지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2014.11
- 수록면
- 480 - 489 (10page)
이용수
초록· 키워드
오류제보하기
Social network analysis has been an interest topic among researchers since the middle of the last century because it helps to understand social behaviors based on the interactions among people. Moreover, at the beginning of 2000’s, online social networking services such as Facebook and Twitter have emerged rapidly and have become very huge data sources for social media analysis. A few studies related to processes that occur on a social network have been published, focused on topics like information diffusion and more precisely modeling diffusion process. On the other hand, process mining is an emergent discipline that combines process modeling and analysis with data mining techniques and offers insights over business process data stored in the so-called event logs, recorded everyday by information systems. Those event logs are the starting point of one process mining type known as process discovery, in which a process model is discovered based only on the data extracted from an event log file. In this paper a novel approach is presented, in which online social network data is used for process mining to discover hierarchical process models. The data first is preprocessed by means of community detection techniques in order to reduce its complexity, and an extended Heuristics Miner is then applied to the discovered communities to give insights about information diffusion process on the network. An experiment with real world Facebook data is conducted and its results are evaluated and discussed.
목차
1. Introduction
2. Background
3. Framework
4. Social Networking Service Data
5. User Clustering
6. Hierarchical Process Discovery
7. Implementation and Experiments
8. Conclusions and Future Work
References
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UCI(KEPA) : I410-ECN-0101-2015-500-002901899