• The Journal of the Korea Contents Association
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• v.18 no.7
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• pp.449-458
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• 2018

As the usage of social network services increases, event information occurring offline is spreading more rapidly. Therefore, studies have been conducted to detect events by analyzing social data. In this paper, we propose a graph based event detection scheme considering user interest in social networks. The proposed scheme constructs a keyword graph by analyzing tweets posted by users. We calculates the interest measure from users' social activities and uses it to identify events by considering changes in interest. Therefore, it is possible to eliminate events that are repeatedly posted without meaning and improve the reliability of the results. We conduct various performance evaluations to demonstrate the superiority of the proposed event detection scheme.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.1270-1271
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• 2011

소셜 네트워크 서비스는 가트너에서 2011년에 이어 2012년에도 각광받을 기술의 하나로 선정된 만큼 미래 인터넷의 핵심 키워드 중 하나로도 뽑히며, 엔터테인먼트, 검색, 방송, 커머스 등의 여러 가지 서비스와 직접 연결된다. 이러한 소셜 네트워크 서비스 가운데 하이브리드형 서비스는 사용자의 정보를 관리 및 파악하여 사용자가 원하는 제품을 예측하고 추천해주고 있으며, 이를 위해 그래프 마이닝 기술을 적용하고 있다. 하지만 그래프 마이닝 기술은 아직 복잡한 그래프 구조의 데이터에서 정보를 추출하기에 제약사항들이 발생하므로 이에 대하여 많은 연구가 활발히 이루어지고 있다. 이러한 그래프 마이닝 기술을 나아가 더 발전시켜 활용하면 기존의 하이브리드형 서비스에서 사용자의 정보를 파악하여 충성도를 높여줄 뿐 아니라 기업에서의 타켓 마케팅과 원투원 마케팅을 가능하게 해주고 기존 사용자에 대한 교차 판매와 격상판매의 전략들을 도출할 수 있을 것이다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.567-575
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• 2020

As online social networks are used as a critical medium for modern people's information sharing and relationship, their users are increasing rapidly every year. This not only increases usage but also surpasses the existing media in terms of information credibility. Therefore, emerging marketing strategies are deliberately attacking social networks. As a result, public opinion, which should be formed naturally, is artificially formed by online attacks, and many people trust it. Therefore, many studies have been conducted to detect agents attacking online social networks. In this paper, we analyze the trends of researches attempting to detect such online social network attackers, focusing on researches using social network graph characteristics. While the existing content-based techniques may represent classification errors due to privacy infringement and changes in attack strategies, the graph-based method proposes a more robust detection method using attacker patterns.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.12
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• pp.533-540
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• 2013

Anonymity is the one of main reasons for substantial improvement of Internet. It encourages various users to express their opinion freely and helps Internet based distributed systems vitalize. But, anonymity can cause unexpected threats because personal information of an online user is hidden. Especially, distributed systems are threatened by Sybil attack, where one malicious user creates and manages multiple fake online identities. To prevent Sybil attack, the traditional solutions include increasing the complexity of identity generation and mapping online identities to real-world identities. But, even though the high complexity of identity generation increases the generation cost of Sybil identities, eventually they are generated and there is no further way to suppress their activity. Also, the mapping between online identities and real identities may cause high possibility of losing anonymity. Recently, some methods using online social network to prevent Sybil attack are researched. In this paper, a new method is proposed for extracting a user's system-wide Sybil-resistant trust value by using the properties embedded in online social network graphs. The proposed method can be categorized into 3 types based on sampling and decision strategies. By using graphs sampled from Facebook, the performance of the 3 types of the proposed method is evaluated. Moreover, the impact of Sybil attack on nodes with different characteristics is evaluated in order to understand the behavior of Sybil attack.

• Journal of the Korea Society of Computer and Information
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• v.18 no.7
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• pp.101-109
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• 2013

In this paper, a method to decide the number of additional edges to integrate two communities in social network by using modularity is studied. The modularity is a measure to be used to describe the quality of the community. By adding additional edges to the communities, the quality of the communities is enhanced, To integrate two communities, we propose methods to decide the number of additional edges by calculating the modularity. Also, the conditions that the proposed method is valid is investigated in a simple test graph and the efficiency of the proposed method is approved by integrating two communities in Zachary Karate Club network.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06c
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• pp.22-24
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• 2012

소셜 애플리케이션은 GPS 센서가 내장된 스마트폰의 보급과 더불어 위치 기반 서비스와 융합된 형태로 발전하고 있다. 기존의 위치기반 서비스는 물류관제, 교통관제, 주문배달 등의 실시간 서비스 제공에 초점을 맞추었기 때문에, 소셜 애플리케이션에서 제공하는 취미, 선호도 기반의 추천 서비스 등의 개인화 서비스 제공에 적합하지 않다. 본 연구에서는 개인화된 소셜 애플리케이션에 적용 가능한 시공간 데이터의 그래프 모델링 기법을 제안한다. 실험을 통해 제안하는 시공간 데이터 모델링 기법의 유용성을 보인다.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.640-645
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• 2014

Many people use social network services as to communicate, to share an information and to build social relationships between others on the Internet. Twitter is such a representative service, where millions of tweets are posted a day and a huge amount of data collection has been being accumulated. Social mining that extracts the meaningful information from the massive data has been intensively studied. Typically, Twitter easily can deliver and retweet the contents using the following-follower relationships. Topic modeling in tweet data is a good tool for issue tracking in social media. To overcome the restrictions of short contents in tweets, we introduce a notion of reply graph which is constructed as a graph structure of which nodes correspond to users and of which edges correspond to existence of reply and retweet messages between the users. The LDA topic model, which is a typical method of topic modeling, is ineffective for short textual data. This paper introduces a topic modeling method that uses reply graph to reduce the number of short documents and to improve the quality of mining results. The proposed model uses the LDA model as the topic modeling framework for tweet issue tracking. Some experimental results of the proposed method are presented for a collection of Twitter data of 7 days.

• The Journal of the Korea Contents Association
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• v.18 no.2
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• pp.438-449
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• 2018

Recently, various graph data have been utilized in various fields such as social networks and citation networks. As the graph changes dynamically over time, it is necessary to manage the graph historical data for tracking changes and retrieving point-in-time graphs. Most historical data changes partially according to time, so unchanged data is stored redundantly when data is stored in units of time. In this paper, we propose a graph history storage management method to minimize the redundant storage of time graphs. The proposed method continuously detects the change of the graph and stores the overlapping subgraph in intersection snapshot. Intersection snapshots are connected by a number of delta snapshots to maintain change data over time. It improves space efficiency by collectively managing overlapping data stored in intersection snapshots. We also linked intersection snapshots and delta snapshots to retrieval the graph at that point in time. Various performance evaluations are performed to show the superiority of the proposed scheme.

• Journal of the HCI Society of Korea
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• v.10 no.1
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• pp.5-17
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• 2015

Nowadays various public data have been serviced to the public. Through the opening of public data, the transparency and effectiveness of public policy developed by governments are increased and users can lead to the growth of industry related to public data. Since end-users of using public data are citizens, it is very important for everyone to figure out the meaning of public data using proper visualization techniques. In this work, to indicate the significance of widespread public data, we consider UN voting record as public data in which many people may be interested. In general, it has high utilization value by diplomatic and educational purposes, and is available in public. If we use proper data mining and visualization algorithms, we can get an insight regarding the voting patterns of UN members. To visualize, it is necessary to measure the voting similarity values among UN members and then a social graph is created by the similarity values. Next, using a graph layout algorithm, the social graph is rendered on the screen. If we use the existing method for visualizing the social graph, it is hard to understand the meaning of the social graph because the graph is usually dense. To improve the weak point of the existing social graph visualization, we propose Friend-Matching, Friend-Rival Matching, and Bubble Heap algorithms in this paper. We also validate that our proposed algorithms can improve the quality of visualizing social graphs displayed by the existing method. Finally, our prototype system has been released in http://datalab.kunsan.ac.kr/politiz/un/. Please, see if it is useful in the aspect of public data utilization.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.432-436
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• 2010

Finding communities from network data including social networks can be done by clustering the nodes of the network as densely interconnected groups, where keeping interconnection between groups sparse. To exploit a clustering algorithm for community detection task, we need a well-defined similarity measure between network nodes. In this paper, we propose a new similarity measure named "Common Neighborhood Sub-graph density" and combine the similarity with affinity propagation, which is a recently devised clustering algorithm.