos FaloutsosCarnegie Mellon University, Pittsburgh, PA, USA
What do graphs look like? How do they evolve over time?
How does influence/news/viruses propagate, over time?
We present a long list of static and temporal laws,
and some recent observations on real graphs.
We show that fractals and self-similarity can explain
several of the observed patterns,
and we conclude with cascade analysis and a surprising
result on virus propagation and immunization.
Christos Faloutsos is a Professor at Carnegie Mellon University.
He has received the Presidential Young Investigator Award by
the National Science Foundation (1989),
the Research Contributions Award in ICDM 2006,
the SIGKDD Innovations Award (2010),
nineteen "best paper" awards
(including two "test of time" awards),
and four teaching awards.
He is an ACM Fellow,
he has served as a member of the executive committee of SIGKDD;
he has published over 200 refereed articles, 11 book chapters
and one monograph. He holds six patents and
he has given over 35 tutorials and over 15 invited distinguished lectures.
His research interests include data mining
for graphs and streams, fractals, database performance,
and indexing for multimedia and bio-informatics data.