석사학위논문. Master s Thesis 온라인소셜네트워크의성장. Growth in Online Social Networks: Sheer Volume vs Social Interaction. 전현우 ( 全玹佑 Chun, Hyunwoo) 전자전산학과전산학전공

Transcription

1 석사학위논문 Master s Thesis 온라인소셜네트워크의성장 Growth in Online Social Networks: Sheer Volume vs Social Interaction 전현우 ( 全玹佑 Chun, Hyunwoo) 전자전산학과전산학전공 Department of Electrical Engineering and Computer Science Division of Computer Science 한국과학기술원 Korea Advanced Institute of Science and Technology 2008

2 온라인소셜네트워크의성장 Growth in Online Social Networks: Sheer Volume vs Social Interaction 2

3 Growth in Online Social Networks: Sheer Volume vs Social Interaction Advisor : Professor Moon, Sue Bok by Chun, Hyunwoo Department of Electrical Engineering and Computer Science Division of Computer Science Korea Advanced Institute of Science and Technology A thesis submitted to the faculty of the Korea Advanced Institute of Science and Technology in partial fulfillment of the requirements for the degree of Master of Engineering in the Department of Electrical Engineering and Computer Science, Division of Computer Science. Daejeon, Korea Approved by Professor Moon, Sue Bok Advisor

4 온라인소셜네트워크의성장 전현우 위논문은한국과학기술원석사학위논문으로학위논문심사 위원회에서심사통과하였음 년 12 월 4 일 심사위원장문수복 ( 인 ) 심사위원윤현수 ( 인 ) 심사위원 Otfried Cheong ( 인 )

5 MCS 전현우. Chun, Hyunwoo. Growth in Online Social Networks: Sheer Volume vs Social Interaction. 온라인소셜네트워크의성장. Department of Electrical Engineering and Computer Science, Division of Computer Science p. Advisor Prof. Moon, Sue Bok. Text in English. Abstract i

6 Contents Abstract i Contents iii List of Tables iv List of Figures v 1 Introduction 1 2 User Interaction Captured in Cyworld Guestbook Growth of Guestbook Activity Self-Posting in Guestbook Activity Network 6 4 Factors Contributing to Activity Peer pressure Reciprocity of User Activity Steady Core Analysis Why Is Steady Core Important? Basic Statistics of Steady Core Topological Characteristics of Steady Core Compare Friendship Network with Activity Network The Impact of Power Users in Two Networks Related Work 17 8 Conclusion 18 Summary (in Korean) 19 References 20 iii

7 List of Tables 2.1 Description of Our Cyworld Data Description of Giant Connected Component in Steady Core iv

8 List of Figures 2.1 Comparison of growth in various user statistics in Cyworld Growth of the activity network in strength and degree The evolution of each user group. (a)average degree per month, (b)average strength per month (a) Node strength vs number of friends (b) Median node strength vs number of friends ALL: Node strength linearly correlated to the number of friends up to 200 or so friends Reciprocity of each user, (a) The number of receiving and writing messages, (b) The median of receiving and writing messages of each user Reciprocity of each link, (a) The number of received and writing messages (A B, A B, (b) The median of received and writing messages CDF of duration of edges last Monthly changing of top 1% hub nodes Degree distribution of steady core v

9 1. Introduction The theme of today s Internet services is social networking. Not only online social network services, Myspace and Facebook, but also other major web 2.0 services, Flickr, Del.icio.us, and YouTube, offer social networking features to their services. Users are getting used to interact each other through these features: making friend relationships, sharing their photos, and writing comments. Especially, online social networks assist users to make explicit frienship in online, and users easily make friendship with other users. These friend relations based on trust are expected to become a key to solve recommendation [], security [], search [], and personalization [] issues. Understanding friend relations is the first step to achieve them, and recent studies about social networks focus on the explicit friend relations [1, 6]. Friend relations on trust are valuable for some research area, but their characteristics are not enough to represent the status of online social networks at that time. The main reason comes from user behavior managing their friends. Once users make friend relations with others in online social network services, they tend not to break relations [1]. Consequently, the friend network is a history of all the past friend networks. The other reason is that a friend relation is the only start line of social interactions in online social network services. Making friend relations typically cannot be repeated after they are created. Looking friends photos, reading friends articles, and leaving comments to friends guestbooks are not observed in the friend relations, although these activities may come from friend relations. Macroscopically, the number of users, the number of daily visitors, and page views are three famous metrics to measure the status of online SNSs. These metrics describe overall status of online social network services, but hard to look into a part of social networks in detail. In this paper, we suggest to move the focus on analysis of online social networks from friend network to activity network for deeply understanding online social networks. The activity network is constructed from complete guestbook logs of Cyworld, the most biggest online social network service in Korea. Over two years guestbook logs give us insight to understand user activities: whether user activities transparently reflect the growth of online social networks? what affects user activities? can we find active user groups in online social networks? what charactistics do those groups have? and how much different the friend network and the activity network? 1

10 To our best knowledge, this is the first large-scale work to analyze user activities in online social network services. Although there are some literatures to anlayze user activities in different medium, call networks [] and msn messenger networks [], we present unique characteristics of activity networks in online social networks. Also, from the difference between the friend network and the activity network, we propose that we must carefully choose relevant one of both networks for different analysis or simulation goals. The remainder of this paper is structured as follows. In Chapter??, we describe the Cyworld, guestbook data of Cyworld. In Chapter 3, we define the activity network and construct it. In Chapter??, we describe factors contributing to activity network. In Chapter 5, we describe the steady core and analysis it. We compare friendship network with activity network in Chapter 6. 2

11 2. User Interaction Captured in Cyworld Guestbook In this section, we describe the social network data we use for this study. Cyworld, launched in 2001, is the largest online social network service in Korea. When a user joins Cyworld, one is given a homepage (called minihompy) that contains an avatar, a photo gallery, a public diary, a testimonial board, a guestbook, etc. A user can establish friend relationships with other users and share information only with those established relationships. As of October 2007, the number of registered Cyworld users has surpassed 20 million, that is more than a third of the entire South Korea population 1. As the huge number of registered users represents, people spend much time logged onto Cyworld and manage various aspects of social life online. Users browse through friends photos and leave comments. They read others public diaries and write testimonials for those established friends. Some of the features, such as writing a testimonial and viewing photos, are often limited to only those with established online friend relationships. The guestbook is accessible to any user and is the most used feature. Also it is a recorded two-way interaction, while viewing photos and reading public diaries are not recorded or reported to the owner of the photos and diaries. Ahn et al have analyzed Cyworld s topological characteristics of bi-directional friend relationships. Once established, a friend relationship is hardly severed and remains whether users stay in touch or not. It is an assertion that some relationship existed, currently active or not. In this work, we delve deeper into the web of social networking and study the user interaction captured in the guestbook. Unlike a friend relationship, that is bidirectional, a message on a guestbook represents a directional interaction between users. On a guestbook, people write greetings, recent news, and replies to messages. And they do not have to have an established friend relationship to use the guestbook. We have obtained the complete guestbook logs of Cyworld from June 2003 to October This period is very important in the development of Cyworld, as the number 1 Upon joining, a new user must have its personal identification number (equivalent of U.S. s social security number) verified. Foreigners have special provisions for membership. All user accounts on Cyworld map to real users, unless a user make an illicit use of other people s personal identification numbers 3

12 of users grew exponentially from 2 million to 16 million and the friend relationship network began to show a sign of densification [1]. In this work we investigate whether the growth in actual user interaction, the key aspect of social networking services, has kept up with the external growth. Our guestbook log consists of three columns: the writer, the guestbook owner, and the time. All user identifiers have been anonymized. As of October 2005, the number of Cyworld subscribers reached 16,023,307, 75.2% or 12,048,186 users have formed friend relationships with others, and 65.4% or 10,476,604 users have written or received at least once during the period of our guestbook logs. Compared to 381,602,530 friend relationships, the number of the writer and guestbook owner pairs is 537,970,431. Table 2.1 summarizes our dataset. Table 2.1: Description of Our Cyworld Data Period Number of Users Appeared in Data 17,788,870 Number of Messages (Tuples) 8,423,218,770 Number of Unique Tuples 537,970,431 Mean # of Written Msg. per User 637 (397) Mean # of Received Msg. per User 484 (297) 2.1 Growth of Guestbook Activity In the previous section, we have claimed that the guestbook is the most used feature and best represents the actual interaction among users. In this section, we quantitatively demonstrate the popularity of the guestbook feature. Figure 2.1 shows the following numbers: Registered users Registered users with friend relationships Users who have written at least one guestbook message during our dataset period Users who have written at least in that particular month We observe that the number of Cyworld users grew almost ten-fold in that short time span, and the cumulative number of guestbook users also experienced an exponential growth. However, the number of guestbook users never caught up with the total number of Cyworld users. Moreover, the monthly statistics of guestbook users started to abate in growth. 4

13 1.8e e e e+07 total users users w/ friends cum # of writers writers per month Population 1e+07 8e+06 6e+06 4e+06 2e Time Figure 2.1: Comparison of growth in various user statistics in Cyworld 2.2 Self-Posting in Guestbook When a friend write a message on a guestbook, the owner of the guestbook often replies in one s own guestbook, instead of visiting the friend s homepage and write there. This activity is captured in our guestbook log as a 3-tuple that has the same writer and owner. We call this tuple a self-post. Self-posts take up about a third or 38.9% in all posts, and they persist over time. Also 81.8% of users who have written at least once have written a self-post. For half of the users, 33.3% of their writings are self-posts. This is not negligible phenomenon. As follows, we determine how to interpret self-posts before analyzing user activities of guestbook logs. A self-post serves either of the two purposes: a message written for viewing by all others (a notice) or a reply specifically for a preceding message. We cannot distinguish a notice from a reply in the guestbook log, as they both appear as 3-tuples with the same writer and owner. The problem is that two types of self-post have no difference in guestbook logs, but influences of two types are greately different; A reply is intuitively motivating other users to interact continuously, but a notice message does not directly influence other users. However, in Cyworld a public diary serves a similar purpose as a notice, and we conjecture most self-posts are actually replies. As self-posting is an important aspect of user activity, we include it in our activity analysis in the next section. 5

14 3. Activity Network Graph representation of social networks is an apt abstraction of their connected nature and allows us to tap into the rich repository of graph and complex network theories. In this section we describe how we represent the user interaction on the guestbook as a graph and define metrics of interaction. We map a user to a node and a message to a directional weighted edge. In the rest of the paper, we refer to a user and a node interchangeably. An edge from node A to node B denotes that user A has written a message on user B s guestbook. The weight of the edge is the number of messages user A has written to user B. We include self-posting in our analysis as a reflexive edge pointing at itself. We call this directed and weighted graph the activity network. Our activity network is different from the friend network studied in [1] in the following two aspects: Edges are directional. Even represented as undirectional graph, the activity network is not a proper subset of the friend network, for users without established friend relationships can still write onto each other s guestbooks. We use the following two metrics to capture users activity quantitatively. Strength: the sum of all weights of edges originating from a node (the total number of messages a user has written) Degree: the number of edges originating from a node (the total number of unique guestbooks a user has written onto) In Figure 3.1 we plot four cumulative distribution functions (CDF): Cumulative node strength since June 2003 Monthly average of node strength Cumulative node degree since June 2003 Monthly average of node degree 6

15 Figure 3.1: Growth of the activity network in strength and degree As our guestbook data is from the period of explosive growth, a large number of users have joined and the time of membership initiation should be taken into consideration. Hence, monthly averages of node strength and degree are calculated by taking the cumulative node strength and degree by October 2006 and dividing them by the number of months since the first time a user has written a message in our dataset period. Not all users write on a guestbook as soon as they join Cyworld and there is a gap between a membership initiation and the first guestbook activity. In this sense the average node strength and degree, as we calculate, are upper bounds on actual monthly average activity of users. Half the Cyworld users with guestbook activity have written at least 170 messages in 25 different users guestbooks. Top 10% of those users have written more than 1800 messages on 100 and more guestbooks. To know whether user activity reflect the status of online social network services, at first we divide all users into three groups by the level of their activities that are top 10%, top 10 to 50%, and remain users. We compare the growth of three groups with that of Cyworld over time. Due to level of activities varies over time, we set theshold values of activity level to the monthly averages of node strength and degree from the Figure 3.1. Half the Cyworld users have written at least 16 messages in 6 different users guestbooks per month. Top 10% of those users have written more than 117 messages on 24 and more guestbooks per month. We compare the evolution of three user groups. In figure 3.2(a), we can observe that the number of top 10% users did not increase from Sep while the number of top 10 to 50% and that of under 50% users have increased steadily. We can also observe similar phenomenon in Fiugre 3.2(b). The only difference between two figures is that the number of top 10% users in Figure 3.2(b) decreases. 7

16 4e e+06 top 10% top 10-50% remains 4e e+06 top 10% top 10-50% remains 3e+06 3e+06 Number of users 2.5e+06 2e e+06 Number of users 2.5e+06 2e e+06 1e+06 1e Time Time Figure 3.2: The evolution of each user group. (a)average degree per month, (b)average strength per month 8

17 4. Factors Contributing to Activity In Section 3, we construct the activity network and observe that activity of users varies over month. In this section we investigate factors that contribute to activity in our online social network. Peer pressure and popularity are often the main causes behind our human social activities. We investigate how these two factors impact activity in the online social network. 4.1 Peer pressure We first ask the following question: "Are people socially more active, if they have many friends?" We would like to know if one s number of friends plays an encouraging role, as the more friends have joined the same online social networking service, the more peer pressure one might receive median node strength # of friends Figure 4.1: (a) Node strength vs number of friends (b) Median node strength vs number of friends ALL: Node strength linearly correlated to the number of friends up to 200 or so friends. We plot the node strength against the number of friends in Figure 4.1(a). At a first glance, the node strength does not seem to increase linearly to the number of users. When we take a closer look at the zoomed-in inset, we recognize a somewhat correlated increase in strength up to users with about 200 of friends. Then the node strength starts to di- 9

18 minish, even as the number of friends increases. In order to verify a trend in correlation, we plot the median node strength per number of friends in Figure 4.1(b). The graph shows a clear linear correlation between node strength and the number of user up to about 200, and then disperses. The Pearson correlation coefficient of the overall graph in Figure 4.1(a) is We split the users into two separate groups, those with 200 or fewer friends and with more than 200, and compute the correlation coefficients. For the first group, the Pearson correlation coefficient is , strongly positive; for the other group, only Intuitively, the more friends one has, the more active one should be socially. However, beyond 200 or so friends, one must reach a limit in one s socializing capacity. The breakoff point of 200 in our analysis is larger than predicted by Dunbar s Law [?]. From our data, we see that people in the 21st century can keep up with social activities involving 200 friends or so. Though the size of human neo-cortex has not grown since Dunbar s time, technology has assisted in our evolution into a more social creature. This outcome is in agreement with the previous work that reports a fall-out from a single scaling behavior in the node degree distribution and conjectures the emergence of online-only relationships [1]. Users with more than 200 friends are not particularly more active or at least as much compared to those with fewer friends. We suppose that the graph is another representation of Dunbar s law. Dunbar s law is that there are different limitations of the number of manageable relationships following species. In our previous work [1], we use Dunbar s law to describe the two scales in degree distribution graph of Cyworld friend networks; we find out scales change occurring at users having about 200 friends. Similarly, in Figure 4.1, changing correlation coefficient from positive to almost 0 occurs from users who having about 200 friends. Changing correlation is strongly related to the manageable limit number of relations rather than our previous work, because the load of writing messages is much harder than send or accept requests for friends. Figure 4.1 (b) clearly shows the changing trends. This graph exhibits the mean number of written messages of users who have the same number of friends; positive scale region under 200 friends and the scattering region over 200 friends. From these two graphs, we assume that 1) there is the limitation of the number of manageable friends, about 200, 2) until the limitation, more friends, more activities. 3) over the limitation, the number of friends are not related to activities. 10

19 4.2 Reciprocity of User Activity We look into the reciprocity of user activities. We show that peer pressure is exerted by not only the number of friends but also the number of exchanged messages. We compare the number of received and written messages of each user in Figure 4.2, and of each user pair in Figure 4.3. We can detect three regions: similar received and written messages, a few received but many written messages, and a few received but many written messages. The first and largest region is placed following y = x. The number of written messages by users who are included in this region is similar with that of received messages. We can assume that received messages are motivated users to write messages, vice and versa. Another two regions present opposite characteristics of two user groups. One group is that they write only a few replies, but they receive many messages. We conjecture a part of these users are originally very popular people such as celebrities. The other group is that they receive a few messages, but they write many messages. We conjecture a part of these users are spammers, or very passionate fanboys. 1e # of received messages e+06 # of written messages Figure 4.2: Reciprocity of each user, (a) The number of receiving and writing messages, (b) The median of receiving and writing messages of each user Through only the summation of written messages to all friends we cannot know the distribution of messages over friends. For example, among 100 friends, actively exchanging 100 messages with only one friends is greatly different from exchanging one message with every 100 friends. To compare peer pressure between these cases, we plot the number of exchanged messages between user pairs in Figure 4.3. The shape of user groups following y = x becomes more sharply; the number of written and received messages are almost the same in this group. Online exchanging messages are motivated each other reciprocally like 11

20 offline conversation # of B->A e+06 # of A->B Figure 4.3: Reciprocity of each link, (a) The number of received and writing messages (A B, A B, (b) The median of received and writing messages 12

21 5. Steady Core Analysis The steady core is defined as the network of users who 1) write amount of guestbook (active user) and 2) write guestbook steadily every month (steady user). In this section, we analyze steady core of networks based on user activities. 5.1 Why Is Steady Core Important? cdf duration Figure 5.1: CDF of duration of edges last # of hubs # of consecutive hubs number month Figure 5.2: Monthly changing of top 1% hub nodes 13

22 Writing a message is a repetitive activity. The number of writing and receiving messages shows how much two users are interested in or related each other. Of course, this is not an accurate but an approximate measure, because Cyworld provide another features to interact bewteen users. Both numbers are various by users in Figure 4.3, as follows, the activity network is changed extensively. To know how much the activity network changes over time, we have two points of view: one is from the edge s perspecive, and the other is from the active node s perspective. We show how long activities last in Figure 5.1 by constructing activity networks every month and compare edges among them. A half of edges do not last even two months; Each user s activity intensively changes. In addition, Over 60% of the most top 1% active users are changed monthly in Figure 5.2. In Section 4.2, we examine that active users can motivate other users to interact each other. If active users are strongly connected each other, they can be a powerful source of activities. Thus, clearly identifying the set of active users is directly related to understand online social networks. 5.2 Basic Statistics of Steady Core In order to determine active user and steady user, we use two metrics: user s overall strength and standard deviaition (SD) of strength per month. Both threshold values are set to median of each metric. In this paper, threshold value of overall strength is set to 171 according to Figure 3.1 and that of SD set to 16. We extract steady from users whose SD value is smaller than 16 and user writes messages at least 171 times. The number of these users is 499,397, and they are divided into 103,657 weakly connected components (WCC), the biggest one has 371,674 users and size of other components is smaller than 18. We choose the most giant connected component (GCC) to analyze the characteristics of steady core. Steady core consists of 371,674 users and 1,269,240 edges. These users are about 2% of entire users. They write all 67,216,193 messages and 36,288,688 messages of them to other users in steady core. These messages are about 0.43% of all messages. Considering the size of the steady core, their activities are quite high. 14

23 Table 5.1: Description of Giant Connected Component in Steady Core Number of Users 371,674 Number of Messages 36,288,688 Number of Unique Tuples 1,269,240 Mean # of Written Msg. per User Topological Characteristics of Steady Core Then, we look into topological characteristics of steady core. Topological analysis can show what basic statistics does not show. In our previous work [1], we present that Cyworld friendship network is mixed up two different types of friend relationships through topological analysis out-degree in-degree 10-2 CCDF degree of node Figure 5.3: Degree distribution of steady core Figure 5.3 is degree distribution of steady core. We can find two different scale that are abserved in the degree distribution of the friend network. Figure?? shows distribution of clustering coefficient of each node. The average clustering coefficient is Comparing XXX, the clustering coefficient of the entire activity newtork, steady core is well clustered. 15

24 6. Compare Friendship Network with Activity Network In this section, we focus on the difference between the friendship network and activity network. Both networks comes from the same Cyworld users but the different connection of users. Not only the connectivity of two users, but also dynamics is greatly different. Activity network changes much faster and intenser. This raise some interesting questions about the activity network. How long does edge last? How much do degree and strength of one node change monthly? Are hub nodes steady? How many nodes are neighbors of hubs? Is this number changed? Through answers of these questions, we deeply understand the activity network. 6.1 The Impact of Power Users in Two Networks Hub nodes in the friendship network and the activity network are representatives of the power users in viral marketing []. Whose hubs are more close to power users in information propagation is out of scope of this paper, but we investigate the characteristics of hubs in both networks as preliminary work for reasoning it. Our first focus is the proportion of overlap of hub nodes in both networks. We extract each top 1% hub nodes from both two networks, the friendship network and the activity network. The number of hub nodes who have more than 151 friends in the friendship network is 121,886 and that of hub nodes who write guestbooks on more than 207 users in the activity network is 134,490. Of two kinds of hub nodes, 63,800 users are overlaped. We also extract each top 1% hub nodes from monthly data in order to know whether hub nodes in the activity network are steady. Threshold value of top 1% degree increases until May 2004 and is kept. Hub nodes who are appeared in both consecutive two months is XX % of total nodes. 16

25 7. Related Work Social network analysis has been developed mainly from sociology and anthropology [12]. As electronically piled social network data enable observing the large-scale statistics of networks, the social network has became a hot topic also for the scientists in other fields, such as physics and computer science. World Wide Web has been giving birth to an army of social network services. SNSs have already occupied substantial portion of our relationships and they are treated as a major part of our social lives [13]. Before the emergence of large-scale online social network services, variety of other online social networks have been analyzed. Many analyses used the networks, the most basic communication medium [2, 5, 10, 11]. Valverde and Solé study the social network of open source communities [10,11]. The massive data of mobile communication was recently analyzed [8, 7]. Using mobile phone records of millions of people, they examine the communication pattern of people. They argued that the stability of communication network largely depends on the weak ties in the network. Holme et al. analyze an online dating community in detail [4]. In this work, the time evolution of activity showed the saturation of degree, power-law activity pattern, and.. Mislove et al. investigate various social network services [6]. Internet communities [9, 4, 3, 5]. 17

26 8. Conclusion 18

27 요약문 온라인소셜네트워크의성장 19

28 References [1] Y.-Y. Ahn, S. Han, H. Kwak, S. Moon, and H. Jeong. Analysis of topological characteristics of huge online social networking services. In WWW 07: Proceedings of the 16th international conference on World Wide Web, pages , New York, NY, USA, ACM Press. [2] H. Ebel, L.-I. Mielsch, and S. Bornholdt. Scale-free topology of networks. Phys. Rev. E, 66:035103, [3] K.-I. Goh, Y.-H. Eom, H. Jeong, B. Kahng, and D. Kim. Structure and evolution of online social relationships: Heterogeneity in unrestricted discussions. Phys. Rev. E, 73:066123, [4] P. Holme, C. R. Edling, and F. Liljeros. Structure and time-evolution of an internet dating community. Social Networks, 26:155, [5] G. Kossinets and D. Watts. Emprical Analysis of an Evolving Social Network. Science, 311(88), [6] A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee. Measurement and Analysis of Online Social Networks. In ACM Internet Measurement Conference, October [7] J.-P. Onnela, J. Saramäki, J. Hyvönen, G. Szabó, M. A. de Menezes, K. Kaski, A.-L. Barabási, and J. Kertész. Analysis of a large-scale weighted network of one-to-one human communication. New Journal of Physics, 9:179, [8] J.-P. Onnela, J. Saramäki, J. Hyvönen, G. Szabó, D. Lazer, K. Kaski, J. Kertész, and A.-L. Barabási. Structure and tie strengths in mobile communication networks. Proc. Nat. Acad. Sci., 104(18):7332, [9] F. T. Rothaermel and S. Sugiyama. Virtual internet communities and commercial success: individual and community-level theory grounded in the atypical case of timezone.com. Journal of Management, 27(3):297, [10] S. Valverde and R. V. Solé. Evolving social weighted networks: Nonlocal dynamics of open source communities. arxiv:physics/ v1. 20

29 [11] S. Valverde and R. V. Solé. Self-organization versus hierarchy in open-source social networks. Phys. Rev. E, 76:046118, [12] S. Wasserman and K. Faust. Social network analysis. Cambridge University Press, Cambridge, [13] B. Wellman. Computer networks as social networks. Science, 293(5537):2031,

30 감사의글 이논문을완성하기까지주위의모든분들로부터수많은도움을받았습니다.

31 이력서 이 름 : 전현우 생년월일 : 1984년 4월 15일 출 생 지 : 부산광역시동구초량동 1247번지의 2 본 적 지 : 부산광역시중구부평동 2가 56번지 주 소 : 부산광역시동구범일 2동한양아파트 주소 : hyunwoo@an.kaist.ac.kr 학 력 부산과학고등학교 (2 년수료 ) 한국과학기술원전산학과 (B.S.) 한국과학기술원전산학과 (M.S.)