November 2011 Archives

Friendship and Mobility

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A couple of weeks ago, I have read this paper: "Friendship and Mobility: User Movement In Location-Based Social Networks". The paper is published in the proceedings of the 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. The paper is mainly concerned with answering the following questions: (a) Do users' friendship affects their mobility ? (b) Do users' mobility make them create new friendship? (c) Do users visit specific places at particular times? Do they visit some places more frequently than others?

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The Contribution of the paper is two-fold: (1) First, the authors have applied extensive data analysis on three data sets; two data sets from location-based social networking websites (i.e., Gowalla and BrightKite). The third data set is from a cell phone service provider in a European country the authors decided not to disclose its name (2) Secondly, the authors derived a user mobility model taking into account three sub-models: (a) Model of spatial locations that a user regularly visits, (b) A model of temporal movement between these locations, and (c) A model of movement that is influenced by the social network ties.

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The authors figured out that distant friends have higher influence on a user than his nearby friends. This is explained by the fact that a user most probably travels (move long distance) to see friends. On the other hand, a user not necessarily visits a friend when s/he moves small distances (e.g., commute to work). Another finding was that users with highly similar trajectories are most probably to be friends.

Playing with Foursquare Data

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Recently, I have been interested in analyzing social networking data. Mainly, I have been playing with Foursquare data. To those who don't know what Foursquare is, it is a mobile location-based social network application. Foursquare users are associated with a home city, and alert friends when visiting a venue (e.g., restaurant) by "checking-in" on their mobile phones. During a "check-in", users can also leave "tips", which are free text notes describing what that they liked about the venue. Any other user can add the "tip" to her "to-do list" if interested in visiting the venue. Once a user visits a venue in the "to-do list" , she marks it as "done". Also, users who check into a venue the most are considered the "mayor" of that venue.

I found out that we can come up with cool findings if we statistically analyze Foursquare data. The data was fetched through APIs provided by Foursquare, which are pretty similar to Facebook APIs (but not exactly the same). Collecting the data is not a piece of cake though; as you may know, all social networking applications start to lock their data as it gets more user base. Anyways, one can always find his way to such data, it is not a big deal after all.

Given such data, I thought of posing this question: "Do Foursquare users visit the same places as their friends?". For instance, if Bob is a Foursquare user, the question is whether he checks-in at the same spatial locations (restaurants, theaters...) as his Foursquare friends. To this end, I have applied a simple statistical analysis on 2186 Foursquare users in the twin cities area. For each user, all her friends were retrieved and all the places she visited. The average number of friends for each user is 44 and the average number of visits per user is 14. The users were then classified into three categories (1) popular, (2) moderate, and (3) unpopular, based upon the number of friends they have. For instance, if the user has 200 friends or more, he is a popular user. If the user has 70 to 200 friends, he is a moderate user, and if he has less than 70 friends, he is unpopular. Different class is assigned for each user (1) active, (2) moderate, (3) inactive, based upon his number of visits. For example, if the user has more than 90 visits he is an active user, 40 to 90 visits he is a moderate user, and less than 40 visits he is an inactive user. The histograms for the user activity as well as user popularity are given in the following figures.


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In order to answer the aforementioned question, we define a parameter called "CoVisits Ratio" for each user is as follows: Let U be the set of user's visits and n be the total number of friends of the user. Fi is the set of visits of friend i, and V is the set of unique visits among the user and all his friends.

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The average CoVisits ratio for each user category (active, moderate, inactive) and also for the other three user categories (popular, moderate, unpopular) was calculated. In the following two figures, the red bars represents the CoVisits ratio for the user with his friends and the blue bar represents the CoVisits ration for the user with random users (who are not his friends). These random users were selected to be exactly the same as the user's friends (with same activity level) As depicted in both figures, in the case of moderate activity and moderate popularity, users seems to visits place that their friends visit more than those places visited by strangers (non-friends).

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The correlation between being friends and the number of co-visits between two users was measured, and the result is shown in figure 3. The X-axis represents the total number of co-visits between any pair of users and the y-axis represents the total number of users pairs. The blue bar represents the total number of user pairs which are friends and the red bar represents the total number of users pairs that are not friends. Notice that the values for (X1 to X3) are trimmed from the graph as they are very large compared to other values. In addition, a correlation test was applied between two variables for each pair of users: (1) Being friends (1) or not friends (0), and (2) Number of Covisits between this pair of users. A Pearson and Spearman correlation methods were applied (using R statistical analysis tool) on both variables and the correlation coefficient was 0.1113512 and 0.08696049, respectively and the 95% confidence interval is [ 0.1100980 , 0.1126042 ]. That means that there is a positive correlation between being friends and visiting the same places, but the correlation is not high.


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The analysis showed that there is a small positive correlation between being friends and visiting the same spatial locations. I believe the results could be extended to answer a more general question which is "Do friends in online social networking websites are also friends in real life (i.e., hang out together in real life) ?". Even though the question was raised before in many articles (Kari Henley 2009, T. J. Borchard 2011, etc...), I still believe figuring out an answer to this question using spatial co-visits could be a good future research direction.

Conference Peer Review Process!

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I was thinking about how the database research community is accepting new ideas. I usually (or should I say always) have hard times trying to come up with this sort of persuasive argument in my papers. Actually, I try to put myself in the reviewer's shoes, but it seems that these kinds of shoes are hard to get. Let me give you a simple example, which I believe, will make my point clear. Once upon a time (Fairy Tale style :D), I submitted a paper to a conference and it was rejected (very typical) and that is not the problem. My normal behavior is to get read the reviewers' comments, fix the paper accordingly, and submit it again to the next conference on my calendar. For the second submission, I am expecting at least to get better reviews than those received upon the first submission. However, I figured out that the reviewers of the second conference almost hated the modifications I made to the paper as a response to the first conference reviewers' comments. That actually drives me crazy, it is like you are cooking a meal and you are consulting two friends to taste the food. Your first friend figured out that it needs more salt, and your second friend sees that it is too salty and the meal needs to be redone. What I am trying to say here is that the review process seems to be too subjective. I appreciate the community is getting bigger and bigger and the reviewers do not have the time to give more insightful reviews. So, does this mean we might sacrifice the reviews quality ? Definitely, not. The review process is crucial for our field to evolve and keep attracting more people to get in. So, the solution should remain in figuring how to enhance the review process in order to make it more objective. I attended ICDE 2010 keynote speech given by Jeffrey Naughton, who focused on the review process and how to make it better. Actually, he came up with a couple of creative ideas that I liked a lot. For instance, he suggested to offer an award for the best reviewers which will encourage the reviewers to do their best. He also suggested to publish the reviews online so that community can see the reviews, and this way the reviewer may feel embarrassed to submit a bad review. The one that I like the most is to reveal the identity of the reviewer to the paper's authors, which will also put some stress on the reviewer to write high quality review. The aforementioned ideas are good, and I believe the community can come up with better ideas if all people agree that there is an issue that needs to be solved so that we would maintain the prestige of our community that it earned in the past..

Best Research Paper Award

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I recently received the Best Paper Award in 12th international symposium on spatial and temporal databases. I believe the closest word to describe my feeling is "Ecstasy", though not enough. I felt it would be good if I could share my experience with you and give you some tips:

1) Don't get discouraged with paper rejections, it will get in eventually. That paper was rejected twice before being accepted and received the Best Research Paper Award.

2) Trust your advisor; he is way more experienced than you are. That does not mean that you should be a pushover and follow your advisor blindly. Your advisor definitely wants you also to have an argument and to support your argument in a good way.

3) Never eat alone: collaborate with smart people to get your work done. Don't you ever say "I am the smartest person on earth, I can do everything on my own". Believe it or not, when you say so, you are screwed.

4) Whenever you are looking for a research problem to solve, find a REAL problem and try to solve it the SIMPLEST way. Complicated solutions usually don't fly.

5) Think Big: When you are solving a problem, think how the world would be different after solving such a problem. Don't just think: "I want my paper to be published so that I can graduate".

That is all I have in my mind now. wish it is useful...

Two Years in Minnesota

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This Month marks my two years anniversary in Minnesota, I would like to share with you what I have achieved during them:

1) received my master's degree in computer science at University of Minnesota.
2) Passed my PhD qualifying exams and became a PhD candidate.
3) learned what the word "Winter" really means.
4) got the Best Paper Award in SSTD 2011.
5) interned at Microsoft Research Redmond.
6) interned at NEC Labs america in the bay area.
7) got to know the best friends I have ever had.

I believe that's all. I am so exited to write the events coming up the next few years.

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This page is an archive of entries from November 2011 listed from newest to oldest.

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