Distributed temporal graph analytics with GRADOOP

Rost, Christopher; Gómez, Kevin; Täschner, Matthias; Fritzsche, Philip; Schons, Lucas; Christ, Lukas; Adameit, Timo; Junghanns, Martin; Rahm, Erhard

doi:10.1007/s00778-021-00667-4

Cited by 23 publications

(8 citation statements)

References 55 publications

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“…During the past several decades, researchers considered different aspects of graph pattern matching, see Conte et al [15] for a survey. The majority of temporal graph models use either time points [24,30,44] or intervals [38,61,67,47] to enrich graphs with temporal information. In our work, we associate each edge in a graph with a set of time points, which is an appropriate representation when events -such as messages between users or citations -are instantaneous and so do not have a duration.…”

Section: Related Workmentioning

confidence: 99%

“…However, understanding how interests of social network users evolve over time, support for contact tracing, and many other research questions and applications require access to information about how a graph changes over time. Consequently, the focus of research has shifted to pattern matching in temporal graphs for tasks such as finding temporal motifs, temporal journeys, and temporal shortest paths [24,30,44,47,50,61,66,67].…”

Section: Introductionmentioning

confidence: 99%

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Temporal graph patterns by timed automata

Aghasadeghi¹,

Bussche²,

Stoyanovich³

2022

Preprint

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Temporal graphs represent graph evolution over time, and have been receiving considerable research attention. Work on expressing temporal graph patterns or discovering temporal motifs typically assumes relatively simple temporal constraints, such as journeys or, more generally, existential constraints, possibly with finite delays. In this paper we propose to use timed automata to express temporal constraints, leading to a general and powerful notion of temporal basic graph pattern (BGP).The new difficulty is the evaluation of the temporal constraint on a large set of matchings. An important benefit of timed automata is that they support an iterative state assignment, which can be useful for early detection of matches and pruning of non-matches. We introduce algorithms to retrieve all instances of a temporal BGP match in a graph, and present results of an extensive experimental evaluation, demonstrating interesting performance trade-offs. We show that an on-demand algorithm that processes total matchings incrementally over time is preferable when dealing with cyclic patterns on sparse graphs. On acyclic patterns or dense graphs, and when connectivity of partial matchings can be guaranteed, the best performance is achieved by maintaining partial matchings over time and allowing automaton evaluation to be fully incremental. The code and datasets are available at http: //github.com/amirpouya/TABGP.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Temporal graph patterns by timed automata

Aghasadeghi¹,

Bussche²,

Stoyanovich³

2022

Preprint

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“…Other works propose an algebra of composable operators for querying temporal property graphs models, in the same idea as the relational algebra [19,22]. They extend existing graph operations [3,6,24], such as the basic graph pattern matching 6 , to incorporate temporal operators functionally similar to Allen operators [1].…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, it does not allow direct querying of temporal graph data at the graph component level [16]. Both [19] and [22]'s operators manipulate data-oriented concepts from their underlying models, such as nodes and edges, instead of user-oriented concepts, such as entities and relationships. Moreover, there is no technical environment that implements them directly.…”

Section: Related Workmentioning

confidence: 99%

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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“…A graph data structure combined with temporal properties would allow modeling and predicting directed edges over time and thus the evaluation of time-dependent changes in the DOM composition (e.g., via photolysis). Such a temporal graph can be particularly useful in identifying new links between the entities (i.e., molecules) in the considered data. − Using graph analytics tools such as link prediction, graph-based clustering, and visual analysis, it is possible to identify hidden patterns in the data, such as the relation between change in intensity of a meaningful subset of MF and transformations likely responsible for this change. Better assessment of the actual occurring chemical transformations in complex DOM would help to better evaluate natural processes as well as anthropogenic impacts constantly altering the molecular composition and chemical properties of DOM.…”

Section: Introductionmentioning

confidence: 99%

A Temporal Graph Model to Predict Chemical Transformations in Complex Dissolved Organic Matter

Plamper

Lechtenfeld

Herzsprung

et al. 2023

Environ. Sci. Technol.

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Dissolved organic matter (DOM) is a complex mixture of thousands of natural molecules that undergo constant transformation in the environment, such as sunlight induced photochemical reactions. Despite molecular level resolution from ultrahigh resolution mass spectrometry (UHRMS), trends of mass peak intensities are currently the only way to follow photochemically induced molecular changes in DOM. Many real-world relationships and temporal processes can be intuitively modeled using graph data structures (networks). Graphs enhance the potential and value of AI applications by adding context and interconnections allowing the uncovering of hidden or unknown relationships in data sets. We use a temporal graph model and link prediction to identify transformations of DOM molecules in a photo-oxidation experiment. Our link prediction algorithm simultaneously considers educt removal and product formation for molecules linked by predefined transformation units (oxidation, decarboxylation, etc.). The transformations are further weighted by the extent of intensity change and clustered on the graph structure to identify groups of similar reactivity. The temporal graph is capable of identifying relevant molecules subject to similar reactions and enabling to study their time course. Our approach overcomes previous data evaluation limitations for mechanistic studies of DOM and leverages the potential of temporal graphs to study DOM reactivity by UHRMS.

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Distributed temporal graph analytics with GRADOOP

Cited by 23 publications

References 55 publications

Temporal graph patterns by timed automata

Temporal graph patterns by timed automata

Querying Temporal Property Graphs

A Temporal Graph Model to Predict Chemical Transformations in Complex Dissolved Organic Matter

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