GraphMineSuite

Besta, Maciej; Vonarburg-Shmaria, Zur; Schaffner, Yannick; Schwarz, Leonardo; Kwasniewski, Grzegorz; Gianinazzi, Lukas; Beránek, Jakub; Janda, Kacper; Holenstein, Tobias; Leisinger, Sebastian; Tatkowski, Peter; Ozdemir, Esref; Balla, Adrian; Copik, Marcin; Lindenberger, Philipp; Konieczny, Marek; Mutlu, Onur; Hoefler, Torsten

doi:10.14778/3476249.3476252

Cited by 10 publications

(3 citation statements)

References 127 publications

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“…One avenue of future works lies in developing new HOGDMs beyond those in Table 1, which could better encapsulate HO interactions between entities in a way that is both computationally efficient and representative of the underlying complexities of the data. For example, one could consider novel substructures for Motif-Graphs, such as different forms of dense subgraphs [44], [49], [111], [113], [158], [173], [210].…”

Section: Research Opportunitiesmentioning

confidence: 99%

Motif Prediction with Graph Neural Networks

Besta¹,

Grob²,

Miglioli³

et al. 2022

Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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Link prediction is one of the central problems in graph mining. However, recent studies highlight the importance of higher-order network analysis, where complex structures called motifs are the first-class citizens. We first show that existing link prediction schemes fail to effectively predict motifs. To alleviate this, we establish a general motif prediction problem and we propose several heuristics that assess the chances for a specified motif to appear. To make the scores realistic, our heuristics consider -among others -correlations between links, i.e., the potential impact of some arriving links on the appearance of other links in a given motif. Finally, for highest accuracy, we develop a graph neural network (GNN) architecture for motif prediction. Our architecture offers vertex features and sampling schemes that capture the rich structural properties of motifs. While our heuristics are fast and do not need any training, GNNs ensure highest accuracy of predicting motifs, both for dense (e.g., 𝑘-cliques) and for sparse ones (e.g., 𝑘-stars). We consistently outperform the best available competitor by more than 10% on average and up to 32% in area under the curve. Importantly, the advantages of our approach over schemes based on uncorrelated link prediction increase with the increasing motif size and complexity. We also successfully apply our architecture for predicting more arbitrary clusters and communities, illustrating its potential for graph mining beyond motif analysis. CCS CONCEPTS• Information systems → Data mining.

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Section: Research Opportunitiesmentioning

confidence: 99%

Motif Prediction with Graph Neural Networks

Besta¹,

Grob²,

Miglioli³

et al. 2022

Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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“…SNB Interactive v1 has been influential in the graph data management space: as of June 2023, 21 audited results were published using this workload. 13…”

Section: Snb Interactive Workload V1mentioning

confidence: 99%

“…Performance challenges. Graph processing problems, including graph pattern matching [32], graph traversal (navigation) [4], and graph mining [13], have irregular memory access patterns and provide little spatial locality or opportunities for data reuse [17]. Contemporary CPUs are ill-suited to handle these workloads, leading to performance problems [37].…”

Section: Introductionmentioning

confidence: 99%

The LDBC Social Network Benchmark

Szárnyas

Waudby

Steer³

et al. 2022

Proc. VLDB Endow.

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The Social Network Benchmark's Business Intelligence workload (SNB BI) is a comprehensive graph OLAP benchmark targeting analytical data systems capable of supporting graph workloads. This paper marks the finalization of almost a decade of research in academia and industry via the Linked Data Benchmark Council (LDBC). SNB BI advances the state-of-the art in synthetic and scalable analytical database benchmarks in many aspects. Its base is a sophisticated data generator, implemented on a scalable distributed infrastructure, that produces a social graph with small-world phenomena, whose value properties follow skewed and correlated distributions and where values correlate with structure. This is a temporal graph where all nodes and edges follow lifespan-based rules with temporal skew enabling realistic and consistent temporal inserts and (recursive) deletes. The query workload exploiting this skew and correlation is based on LDBC's "choke point"-driven design methodology and will entice technical and scientific improvements in future (graph) database systems. SNB BI includes the first adoption of "parameter curation" in an analytical benchmark, a technique that ensures stable runtimes of query variants across different parameter values. Two performance metrics characterize peak single-query performance (power) and sustained concurrent query throughput. To demonstrate the portability of the benchmark, we present experimental results on a relational and a graph DBMS. Note that these do not constitute an official LDBC Benchmark Result - only audited results can use this trademarked term.

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