ICDM Trip Soon

This Sunday, I will be out of town for ICDM conference held in Miami, FL.

I will present my paper:

Uncovering Groups via Heterogeneous Interaction Analysis.


This work addresses the community detection problem when multiple different types of interactions are presented between the same set of users.

A more general case is that users registered at different social media sites. Can we somehow uncover the hidden community structure?

We show that using an integration based on structural features is more robust. For evaluation, I proposed a simple cross-dimension network validation scheme. Similar to cross validation. This could be used as a simple rule for evaluation in the future.

Of course, there are many interesting directions to pursue in the future. One important aspect is that some of the dimensions are noisy. Is it possible to identify them? Is this the same as tensor decomposition?

about collective behavior

Recently, I just submitted a magzine article talking about collective behavior to IEEE Intelligent systems based on my recent work published this year:
Relational Learning via Latent Social Dimensions  KDD-2009
Scalable Learning of Collective Behavior based on Sparse Social Dimensions CIKM-2009.

So what is collective behavior? I define collective behavior as behaviors when individuals are exposed in a social network environment. 

I found that different areas have quite different definitions. 
(1) . According to wiki (mostly written by sociologist),"The term "collective behavior" was first used by Robert E. Park, and employed definitively by Herbert Blumer, to refer to social processes and events which do not reflect existing social structure (laws, conventions, and institutions), but which emerge in a "spontaneous" way. "Collective behavior can be divided into four categories:

1. The Crowd

2. The public

3. The mass

4. The social movement

It is subtle to differentiate these four terms based on the name.

(2) On the other hand, in other field, such as artificial life

Swarm Intelligence in Data Mining

collective behavior refers to a group of agents which can be treated as an entity, which is commonly observed in bird flocks, ants and other animals.  There are several principles:

homogeneity (all the agents follow the same behavior model), locality (influenced only by neighbors), collision avoidance (avoid with nearby flock mates), velocity matching and flock centering.

Two popular methods are introduced in the chapter: particle swarm intelligence and ant colony optimization.  quite interesting ideas :)

(3) In data mining field, there is one paper talking about learning from collective behavior.

The setup is like oracle: suppose you have the luxury to observe the interaction and corresponding actions of a group of people and each person in the group follow a fixed policy,  then  how can we learn the policy and strategies so that when new situations arrive, we can predict the collective behavior?  The authors provide some theoretical bounds about the learnability. Unfortunatelly, evertying is synthetic and it is really difficult for me to figure out a proper scenario such that their setup might be true.

The nice part of this work is that at least two kinds of policies are studied:

• one is mimic your friend
• the other is try to differentiate from your neighbors
  

while the first one is well known, the 2nd strategy, as I believe existing in the real world, do not studied well in data mining or SNA. One way of achieving this is connecting those nodes that are two hops away and seperate them from 1-hop away neighbors as suggested in using ghost edges for sparsely labeled networks But when everyting is mixed, more work needs to be done.

Another key difference of this work from my article is that my article talks about "spatial" prediction of collective behavior (given some observations, predict the others within the same network), while this one talks about "temporal" prediction.

I think it is a great idea to combine these two aspects together.

(4) Adaptive networks and behavior. Concerning social networks and collective behavior, two directions are converging. One study the dynamics of networks, the other study the dynamics on networks. In reality, there two factors are evolving simultaneously. Social networks can evolve, so are collective behavior. How to capture these two factors in the modeling? 

This also relates to social influence and social selection and several papers are talking about this issue:

Modeling the co-evolution of network and behavior

Nonequilibrium phase transition in the coevolution of networks and opinions

Feedback effects between similarity and social influence in online communities

(5) Collective Attention investigate how a news or a resource attracts users' attentions. Does networks come into play? This can also be a further direction for investigation.

Anyway, I feel that this direction has many more issues to address. Also many challenges such as problem formulation, data collection, and evaluation. More in the near future :)
Just check my homepage

Change default paper size in tetex in Linux

Run command in sudo mode:

texconfig

Tips in research

writing is like a hour glass.

Starts big, cover every aspects of the neck, and present a big picture.

Use conference deadlines to make yourself efficient.

Start from bad writing then improve!