12 papers · ranked by Valyu relevance
Chiwan Park, Ha-Myung Park, U. Kang, Roland Bouffanais
How can we analyze large graphs such as the Web, and social networks with hundreds of billions of vertices and edges? Although many graph mining systems have been proposed to perform various graph mining algorithms on such large graphs, they have difficulties in processing Web-scale graphs due to massive communication…
Yuzhong Chen, Zhenyu Liu, Yulin Liu, Chen Dong
Attack graph modeling aims to generate attack models by investigating attack behaviors recorded in intrusion alerts raised in network security devices. Attack models can help network security administrators discover an attack strategy that intruders use to compromise the network and implement a timely response to…
Lorenzo Di Rocco, Umberto Ferraro Petrillo, Simona E. Rombo
Background Huge amounts of molecular interaction data are continuously produced and stored in public databases. Although many bioinformatics tools have been proposed in the literature for their analysis, based on their modeling through different types of biological networks, several problems still remain unsolved when…
Amin Sahebi, Marco Barbone, Marco Procaccini, Wayne Luk + 2 more
'Georgi Gaydadjiev' 'Roberto Giorgi'] Processing large-scale graphs is challenging due to the nature of the computation that causes irregular memory access patterns. Managing such irregular accesses may cause significant performance degradation on both CPUs and GPUs. Thus, recent research trends propose graph…
Jie Cao, Haoxiang Wang, Jingru Jiao, Kekun Hu + 1 more
With the rapid expansion of social networks, efficiently mining and analyzing massive graph data has become a fundamental challenge in social network research. Graph partitioning plays a pivotal role in enhancing the performance of such analyses. However, conventional graph partitioning methods predominantly rely on…
Ha-Myung Park, Namyong Park, Sung-Hyon Myaeng, U Kang + 1 more
'Tatsuro Kawamoto'] A connected component in a graph is a set of nodes linked to each other by paths. The problem of finding connected components has been applied to diverse graph analysis tasks such as graph partitioning, graph compression, and pattern recognition. Several distributed algorithms have been proposed to…
Shunyun Yang, Runxin Guo, Rui Liu, Xiangke Liao + 3 more
'Benyun Shi' 'Shaoliang Peng'] Background Frequent subgraphs mining is a significant problem in many practical domains. The solution of this kind of problem can particularly used in some large-scale drug molecular or biological libraries to help us find drugs or core biological structures rapidly and predict toxicity…
Wilfried Yves Hamilton Adoni, Tarik Nahhal, Moez Krichen, Abdeltif El byed + 1 more
'Abdeltif El byed' 'Ismail Assayad'] Big graphs are part of the movement of “Not Only SQL” databases (also called NoSQL) focusing on the relationships between data, rather than the values themselves. The data is stored in vertices while the edges model the interactions or relationships between these data. They offer…
Miguel E. Coimbra, Alexandre P. Francisco, Luís Veiga
The value of graph-based big data can be unlocked by exploring the topology and metrics of the networks they represent, and the computational approaches to this exploration take on many forms. For the use-case of performing global computations over a graph, it is first ingested into a graph processing system from one…
Omar Batarfi, Radwa Elshawi, Ayman Fayoumi, Ahmed Barnawi + 1 more
'Sherif Sakr'] A graph is a popular data model that has become pervasively used for modeling structural relationships between objects. In practice, in many real-world graphs, the graph vertices and edges need to be associated with descriptive attributes. Such type of graphs are referred to as attributed graphs.…
Hossein Shafiei, Aresh Dadlani
Online social networks have attracted billions of active users over the past decade. These systems play an integral role in the everyday life of many people around the world. As such, these platforms are also attractive for misinformation, hoaxes, and fake news campaigns which usually utilize social trolls and/or…
Belgin Ergenç Bostanoğlu, Nourhan Abuzayed, Bilal Alatas
Frequent subgraph mining (FSM) is an essential and challenging graph mining task used in several applications of the modern data science. Some of the FSM algorithms have the objective of finding all frequent subgraphs whereas some of the algorithms focus on discovering frequent subgraphs approximately. On the other…