Time Constrained Continuous Subgraph Search Over Streaming Graphs

Li, Youhuan; Zou, Lei; Özsu, M. Tamer; Zhao, Dongyan

doi:10.1109/icde.2019.00100

Cited by 32 publications

(23 citation statements)

References 16 publications

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“…Proposition 3.1. The log-Hadamard representation (•) is a monomorphism 5 between the additive group on the set S N (Z 2 ) and the corresponding group of unitary operators under composition. That is, for all A, B ∈ S N (Z 2 ), we have:…”

Section: Operations On the Representationmentioning

confidence: 99%

“…The sub-graph isomorphism problem (under all its embodiments) has numerous applications when data can represented as networks, and notably in graph databases, biochemistry, computer vision, social network analysis, knowledge graph query, among many others [2][3][4]. Other important examples include finding patterns to detect cyber-attacks or credit card fraud [5].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Quantum Algorithm for the Sub-Graph Isomorphism Problem

Mariella¹,

Simonetto²

2021

Preprint

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We propose a novel variational method for solving the sub-graph isomorphism problem on a gatebased quantum computer. The method relies (1) on a new representation of the adjacency matrices of the underlying graphs, which requires a number of qubits that scales logarithmically with the number of vertices of the graphs; and (2) on a new Ansatz that can efficiently probe the permutation space. Simulations are then presented to showcase the approach on graphs up to 16 vertices, whereas, given the logarithmic scaling, the approach could be applied to realistic sub-graph isomorphism problem instances in the medium term.

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Section: Operations On the Representationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Quantum Algorithm for the Sub-Graph Isomorphism Problem

Mariella¹,

Simonetto²

2021

Preprint

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“…In addition, Li et al, [25] proposed a method to seek cohesive subgraphs in a signed network, in which each edge can be positive or negative, denoting friendship or conflict respectively. Li et al, [26] proposed a solution to efficiently answer subgraph search in streaming graph data. In the method, they designed concurrency management strategies to improve system throughput.…”

Section: Related Workmentioning

confidence: 99%

Updates-Aware Graph Pattern based Node Matching

Sun¹,

Liu²,

Wang³

et al. 2020

Preprint

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Graph Pattern based Node Matching (GPNM) is to find all the matches of the nodes in a data graph GD based on a given pattern graph GP . GPNM has become increasingly important in many applications, e.g., group finding and expert recommendation. In real scenarios, both GP and GD are updated frequently. However, the existing GPNM methods either need to perform a new GPNM procedure from scratch to deliver the node matching results based on the updated GP and GD or incrementally perform the GPNM procedure for each of the updates, leading to low efficiency. Therefore, there is a pressing need for a new method to efficiently deliver the node matching results on the updated graphs. In this paper, we first analyze and detect the elimination relationships between the updates. Then, we construct an Elimination Hierarchy Tree (EH-Tree) to index these elimination relationships. In order to speed up the GPNM process, we propose a graph partition method and then propose a new updates-aware GPNM method, called UA-GPNM, considering the single-graph elimination relationships among the updates in a single graph of GP or GD, and also the cross-graph elimination relationships between the updates in GP and the updates in GD. UA-GPNM first delivers the GPNM result of an initial query, and then delivers the GPNM result of a subsequent query, based on the initial GPNM result and the multiple updates that occur between two queries. The experimental results on five real-world social graphs demonstrate that our proposed UA-GPNM is much more efficient than the state-of-the-art GPNM methods.

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“…The input of S-BENU contains the pattern graph P , the initial data graph G 0 , and the batch update ∆o t at each time step t. S-BENU outputs ∆R + t and ∆R − t at each time step. Some existing continuous subgraph enumeration methods [25] [26] [27] [28] maintain the (partial) matching results of each time step in memory or on disk. They use the matching results of the time step t to compute the matching results of the time step t + 1, avoiding re-computing some intermediate results.…”

Section: S-benu Frameworkmentioning

confidence: 99%

BENU: Distributed Subgraph Enumeration with Backtracking-Based Framework

Wang

et al. 2019

2019 IEEE 35th International Conference on Data Engineering (ICDE)

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Given a small pattern graph and a large data graph, the task of subgraph enumeration is to find all subgraphs of the data graph that are isomorphic to the pattern graph. When the data graph is dynamic, the task of continuous subgraph enumeration is to detect the changes in the matching results caused by the edge updates at each time step. The two tasks are fundamental in many graph analysis applications. The state-of-the-art distributed methods solve them via distributed multi-way join. However, they are inefficient in communication since they have to shuffle partial matching results during the join. The partial matching results may be much larger than the data graph itself. To overcome the drawback, we develop the BENU framework for distributed subgraph enumeration. Given a data graph, BENU generates a group of local search tasks that follow a backtracking-based execution plan to enumerate subgraphs of the pattern graph. BENU executes the tasks in parallel. The tasks query the data graph stored in a distributed database on demand, avoid shuffling partial matching results. To handle dynamic data graphs, we propose the concept of incremental pattern graphs. We solve the continuous subgraph enumeration via enumerating incremental pattern graphs in the data graph snapshots at each time step. We extend BENU into S-BENU to enumerate them efficiently. We develop implementations for BENU and S-BENU with two optimization techniques. The extensive experiments show that BENU and S-BENU are scalable. They outperform the state-of-the-art by up to one and two orders of magnitude, respectively.Index Terms-backtracking-based framework, continuous subgraph matching, distributed graph querying, subgraph isomorphism, subgraph matching. !

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Time Constrained Continuous Subgraph Search Over Streaming Graphs

Cited by 32 publications

References 16 publications

A Quantum Algorithm for the Sub-Graph Isomorphism Problem

A Quantum Algorithm for the Sub-Graph Isomorphism Problem

Updates-Aware Graph Pattern based Node Matching

BENU: Distributed Subgraph Enumeration with Backtracking-Based Framework

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