최근 소셜 네트워크 서비스가 급속히 증가함에 따라 많은 기업들과 사용자들은 자신들의 정보가 더욱 빠르게 확산되길 바란다. 이러한 소셜 네트워크에서의 정보 확산을 연구하기 위하여 많은 알고리즘들이 제안되었는데 그 중 대표적인 알고리즘은 그리디 알고리즘(Greedy Algorithm)과 휴리스틱 알고리즘(Heuristic Algorithm)이다. 하지만 그리디 알고리즘의 경우 복잡성으로 인하여 실제 소셜 네트워크에 사용이 제한적이며, 기존 휴리스틱 기반의 메시지 전파 기법의 경우 균일한 소셜 네트워크 환경을 고려하였기 때문에 현재 소셜 네트워크의 특성을 반영한 연구가 요구된다. 이에 본 논문에서는 휴리스틱 알고리즘 기반의 정보 확산 확률과 노드 연결성을 고려한 정보 확산 능력 최대화 알고리즘을 제안하고, 실제 소셜 네트워크 데이터베이스를 이용하여 제안 알고리즘의 성능을 분석하였다. 그 결과 기존 알고리즘이 비해 더 많은 노드를 활성화 시킬 수 있어 정보 확산 능력을 향상시킬 수 있음을 확인하였다.

소셜 네트워크를 위한 확산 확률과 노드 연결성 기반의 정보 확산 최대화 알고리즘

소셜 네트워크를 위한 확산기반 영향력 극대화 기법

Recently, with the proliferation of social network services, users and many companies hope that their information spread more faster. In order to study the information diffusion in the social networks, many algorithms such as greedy algorithm and heuristic algorithm have been proposed. However, the greedy algorithm is too complicated to use in real-life social network, and the heuristic algorithms have been studied under the uniform distribution of diffusion probability, which is different from the real social network property. In this paper, we propose an heuristic information diffusion maximization algorithm based on diffusion probability and node degree. For performance evaluation, we use real social network database, and it is verified that our proposed algorithm activates more active nodes than existing algorithms, which enables faster and wider information diffusion.

/pdf/an-information-diffusion-maximization-algorithm-based-on-26n9b5dmc9.pdf

An Information Diffusion Maximization Algorithm Based on Diffusion Probability and Node Degree for Social Networks

Design business model creating Social Network and 'self-employed’ company

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Design business model creating Social Network and 'self-employed’ company