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큰 그래프 상에서의 개인화된 페이지 랭크에 대한 빠른 계산 기법
박성찬,김연아,이상구 한국정보과학회 2022 정보과학회논문지 Vol.49 No.10
Computation of Personalized PageRank (PPR) in graphs is an important function that is widely utilized in myriad application domains such as search, recommendation, and knowledge discovery. As the computation of PPR is an expensive process, a good number of innovative and efficient algorithms for computing PPR have been developed. However, efficient computation of PPR within very large graphs with over millions of nodes is still an open problem. Moreover, previously proposed algorithms cannot handle updates efficiently, thereby severely limiting their capability of handling dynamic graphs. In this paper, we present a fast converging algorithm that guarantees high and controlled precision. We attempted to improve the convergence rate of the traditional Power Iteration approximation methods and fully exact methods. The results revealed that the proposed algorithm is at least 20 times faster than the Power Iteration and outperforms other state-of-the-art algorithms in terms of computation time. 그래프 내에서 개인화된 페이지랭크(Personalized PageRank, PPR)를 계산하는 것은 검색, 추천, 지식발견 등 여러 분야에서 광범위하게 활용되는 중요한 작업이다. 개인화된 페이지랭크를 계산하는 것은 고비용의 과정이 필요하므로, 개인화된 페이지랭크를 계산하는 효율적이고 혁신적인 방법들이 다수 개발되어왔다. 그러나 수백만 이상의 노드를 가진 대용량 그래프에 대한 PPR 계산은 여전히 시간이 크게 소요되는 작업이다. 그에 더하여, 기존에 제시된 알고리즘들은 그래프 갱신을 효율적으로 처리하지 못하여, 동적으로 변화하는 그래프 처리에 한계가 있다. 이에 대응하여, 본 연구에서는 높은 정밀도를 보장하면서도 빠르게 수렴하는 PPR 계산 알고리즘을 제시한다. 전통적인 거듭제곱법(Power Iteration)에, 축차가속 완화법(Successive Over Relaxation)과 초기 추측값 보정법(Initial Guess Revision)을 활용한 벡터 재사용 전략을 적용하여 수렴 속도를 개선하였다. 제시된 방법은 기존 거듭제곱법의 장점인 단순성과 엄밀성을 유지하면서도 수렴율과 계산속도를 크게 개선한다. 또한 개인화된 페이지랭크 벡터의 갱신을 위하여 이전에 계산되어 저장된 벡터를 재사용하여, 갱신에 드는 시간이 크게 단축된다. 본 방법은 주어진 오차 한계에 도달하는 즉시 결과값을 산출하므로 정확도와 계산시간을 유연하게 조절할 수 있으며, 이는 표본 기반 추정방법이나 역행렬 기반 방법이 가지지 못한 특성이다. 실험 결과, 본 방법은 거듭제곱법에 비하여 20배 이상 빠르게 수렴한다는 것이 확인되었으며, 기 제시된 최속 알고리즘과 비교하여도 결과 품질을 일정 수준 이상으로 유지하면서도 수행시간 면에서 우수한 성능을 보이는 것 또한 확인되었다.
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Finding Top-k Answers in Node Proximity Search Using Distribution State Transition Graph
박재휘,이상구 한국전자통신연구원 2016 ETRI Journal Vol.38 No.4
Considerable attention has been made for processing graph data in recent years. Efficient method on how to compute node proximity is one of the most challenging problems for many applications such as recommendation systems and social networks. Regarding large-scale, mutable datasets and user queries, top-k query processing gains much interest. This paper presents a novel method to find top-k answers in node proximity search based on the well-known measure, Personalized PageRank (PPR). First, we introduce Distribution State Transition Graph (DSTG) to depict iterative steps of solving the PPR equation. Second we propose a weight distribution model of DSTG to capture the states of intermediate PPR scores and their distribution. Using DSTG, we can selectively follow and compare the multiple random paths with different lengths to find the most promising nodes. Moreover, we prove the results of our method are equivalent to the PPR results. The comparative performance studies using two real data sets clearly show that our method is practical and accurate.
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Analysis of risk propagation using the world trade network
Kim Sungyong,Yun Jinhyuk 한국물리학회 2022 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.81 No.7
An economic system is an exemplar of a complex system in which all agents interact simultaneously. Interactions between countries have generally been studied using the fow of resources across diverse trade networks, in which the degree of dependence between two countries is typically measured based on the trade volume. However, indirect infuences may not be immediately apparent. Herein, we compared a direct trade network to a trade network constructed using the personalized PageRank (PPR) encompassing indirect infuences. By analyzing the correlation of the gross domestic product (GDP) between countries, we discovered that the PPR trade network has greater explanatory power on the propagation of economic events than direct trade by analyzing the GDP correlation between countries. To further validate our observations, an agentbased model of the spreading economic crisis was implemented for the Russia–Ukraine war of 2022. The model also demonstrates that the PPR explains the actual impact more efectively than the direct trade network. Our research highlights the signifcance of indirect and long-range relationships, which have often been overlooked.
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Supporting inter-topic entity search for biomedical Linked Data based on heterogeneous relationships
Zong, N.,Lee, S.,Ahn, J.,Kim, H.G. Pergamon Press ; Elsevier Science Ltd 2017 Computers in biology and medicine Vol.87 No.-
Objective: The keyword-based entity search restricts search space based on the preference of search. When given keywords and preferences are not related to the same biomedical topic, existing biomedical Linked Data search engines fail to deliver satisfactory results. This research aims to tackle this issue by supporting an inter-topic search-improving search with inputs, keywords and preferences, under different topics. Methods: This study developed an effective algorithm in which the relations between biomedical entities were used in tandem with a keyword-based entity search, Siren. The algorithm, PERank, which is an adaptation of Personalized PageRank (PPR), uses a pair of input: (1) search preferences, and (2) entities from a keyword-based entity search with a keyword query, to formalize the search results on-the-fly based on the index of the precomputed Individual Personalized PageRank Vectors (IPPVs). Results: Our experiments were performed over ten linked life datasets for two query sets, one with keyword-preference topic correspondence (intra-topic search), and the other without (inter-topic search). The experiments showed that the proposed method achieved better search results, for example a 14% increase in precision for the inter-topic search than the baseline keyword-based search engine. Conclusion: The proposed method improved the keyword-based biomedical entity search by supporting the inter-topic search without affecting the intra-topic search based on the relations between different entities.
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