Efficient Subgraph Matching

Han, Myoungji; Kim, Hyunjoon; Gu, Geonmo; Park, Kunsoo; Han, Wook-Shin

doi:10.1145/3299869.3319880

Cited by 116 publications

(29 citation statements)

References 44 publications

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“…Replacing the k by n k in ( 17), (18), and ( 19), taking limits on both sides as k → ∞, we obtain that…”

Section: Additional Experiments Resultsmentioning

confidence: 99%

“…The problem of subgraph alignment is to determine if a query graph is isomorphic to a subgraph of a large target graph [14,18]. For graphs of similar size, instead of solving the restricted quadratic assignment problem [21,20], the GW distance provides the optimal probabilistic correspondence relationship by preserving the isometric property.…”

Section: Subgraph Alignmentmentioning

confidence: 99%

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Outlier-Robust Gromov Wasserstein for Graph Data

Kong¹,

Li²,

So³

2023

Preprint

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Gromov Wasserstein (GW) distance is a powerful tool for comparing and aligning probability distributions supported on different metric spaces. It has become the main modeling technique for aligning heterogeneous data for a wide range of graph learning tasks. However, the GW distance is known to be highly sensitive to outliers, which can result in large inaccuracies if the outliers are given the same weight as other samples in the objective function. To mitigate this issue, we introduce a new and robust version of the GW distance called RGW. RGW features optimistically perturbed marginal constraints within a ϕ-divergence based ambiguity set. To make the benefits of RGW more accessible in practice, we develop a computationally efficient algorithm, Bregman proximal alternating linearization minimization, with a theoretical convergence guarantee. Through extensive experimentation, we validate our theoretical results and demonstrate the effectiveness of RGW on real-world graph learning tasks, such as subgraph matching and partial shape correspondence.

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“…Replacing the k by n k in ( 17), (18), and ( 19), taking limits on both sides as k → ∞, we obtain that…”

Section: Additional Experiments Resultsmentioning

confidence: 99%

Section: Subgraph Alignmentmentioning

confidence: 99%

Outlier-Robust Gromov Wasserstein for Graph Data

Kong¹,

Li²,

So³

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Despite many of the simplifications and optimizations to the subgraph monomorphism task mentioned here, this task in general is NP-complete [60,39,61,62]. Even in a relatively small connectome such as that of C. elegans, certain sufficiently common or poorly-optimized motif searches may still remain infeasible to run on consumer hardware.…”

Section: Discussionmentioning

confidence: 99%

DotMotif: An open-source tool for connectome subgraph isomorphism search and graph queries

Matelsky

Reilly

Johnson

et al. 2020

Preprint

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As connectomics datasets continue to grow in size and complexity, methods to search for and identify interesting graph patterns offer a promising approach to quickly reduce data dimensionality and enable discovery. Recent advances in neuroscience have enabled brain structure exploration at the level of individual synaptic connections. These graphs are often too large to be analyzed manually, presenting significant barriers to searching for structure and testing hypotheses. We combine graph database and analysis libraries with an easy-to-use neuroscience grammar suitable for rapidly constructing queries and searching for subgraphs and patterns of interest. This abstracts many of the computer science and graph theory challenges associated with nanoscale brain network analysis and allows scientists to quickly achieve reproducible findings at scale. We demonstrate these tools to search for motifs on simulated data and real public connectomics datasets, and share simple and complex structures relevant to the neuroscience community. We contextualize these results and provide case studies to motivate future neuroscience questions. All of our tools are released open source to empower other scientists to use and extend these methods.

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“…Subgraph matching 1,2 is a fundamental problem in graph theory. There are many matching forms of subgraph matching.…”

Section: Introductionmentioning

confidence: 99%

A novel top‐k key node query problem in subgraph matching and its greedy strategy

Xue

2021

Engineering Reports

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Top‐k node selection in graph data is an essential problem in computer science and applications. In view of an important issue in the field of graph data, subgraph matching issue, we define the problem and propose its method for the top‐k key node query w.r.t. the subgraph matching. Unlike the general top‐k query problem, we aim to find out k nodes that make the matching subgraphs in data graph G that are covered by the k nodes as more as possible. This is a problem of the maximum coverage of subgraph matching, which belongs to the NP‐hard problem. We study the problem based on a greedy algorithm and give an intuitive solution. Considering the characteristics of the top‐k problem, we propose an improved and more efficient greedy algorithm. Experiments on real social network graph data set (Twitter) show that the related results represent the key nodes that can better reveal the essential characteristics of the query graph in the data graph G. The key node query problem in subgraph matching proposed in this article may have extensive applications in reality, such as the assessment of the influence of specific group members in social network, the detection of abnormal communication in a computer communication network, the road traffic evaluation and load balance problem in a road traffic network, and so on.

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Efficient Subgraph Matching

Cited by 116 publications

References 44 publications

Outlier-Robust Gromov Wasserstein for Graph Data

Outlier-Robust Gromov Wasserstein for Graph Data

DotMotif: An open-source tool for connectome subgraph isomorphism search and graph queries

A novel top‐k key node query problem in subgraph matching and its greedy strategy

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