@article{MTMT:32538562,
	title = {A cross-technology benchmark for incremental graph queries},
	url = {https://m2.mtmt.hu/api/publication/32538562},
	author = {Hinkel, Georg and Garcia-Dominguez, Antonio and Schöne, René and Boronat, Artur and Tisi, Massimo and Le Calvar, Théo and Jouault, Frederic and Marton, József and Nyíri, Tamás and Antal, János Benjamin and Elekes, Márton and Szárnyas, Gábor},
	doi = {10.1007/s10270-021-00927-5},
	journal-iso = {SOFTW SYST MODEL},
	journal = {SOFTWARE AND SYSTEMS MODELING},
	volume = {21},
	unique-id = {32538562},
	issn = {1619-1366},
	abstract = {To cope with the increased complexity of systems, models are used to capture what is considered the essence of a system. Such models are typically represented as a graph, which is queried to gain insight into the modelled system. Often, the results of these queries need to be adjusted according to updated requirements and are therefore a subject of maintenance activities. It is thus necessary to support writing model queries with adequate languages. However, in order to stay meaningful, the analysis results need to be refreshed as soon as the underlying models change. Therefore, a good execution speed is mandatory in order to cope with frequent model changes. In this paper, we propose a benchmark to assess model query technologies in the presence of model change sequences in the domain of social media. We present solutions to this benchmark in a variety of 11 different tools and compare them with respect to explicitness of incrementalization, asymptotic complexity and performance.},
	year = {2022},
	eissn = {1619-1374},
	pages = {755-804},
	orcid-numbers = {Marton, József/0000-0003-4752-4234; Elekes, Márton/0000-0002-3558-147X}
}

@inproceedings{MTMT:32398430,
	title = {Towards Testing ACID Compliance in the LDBC Social Network Benchmark},
	url = {https://m2.mtmt.hu/api/publication/32398430},
	author = {Waudby, Jack and Steer, Benjamin A. and Karimov, Karim and Marton, József and Boncz, Peter and Szárnyas, Gábor},
	booktitle = {Performance Evaluation and Benchmarking},
	doi = {10.1007/978-3-030-84924-5_1},
	unique-id = {32398430},
	abstract = {Verifying ACID compliance is an essential part of database benchmarking, because the integrity of performance results can be undermined as the performance benefits of operating with weaker safety guarantees (at the potential cost of correctness) are well known. Traditionally, benchmarks have specified a number of tests to validate ACID compliance. However, these tests have been formulated in the context of relational database systems and SQL, whereas our scope of benchmarking are systems for graph data, many of which are non-relational. This paper presents a set of data model-agnostic ACID compliance tests for the LDBC (Linked Data Benchmark Council) Social Network Benchmark suite's Interactive (SNB-I) workload, a transaction processing benchmark for graph databases. We test all ACID properties with a particular emphasis on isolation, covering 10 transaction anomalies in total. We present results from implementing the test suite on 5 database systems.},
	keywords = {Computer Science, Software Engineering},
	year = {2021},
	pages = {1-17},
	orcid-numbers = {Marton, József/0000-0003-4752-4234}
}

@article{MTMT:32241157,
	title = {The future is big graphs: a community view on graph processing systems},
	url = {https://m2.mtmt.hu/api/publication/32241157},
	author = {Sakr, Sherif and Bonifati, Angela and Voigt, Hannes and Iosup, Alexandru and Ammar, Khaled and Angles, Renzo and Aref, Walid and Arenas, Marcelo and Besta, Maciej and Boncz, Peter A. and Daudjee, Khuzaima and Valle, Emanuele Della and Dumbrava, Stefania and Hartig, Olaf and Haslhofer, Bernhard and Hegeman, Tim and Hidders, Jan and Hose, Katja and Iamnitchi, Adriana and Kalavri, Vasiliki and Kapp, Hugo and Martens, Wim and Özsu, M. Tamer and Peukert, Eric and Plantikow, Stefan and Ragab, Mohamed and Ripeanu, Matei R. and Salihoglu, Semih and Schulz, Christian and Selmer, Petra and Sequeda, Juan F. and Shinavier, Joshua and Szárnyas, Gábor and Tommasini, Riccardo and Tumeo, Antonino and Uta, Alexandru and Varbanescu, Ana Lucia and Wu, Hsiang-Yun and Yakovets, Nikolay and Yan, Da and Yoneki, Eiko},
	doi = {10.1145/3434642},
	journal-iso = {COMMUN ACM},
	journal = {COMMUNICATIONS OF THE ACM},
	volume = {64},
	unique-id = {32241157},
	issn = {0001-0782},
	year = {2021},
	eissn = {1557-7317},
	pages = {62-71}
}

@article{MTMT:32028274,
	title = {Automated Generation of Consistent, Diverse and Structurally Realistic Graph Models},
	url = {https://m2.mtmt.hu/api/publication/32028274},
	author = {Semeráth, Oszkár and Aren, A. Babikian and Boqi, Chen and Chuning, Li and Marussy, Kristóf and Szárnyas, Gábor and Varró, Dániel},
	doi = {10.1007/s10270-021-00884-z},
	journal-iso = {SOFTW SYST MODEL},
	journal = {SOFTWARE AND SYSTEMS MODELING},
	volume = {20},
	unique-id = {32028274},
	issn = {1619-1366},
	abstract = {In this paper, we present a novel technique to automatically synthesize consistent, diverse and structurally realistic domain-specific graph models. A graph model is (1) consistent if it is metamodel-compliant and it satisfies the well-formedness constraints of the domain; (2) it is diverse if local neighborhoods of nodes are highly different; and (1) it is structurally realistic if a synthetic graph is at a close distance to a representative real model according to various graph metrics used in network science, databases or software engineering. Our approach grows models by model extension operators using a hill-climbing strategy in a way that (A) ensures that there are no constraint violation in the models (for consistency reasons), while (B) more realistic candidates are selected to minimize a target metric value (wrt. the representative real model). We evaluate the effectiveness of the approach for generating realistic models using multiple metrics for guidance heuristics and compared to other model generators in the context of three case studies with a large set of real human models. We also highlight that our technique is able to generate a diverse set of models, which is a requirement in many testing scenarios.},
	year = {2021},
	eissn = {1619-1374},
	pages = {1713-1734},
	orcid-numbers = {Marussy, Kristóf/0000-0002-9135-8256; Varró, Dániel/0000-0002-8790-252X}
}

@inproceedings{MTMT:32868652,
	title = {Evaluation of Graph Analytics Frameworks Using the GAP Benchmark Suite},
	url = {https://m2.mtmt.hu/api/publication/32868652},
	author = {Azad, Ariful and Aznaveh, Mohsen Mahmoudi and Beamer, Scott and Blanco, Mark and Chen, Jinhao and D'Alessandro, Luke and Dathathri, Roshan and Davis, Tim and Deweese, Kevin and Firoz, Jesun and Gabb, Henry A and Gill, Gurbinder and Hegyi, Balint and Kolodziej, Scott and Low, Tze Meng and Lumsdaine, Andrew and Manlaibaatar, Tugsbayasgalan and Mattson, Timothy G and McMillan, Scott and Peri, Ramesh and Pingali, Keshav and Sridhar, Upasana and Szárnyas, Gábor and Zhang, Yunming and Zhang, Yongzhe},
	booktitle = {2020 IEEE International Symposium on Workload Characterization (IISWC)},
	doi = {10.1109/IISWC50251.2020.00029},
	unique-id = {32868652},
	year = {2020},
	pages = {216-227}
}

@inproceedings{MTMT:31861479,
	title = {A GraphBLAS solution to the SIGMOD 2014 Programming Contest using multi-source BFS},
	url = {https://m2.mtmt.hu/api/publication/31861479},
	author = {Elekes, Márton and Nagy, Attila and Sándor, Dávid and Antal, János Benjamin and Davis, Timothy A. and Szárnyas, Gábor},
	booktitle = {2020 IEEE High Performance Extreme Computing Conference (HPEC)},
	doi = {10.1109/HPEC43674.2020.9286186},
	unique-id = {31861479},
	year = {2020},
	pages = {1-7},
	orcid-numbers = {Elekes, Márton/0000-0002-3558-147X}
}

@inproceedings{MTMT:31344297,
	title = {Supporting Dynamic Graphs and Temporal Entity Deletions in the LDBC Social Network Benchmark's Data Generator},
	url = {https://m2.mtmt.hu/api/publication/31344297},
	author = {Waudby, Jack and Steer, Benjamin A. and Prat-Pérez, Arnau and Szárnyas, Gábor},
	booktitle = {Proceedings of the 3rd Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA)},
	doi = {10.1145/3398682.3399165},
	unique-id = {31344297},
	year = {2020},
	pages = {1-8}
}

@inproceedings{MTMT:31340716,
	title = {An incremental GraphBLAS solution for the 2018 TTC Social Media case study},
	url = {https://m2.mtmt.hu/api/publication/31340716},
	author = {Elekes, Márton and Szárnyas, Gábor},
	booktitle = {2020 IEEE 34th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2020},
	doi = {10.1109/IPDPSW50202.2020.00045},
	unique-id = {31340716},
	abstract = {Graphs are increasingly important for modelling and analysing connected data sets. Traditionally, graph analytical tools targeted global fixed-point computations, while graph databases focused on simpler transactional read operations such as retrieving the neighbours of a node. However, recent applications of graph processing (such as financial fraud detection and serving personalized recommendations) often necessitate a mix of the two workload profiles. A potential approach to tackle these complex workloads is to formulate graph algorithms in the language of linear algebra. To this end, the recent GraphBLAS standard defines a linear algebraic graph computational model and an API for implementing such algorithms. To investigate its usability and efficiency, we have implemented a GraphBLAS solution for the "Social Media" case study of the 2018 Transformation Tool Contest. This paper presents our solution along with an incrementalized variant to improve its runtime for repeated evaluations. Preliminary results show that the GraphBLAS-based solution is competitive but implementing it requires significant development efforts.},
	year = {2020},
	pages = {203-206},
	orcid-numbers = {Elekes, Márton/0000-0002-3558-147X}
}

@inproceedings{MTMT:31340549,
	title = {Incremental view maintenance in graph databases: A case study in Neo4j},
	url = {https://m2.mtmt.hu/api/publication/31340549},
	author = {Elekes, Márton and Szárnyas, Gábor},
	booktitle = {Proceedings of the 27th PhD Mini-Symposium},
	unique-id = {31340549},
	abstract = {With the increasing amount of densely connected data sets, graph analysis has become an integral part of data processing pipelines. Therefore, the last decade saw the emergence of numerous dedicated graph analytical systems along with specialized graph database management systems. Traditionally, graph analytical tools targeted global fixed-point computations, while graph databases focused on simpler transactional read operations such as retrieving the neighbours of a node. However, recent applications of graph processing (such as financial fraud detection and serving personalized recommendations) often necessitate a mix of the two workload profiles. Following this trend, the 2018 Transformation Tool Contest (an annual competition for graph transformation tools) presented a case study that requires participants to compute complex graph queries defined on a continuously changing social network graph. The solutions are assessed based on their scalability and query reevaluation time, therefore, solutions are encouraged to incrementalize their implementations. This paper demonstrates a solution in the popular Neo4j graph database using several incrementalization techniques and compares them against the reference implementation of the case study.},
	year = {2020},
	pages = {33-36},
	orcid-numbers = {Elekes, Márton/0000-0002-3558-147X}
}

@inproceedings{MTMT:31329295,
	title = {Parallel GraphBLAS with OpenMP},
	url = {https://m2.mtmt.hu/api/publication/31329295},
	author = {Aznaveh, Mohsen and Chen, Jinhao and Davis, Timothy A. and Hegyi, Bálint and Kolodziej, Scott P. and Mattson, Timothy G. and Szárnyas, Gábor},
	booktitle = {2020 Proceedings of the SIAM Workshop on Combinatorial Scientific Computing},
	doi = {10.1137/1.9781611976229.14},
	unique-id = {31329295},
	year = {2020},
	pages = {138-148}
}