BLADYG: A Graph Processing Framework for Large Dynamic Graphs

Aridhi, Sabeur; Montresor, Alberto; Velegrakis, Yannis

doi:10.1016/j.bdr.2017.05.003

Cited by 25 publications

(14 citation statements)

References 19 publications

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“…The algorithm is already designed in such a way that both the Graph Construction and Label Propagation steps can be done in parallel and in a distributed way. Our next aim is to implement a distributed version of GrAPFI using a parallel/distributed framework such as Hadoop MapReduce [7], BLADYG [2] and Spark [32].…”

Section: Resultsmentioning

confidence: 99%

Exploiting Complex Protein Domain Networks for Protein Function Annotation

Sarker

Ritchie

Aridhi

2018

Studies in Computational Intelligence

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Huge numbers of protein sequences are now available in public databases. In order to exploit more fully this valuable biological data, these sequences need to be annotated with functional properties such as Enzyme Commission (EC) numbers and Gene Ontology terms. The UniProt Knowledgebase (UniProtKB) is currently the largest and most comprehensive resource for protein sequence and annotation data. In the March 2018 release of UniProtKB, some 556,000 sequences have been manually curated but over 111 million sequences still lack functional annotations. The ability to annotate automatically these unannotated sequences would represent a major advance for the field of bioinformatics. Here, we present a novel network-based approach called GrAPFI for the automatic functional annotation of protein sequences. The underlying assumption of GrAPFI is that proteins may be related to each other by the protein domains, families, and super-families that they share. Several protein domain databases exist such as In-terPro, Pfam, SMART, CDD, Gene3D, and Prosite, for example. Our approach uses Interpro domains, because the InterPro database contains information from several other major protein family and domain databases. Our results show that GrAPFI achieves better EC number annotation performance than several other previously described approaches.

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Section: Resultsmentioning

confidence: 99%

Exploiting Complex Protein Domain Networks for Protein Function Annotation

Sarker

Ritchie

Aridhi

2018

Studies in Computational Intelligence

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“…The balancing of the weights of the partitions can be done simply by a random placement of the vertices so as to have partitions of weight close to |Ed| k [8]. However, this will involve serious communication costs between partitions and not guarantee that the topology of the graph will be preserved [13]. The proposed approach takes these two compromises into account.…”

Section: Methodsmentioning

confidence: 99%

“…Large-scale network such as social networks (e.g., Facebook and Twitter) [7,8], road networks [9,10,11,12,8], brain networks [2], etc... with their heterogeneity allow to analyze a chaotic dynamics or represent a complex phenomenon. They represent numerous exciting challenges related to high performance computing problems, where data scalability, program complexity and robustness hardware configurations play an important role [13]. Solving these problems can contribute to efficiently manage the new trend technologies such as big data (e.g., dataViz), distributed systems (e.g., Hadoop [14] and Spark [15]) or future communication networks (e.g., 5G or IoT).…”

Section: Introductionmentioning

confidence: 99%

DHPV: A Distributed Algorithm for Large-Scale Graph Partitioning

Adoni

Nahhal

Krichen

et al. 2020

Preprint

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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 flexibility in handling data that is strongly connected to each other. The analysis of a big graph generally involves exploring all of its vertices. Thus, this operation is costly in time and resources because big graphs are generally composed of millions of vertices connected through billions of edges. Consequently, the graph algorithms are expansive compared to the size of the big graph, and are therefore ineffective for data exploration. Thus, partitioning the graph stands out as an efficient and less expensive alternative for exploring a big graph. This technique consists in partitioning the graph into a set of k sub-graphs in order to reduce the complexity of the queries. Nevertheless, it presents many challenges because it is an NP-complete problem. In this article, we present DPHV (Distributed Placement of Hub-Vertices) an efficient parallel and distributed heuristic for large-scale graph partitioning. An application on a real-world graphs demonstrates the feasibility and reliability of our method. The experiments carried on a 10-nodes Spark cluster proved that the proposed methodology achieves significant gain in term of time and outperforms JA-BE-JA, Greedy, DFEP.

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“…Moreover, many graph processing systems have been proposed [4]. Such frameworks include Pregel [39], Graphlab [38], Bladyg [3] and Trinity [47].…”

Section: Big Graph Processingmentioning

confidence: 99%

An experimental survey on big data frameworks

Inoubli

Aridhi

Mezni

et al. 2018

Future Generation Computer Systems

Self Cite

111

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Recently, increasingly large amounts of data are generated from a variety of sources. Existing data processing technologies are not suitable to cope with the huge amounts of generated data. Yet, many research works focus on Big Data, a buzzword referring to the processing of massive volumes of (unstructured) data. Recently proposed frameworks for Big Data applications help to store, analyze and process the data. In this paper, we discuss the challenges of Big Data and we survey existing Big Data frameworks. We also present an experimental evaluation and a comparative study of the most popular Big Data frameworks with several representative batch and iterative workloads. This survey is concluded with a presentation of best practices related to the use of studied frameworks in several application domains such as machine learning, graph processing and real-world applications.

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BLADYG: A Graph Processing Framework for Large Dynamic Graphs

Cited by 25 publications

References 19 publications

Exploiting Complex Protein Domain Networks for Protein Function Annotation

Exploiting Complex Protein Domain Networks for Protein Function Annotation

DHPV: A Distributed Algorithm for Large-Scale Graph Partitioning

An experimental survey on big data frameworks

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