ChronoGraph: Enabling temporal graph traversals for efficient information diffusion analysis over time

Byun, Jaewook; Woo, Sungpil; Kim, Dae-Young

doi:10.1109/icde48307.2020.00232

Cited by 8 publications

(20 citation statements)

References 5 publications

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“…The algorithm finds the shortest paths from a single source to the other vertices over edge events. Also, to support temporal graph traversals at both the framework and platform levels, ChronoGraph [11] provides the temporal syntax over existing static property graph(s) and its traversal language by extending de-facto standards [12], [13]. Based on the syntax, ChronoGraph guarantees efficient temporal graph traversals via temporal indexes and parallelism support.…”

Section: From V1mentioning

confidence: 99%

“…Based on the syntax, ChronoGraph guarantees efficient temporal graph traversals via temporal indexes and parallelism support. ChronoGraph outperforms existing graph databases [14], [15], [16] in terms of temporal graph traversals, and the details can be found in a previous publication [11].…”

Section: From V1mentioning

confidence: 99%

“…Finally, (v( 5), (14, 3)) would be included in γ. Both algorithms are provided as pseudocodes [1], [10] and traversal language statements [11]; thus we omit the details.…”

Section: Definition 2 (Temporal Single Source Shortest Path (Tsssp))mentioning

confidence: 99%

“…With the abstraction, users are only required to define how each vertex computes and scatters the temporal messages where unnecessary and redundant execution are avoided by the system. ChronoGraph [11] is our previous work, which is extended to propose a time-centric approach. This system provides the temporal syntax for Blueprints and Gremlin and the temporal support enabling efficient temporal graph traversals.…”

Section: Graph Analytics Systemmentioning

confidence: 99%

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Enabling Time-Centric Computation for Efficient Temporal Graph Traversals From Multiple Sources

Byun

2022

IEEE Trans. Knowl. Data Eng.

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Temporal graph traversal is an approach for analyzing how information spreads throughout a network over time. A system has been recently proposed as an initial effort for efficient analyses against higher time complexity and infinitely evolving data unlike static graph. However, with the system, the response time for traversals from multiple sources is proportional to the number of sources; thus, application domains of the system can be limited. To resolve this problem, the state-of-the-art vertex-centric paradigm can be considered; however, we have found that the paradigm is not fitted into this computation. The paper proposes a novel time-centric computation approach for efficient all-pairs temporal graph traversals. One benefit of this approach is that users only need to focus on designing a repetitive task for graph elements that are valid at each sliding time, which simplifies the program logic and alleviates the burden of writing codes. Another benefit is that the approach is expected to enhance the performance by facilitating the reuse of intermediate results of multiple sources. The proposed approach is evaluated with a prototyped system, the recipes for existing algorithms, and the experiments with open temporal datasets. In addition, we also discuss how to handle ever-evolving real-world temporal networks.

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Section: From V1mentioning

confidence: 99%

Section: From V1mentioning

confidence: 99%

“…Finally, (v( 5), (14, 3)) would be included in γ. Both algorithms are provided as pseudocodes [1], [10] and traversal language statements [11]; thus we omit the details.…”

Section: Definition 2 (Temporal Single Source Shortest Path (Tsssp))mentioning

confidence: 99%

Section: Graph Analytics Systemmentioning

confidence: 99%

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Enabling Time-Centric Computation for Efficient Temporal Graph Traversals From Multiple Sources

Byun

2022

IEEE Trans. Knowl. Data Eng.

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“…Prior research on time-varying graphs focused mostly on representation and modeling [16,27,36], and the analysis of temporal graphs [5,7,32,35]. On the algorithmic front, traditional graph algorithms, such as the single shortest path problem [11,13,16,37,38], breadth and depth first search [21], minimum spanning tree [22], page-rank [20,29], and community detection [19] are proposed for temporal graphs.…”

Section: Introductionmentioning

confidence: 99%

Exploring accelerator and parallel graph algorithmic choices for temporal graphs

Rehman¹,

Ahmad²,

Khan³

2020

Proceedings of the Eleventh International Workshop on Programming Models and Applications for Multicores and Manycores

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Many real-world systems utilize graphs that are time-varying in nature, where edges appear and disappear with respect to time. Moreover, the weights of different edges are also a function of time. Various conventional graph algorithms, such as single source shortest path (SSSP) have been developed for time-varying graphs. However, these algorithms are sequential in nature and their parallel counterparts are largely overlooked. On the other hand, parallel algorithms for static graphs are implemented as ordered and unordered variants. Unordered implementations do not enforce local or global order for processing tasks in parallel, but incur redundant task processing to converge their solutions. These implementations expose parallelism at the cost of high redundant work. Relaxordered implementations maintain local order through per-core priority queues to reduce the amount of redundant work, while exposing parallelism. Finally, strict-ordered implementations achieve the work efficiency of sequential version by enforcing a global order at the expense of high thread synchronizations. These parallel implementations are adopted for temporal graphs to explore the choices that provide optimal performance on different parallel accelerators. This work shows that selecting the optimal parallel implementation extracts geometric performance gain of 46.38% on Intel Xeon-40 core and 20.30% on NVidia GTX-1080 GPU. It is also shown that optimal implementation choices for temporal graphs are not always the same as their respective static graphs. CCS CONCEPTS• Computer systems organization → Multicore architectures; • Computing methodologies → Shared memory algorithms.

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Querying Temporal Property Graphs

Ravat

Song

Vallès-Parlangeau

2022

Advanced Information Systems Engineering

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Nowadays, in many real-world problems, objects are characterized by properties and interactions that evolve over time. Several temporal property graph models associated with query languages are proposed in the literature to manage the temporal and interconnectivity features of such problems. However, they are not widely used due to the lack of a conceptual view. To overcome this drawback, we propose useroriented operators to analyze temporal evolution of property graphs. We also define translation rules between our operators and existing property graph query languages to implement them directly. To illustrate the feasibility of our solution, we present two case studies based on a Neo4j and an OrientDB implementations of our operators and show some real-world querying examples.

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ChronoGraph: Enabling temporal graph traversals for efficient information diffusion analysis over time

Cited by 8 publications

References 5 publications

Enabling Time-Centric Computation for Efficient Temporal Graph Traversals From Multiple Sources

Enabling Time-Centric Computation for Efficient Temporal Graph Traversals From Multiple Sources

Exploring accelerator and parallel graph algorithmic choices for temporal graphs

Querying Temporal Property Graphs

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