Practice of Streaming Processing of Dynamic Graphs: Concepts, Models, and Systems

Besta, Maciej; Fischer, M. L.; Kalavri, Vasiliki; Kapralov, Michael; Hoefler, Torsten

doi:10.48550/arxiv.1912.12740

Cited by 6 publications

(8 citation statements)

References 152 publications

(202 reference statements)

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“…As observed in [7,10,23], it is rather challenging to develop eicient incremental algorithms starting from scratch, due to the lack of efective data structures, storage and computation model that support computations on dynamic graphs. To this end, an alternative approach is to deduce an incremental algorithm T ∆ from a popular batch algorithm T , by reusing the data structures and computation logic of T to a large extent.…”

Section: Relative Boundednessmentioning

confidence: 99%

See 1 more Smart Citation

Incremental Graph Computations: Doable and Undoable

Fan

Tian

2022

ACM Trans. Database Syst.

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The incremental problem for a class \({\mathcal {Q}} \) of graph queries aims to compute, given a query \(Q \in {\mathcal {Q}} \) , graph G , answers Q ( G ) to Q in G and updates ΔG to G as input, changes ΔO to output Q ( G ) such that Q ( G ⊕ ΔG ) = Q ( G )⊕ ΔO . It is called bounded if its cost can be expressed as a polynomial function in the sizes of Q , ΔG and ΔO , which reduces the computations on possibly big G to small ΔG and ΔO . No matter how desirable, however, our first results are negative: for common graph queries such as traversal, connectivity, keyword search, pattern matching, and maximum cardinality matching, their incremental problems are unbounded. In light of the negative results, we propose two characterizations for the effectiveness of incremental graph computation: (a) localizable , if its cost is decided by small neighbors of nodes in ΔG instead of the entire G ; and (b) bounded relative to a batch graph algorithm \({\mathcal {T}} \) , if the cost is determined by the sizes of ΔG and changes to the affected area that is necessarily checked by any algorithms that incrementalize \({\mathcal {T}} \) . We show that the incremental computations above are either localizable or relatively bounded, by providing corresponding incremental algorithms. That is, we can either reduce the incremental computations on big graphs to small data, or incrementalize existing batch graph algorithms by minimizing unnecessary recomputation. Using real-life and synthetic data, we experimentally verify the effectiveness of our incremental algorithms.

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Section: Relative Boundednessmentioning

confidence: 99%

“…. Then starting from each node in S, it updates the layered graph G l by performing new BFS traversals (lines[10][11]. The traversals are conducted following the ascending order of the nodes in S, and level and parent values are adjusted by adopting the same semantics as that of HopcrotK.…”

mentioning

confidence: 99%

Incremental Graph Computations: Doable and Undoable

Fan

Tian

2022

ACM Trans. Database Syst.

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“…In this section, we describe our reasoning and efforts towards a working prototype 6 for evaluating Seraph queries and present a series of experiments to assess the performances of such a prototype.…”

Section: System Prototypementioning

confidence: 99%

“…In particular, we position Seraph in the state of the art and we discuss its relation with other work in the area of graphs and stream processing. Dynamic Graphs and Streaming Graphs are two additional attempts to extend existing graph data models with a temporal dimension [6]. Dynamic Graphs are graphs whose content, i.e., vertices and edges, is unpredictably updated.…”

Section: Related Workmentioning

confidence: 99%

Semantic Foundations of Seraph Continuous Graph Query Language

Falzone¹,

Tommasini²,

Valle³

et al. 2021

Preprint

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The scientific community has been studying graph data models for decades. Their high expressiveness and elasticity led the scientific community to design a variety of graph data models and graph query languages, and the practitioners to use them to model real-world cases and extract useful information. Recently, property graphs and, in particular, Cypher 9 (the first open version of the well-known Neo4j Inc.'s language) are gaining popularity. Practitioners find Cypher useful and applicable in many scenarios. However, we are living in a streaming world where data continuously flows. A growing number of Cypher's users show interest in continuously querying graph data to act in a timely fashion. Indeed, Cypher lacks the features for dealing with streams of (graph) data and continuous query evaluation. In this work, we propose Seraph, an extension of Cypher, as a first attempt to introduce streaming features in the context of property graph query languages. Specifically, we define Seraph semantics, we propose a first version of Seraph syntax, and we discuss the potential impacts from a user perspective.

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“…The arboricity measures the number of such forests required for a given graph. Many real-world graphs are sparse [5,6,[9][10][11]14] and have a low degeneracy and arboricity [4,7,25,49,50].…”

Section: Introductionmentioning

confidence: 99%

Parallel Algorithms for Finding Large Cliques in Sparse Graphs

Gianinazzi,

Besta,

Schaffner

et al. 2021

Preprint

Self Cite

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We present a parallel 𝑘-clique listing algorithm with improved work bounds (for the same depth) in sparse graphs with low degeneracy or arboricity. We achieve this by introducing and analyzing a new pruning criterion for a backtracking search. Our algorithm has better asymptotic performance, especially for larger cliques (when 𝑘 is not constant), where we avoid the straightforwardly exponential runtime growth with respect to the clique size. In particular, for cliques that are a constant factor smaller than the graph's degeneracy, the work improvement is an exponential factor in the clique size compared to previous results. Moreover, we present a low-depth approximation to the community degeneracy (which can be arbitrarily smaller than the degeneracy). This approximation enables a low depth clique listing algorithm whose runtime is parameterized by the community degeneracy. CCS CONCEPTS• Theory of computation → Parallel algorithms.

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Practice of Streaming Processing of Dynamic Graphs: Concepts, Models, and Systems

Cited by 6 publications

References 152 publications

Incremental Graph Computations: Doable and Undoable

Incremental Graph Computations: Doable and Undoable

Semantic Foundations of Seraph Continuous Graph Query Language

Parallel Algorithms for Finding Large Cliques in Sparse Graphs

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